diff --git a/docs/extrap-with-metadata-aggregated.ipynb b/docs/extrap-with-metadata-aggregated.ipynb
index cf48e872..16bc6851 100644
--- a/docs/extrap-with-metadata-aggregated.ipynb
+++ b/docs/extrap-with-metadata-aggregated.ipynb
@@ -2,13 +2,13 @@
  "cells": [
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+   "id": "2c5ce0c3",
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     "tags": []
@@ -34,19 +34,19 @@
   {
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@@ -411,13 +411,13 @@
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@@ -431,19 +431,19 @@
   {
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+   "id": "b2420c28",
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@@ -456,13 +456,13 @@
   },
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@@ -474,19 +474,19 @@
   {
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+   "id": "df26c26a",
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@@ -497,11 +497,11 @@
      "data": {
       "text/plain": [
        "profile\n",
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+       "-8090931487673920031     64\n",
+       "-281908797508534030     125\n",
+       " 938109236477167326     216\n",
+       " 3071605971850147300     27\n",
+       " 5507601860982680356    343\n",
        "Name: jobsize, dtype: int64"
       ]
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@@ -516,13 +516,13 @@
   },
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+   "id": "aa5debf6",
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@@ -537,19 +537,19 @@
   {
    "cell_type": "code",
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-   "id": "27908eef",
+   "id": "526ad854",
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@@ -593,7 +593,7 @@
       " |          column (list): list of column names in the aggregated statistics table to\n",
       " |              componentize. Values must be of type 'thicket.model_extrap.ModelWrapper'.\n",
       " |  \n",
-      " |  produce_models(self, agg_func=<function mean at 0xffff2a4f10d0>, add_stats=True)\n",
+      " |  produce_models(self, agg_func=<function mean at 0xffff5e14b0d0>, add_stats=True)\n",
       " |      Produces an Extra-P model. Models are generated by calling Extra-P's\n",
       " |          ModelGenerator.\n",
       " |      \n",
@@ -624,13 +624,13 @@
   },
   {
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@@ -648,19 +648,19 @@
   {
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-   "id": "69cf979b",
+   "id": "1b15d8e6",
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@@ -680,13 +680,13 @@
   },
   {
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+   "id": "15731a3f",
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@@ -700,19 +700,19 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "812074bd",
+   "id": "822a0edc",
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      "exception": false,
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@@ -763,7 +763,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Allreduce', 'type': 'function'}</th>\n",
        "      <td>MPI_Allreduce</td>\n",
-       "      <td>-0.002483573830186518 + 4.672313710732994e-09 ...</td>\n",
+       "      <td>-0.002483573830186528 + 4.6723137107329955e-09...</td>\n",
        "      <td>2.373695e-02</td>\n",
        "      <td>33.638540</td>\n",
        "      <td>71.854711</td>\n",
@@ -773,7 +773,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Bcast', 'type': 'function'}</th>\n",
        "      <td>MPI_Bcast</td>\n",
-       "      <td>0.0055946227666686076 + 1.1211777143604542e-05...</td>\n",
+       "      <td>0.00559462276666876 + 1.1211777143604536e-05 *...</td>\n",
        "      <td>5.234884e-03</td>\n",
        "      <td>0.592849</td>\n",
        "      <td>18.775441</td>\n",
@@ -783,7 +783,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Comm_dup', 'type': 'function'}</th>\n",
        "      <td>MPI_Comm_dup</td>\n",
-       "      <td>0.20714199930961935 + 0.0003794872132338879 * ...</td>\n",
+       "      <td>0.20714199930961907 + 0.00037948721323388787 *...</td>\n",
        "      <td>1.468475e+01</td>\n",
        "      <td>1.458439</td>\n",
        "      <td>50.350316</td>\n",
@@ -793,7 +793,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Comm_free', 'type': 'function'}</th>\n",
        "      <td>MPI_Comm_free</td>\n",
-       "      <td>2.974896446151339e-05 + 2.9632810629043057e-06...</td>\n",
+       "      <td>2.9748964461513302e-05 + 2.9632810629043057e-0...</td>\n",
        "      <td>4.012398e-08</td>\n",
        "      <td>0.026386</td>\n",
        "      <td>5.697692</td>\n",
@@ -803,7 +803,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Comm_split', 'type': 'function'}</th>\n",
        "      <td>MPI_Comm_split</td>\n",
-       "      <td>0.03409920994697278 + 4.861767349462249e-07 * ...</td>\n",
+       "      <td>0.03409920994697361 + 4.861767349462246e-07 * ...</td>\n",
        "      <td>4.094649e+00</td>\n",
        "      <td>1.584798</td>\n",
        "      <td>40.927787</td>\n",
@@ -813,7 +813,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Gather', 'type': 'function'}</th>\n",
        "      <td>MPI_Gather</td>\n",
-       "      <td>1.111071710084141e-05 + 1.8759317720297382e-09...</td>\n",
+       "      <td>1.1110717100841163e-05 + 1.875931772029739e-09...</td>\n",
        "      <td>1.805567e-07</td>\n",
        "      <td>2.319450</td>\n",
        "      <td>37.410684</td>\n",
@@ -823,7 +823,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Initialized', 'type': 'function'}</th>\n",
        "      <td>MPI_Initialized</td>\n",
-       "      <td>-1.2157511991338818e-06 + 4.4736500077363466e-...</td>\n",
+       "      <td>-1.215751199133331e-06 + 4.473650007736346e-06...</td>\n",
        "      <td>2.079887e-09</td>\n",
        "      <td>0.016595</td>\n",
        "      <td>4.528570</td>\n",
@@ -833,7 +833,7 @@
        "    <tr>\n",
        "      <th>{'name': 'main', 'type': 'function'}</th>\n",
        "      <td>main</td>\n",
-       "      <td>5.3345854025489245 + 50.44722398708033 * p^(1)</td>\n",
+       "      <td>5.334585402553237 + 50.44722398708032 * p^(1)</td>\n",
        "      <td>1.428021e+07</td>\n",
        "      <td>0.132731</td>\n",
        "      <td>15.651588</td>\n",
@@ -843,7 +843,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Barrier', 'type': 'function'}</th>\n",
        "      <td>MPI_Barrier</td>\n",
-       "      <td>-3.8613044063814517 + 0.722886713004767 * log2...</td>\n",
+       "      <td>-3.8613044063814534 + 0.7228867130047674 * log...</td>\n",
        "      <td>7.133681e+00</td>\n",
        "      <td>80964.639853</td>\n",
        "      <td>127.158285</td>\n",
@@ -853,7 +853,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Irecv', 'type': 'function'}</th>\n",
        "      <td>MPI_Irecv</td>\n",
-       "      <td>0.0001693042315778418 + 5.34448636184851e-05 *...</td>\n",
+       "      <td>0.0001693042315778342 + 5.3444863618485094e-05...</td>\n",
        "      <td>5.902043e-04</td>\n",
        "      <td>0.048232</td>\n",
        "      <td>8.436975</td>\n",
@@ -863,7 +863,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Isend', 'type': 'function'}</th>\n",
        "      <td>MPI_Isend</td>\n",
-       "      <td>-0.7378766027055425 + 0.011372374310428847 * p...</td>\n",
+       "      <td>-0.7378766027055431 + 0.011372374310428852 * p...</td>\n",
        "      <td>5.386096e-01</td>\n",
        "      <td>5023.400772</td>\n",
        "      <td>80.306304</td>\n",
@@ -873,7 +873,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Reduce', 'type': 'function'}</th>\n",
        "      <td>MPI_Reduce</td>\n",
-       "      <td>0.00895051336452584 + 2.1551606902927186e-05 *...</td>\n",
+       "      <td>0.008950513364525844 + 2.1551606902927193e-05 ...</td>\n",
        "      <td>1.756609e-03</td>\n",
        "      <td>32.371981</td>\n",
        "      <td>49.251316</td>\n",
@@ -883,7 +883,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Wait', 'type': 'function'}</th>\n",
        "      <td>MPI_Wait</td>\n",
-       "      <td>-0.08273770855385464 + 0.0008985035981343795 *...</td>\n",
+       "      <td>-0.08273770855385452 + 0.0008985035981343793 *...</td>\n",
        "      <td>2.613648e-01</td>\n",
        "      <td>15.106425</td>\n",
        "      <td>50.478159</td>\n",
@@ -898,12 +898,12 @@
        "      <td>282.767881</td>\n",
        "      <td>109.810235</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>16.324158</td>\n",
+       "      <td>0.508850</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'lulesh.cycle', 'type': 'function'}</th>\n",
        "      <td>lulesh.cycle</td>\n",
-       "      <td>6.361023656688513 + 50.40069659189234 * p^(1)</td>\n",
+       "      <td>6.361023656692823 + 50.40069659189231 * p^(1)</td>\n",
        "      <td>1.431665e+07</td>\n",
        "      <td>0.133362</td>\n",
        "      <td>15.688203</td>\n",
@@ -913,7 +913,7 @@
        "    <tr>\n",
        "      <th>{'name': 'LagrangeLeapFrog', 'type': 'function'}</th>\n",
        "      <td>LagrangeLeapFrog</td>\n",
-       "      <td>-588.7139217783116 + 118.0201880558252 * p^(4/5)</td>\n",
+       "      <td>-588.7139217783108 + 118.02018805582516 * p^(4/5)</td>\n",
        "      <td>3.026414e+06</td>\n",
        "      <td>0.040647</td>\n",
        "      <td>7.619168</td>\n",
@@ -923,7 +923,7 @@
        "    <tr>\n",
        "      <th>{'name': 'CalcTimeConstraintsForElems', 'type': 'function'}</th>\n",
        "      <td>CalcTimeConstraintsForElems</td>\n",
-       "      <td>0.29121283787713886 + 0.1546446810459539 * p^(1)</td>\n",
+       "      <td>0.2912128378771511 + 0.1546446810459539 * p^(1)</td>\n",
        "      <td>6.423611e-01</td>\n",
        "      <td>0.010278</td>\n",
        "      <td>2.671244</td>\n",
@@ -933,7 +933,7 @@
        "    <tr>\n",
        "      <th>{'name': 'LagrangeElements', 'type': 'function'}</th>\n",
        "      <td>LagrangeElements</td>\n",
-       "      <td>55.80235706834194 + 12.219839260204246 * p^(1)</td>\n",
+       "      <td>55.80235706834264 + 12.219839260204242 * p^(1)</td>\n",
        "      <td>2.180612e+05</td>\n",
        "      <td>0.041861</td>\n",
        "      <td>8.942728</td>\n",
@@ -943,7 +943,7 @@
        "    <tr>\n",
        "      <th>{'name': 'ApplyMaterialPropertiesForElems', 'type': 'function'}</th>\n",
        "      <td>ApplyMaterialPropertiesForElems</td>\n",
-       "      <td>6.909192329599132 + 3.812718071421943 * p^(1)</td>\n",
+       "      <td>6.909192329599409 + 3.812718071421941 * p^(1)</td>\n",
        "      <td>1.344775e+03</td>\n",
        "      <td>0.001975</td>\n",
        "      <td>1.640254</td>\n",
@@ -953,7 +953,7 @@
        "    <tr>\n",
        "      <th>{'name': 'EvalEOSForElems', 'type': 'function'}</th>\n",
        "      <td>EvalEOSForElems</td>\n",
-       "      <td>7.437812732121217 + 3.719775901083089 * p^(1)</td>\n",
+       "      <td>7.437812732121435 + 3.719775901083088 * p^(1)</td>\n",
        "      <td>1.057620e+03</td>\n",
        "      <td>0.001701</td>\n",
        "      <td>1.524714</td>\n",
@@ -963,7 +963,7 @@
        "    <tr>\n",
        "      <th>{'name': 'CalcEnergyForElems', 'type': 'function'}</th>\n",
        "      <td>CalcEnergyForElems</td>\n",
-       "      <td>7.361621962060903 + 2.352394804115736 * p^(1)</td>\n",
+       "      <td>7.361621962061049 + 2.352394804115736 * p^(1)</td>\n",
        "      <td>1.943577e+02</td>\n",
        "      <td>0.001304</td>\n",
        "      <td>1.490833</td>\n",
@@ -973,7 +973,7 @@
        "    <tr>\n",
        "      <th>{'name': 'CalcLagrangeElements', 'type': 'function'}</th>\n",
        "      <td>CalcLagrangeElements</td>\n",
-       "      <td>-3.807204897434129 + 1.503218405548106 * p^(3/...</td>\n",
+       "      <td>-3.8072048974344073 + 1.5032184055481064 * p^(...</td>\n",
        "      <td>7.778453e+02</td>\n",
        "      <td>0.002324</td>\n",
        "      <td>1.859640</td>\n",
@@ -983,7 +983,7 @@
        "    <tr>\n",
        "      <th>{'name': 'CalcKinematicsForElems', 'type': 'function'}</th>\n",
        "      <td>CalcKinematicsForElems</td>\n",
-       "      <td>-3.191063749981487 + 1.4407396344757235 * p^(3...</td>\n",
+       "      <td>-3.191063749981473 + 1.4407396344757237 * p^(3...</td>\n",
        "      <td>9.575080e+02</td>\n",
        "      <td>0.002845</td>\n",
        "      <td>1.935065</td>\n",
@@ -993,7 +993,7 @@
        "    <tr>\n",
        "      <th>{'name': 'CalcQForElems', 'type': 'function'}</th>\n",
        "      <td>CalcQForElems</td>\n",
-       "      <td>6.9792314208886985 + 2.7990338043031584 * p^(3...</td>\n",
+       "      <td>6.979231420888447 + 2.7990338043031593 * p^(3/...</td>\n",
        "      <td>3.048464e+05</td>\n",
        "      <td>0.191824</td>\n",
        "      <td>17.597453</td>\n",
@@ -1003,7 +1003,7 @@
        "    <tr>\n",
        "      <th>{'name': 'CalcMonotonicQForElems', 'type': 'function'}</th>\n",
        "      <td>CalcMonotonicQForElems</td>\n",
-       "      <td>-4.021092516288966 + 0.49595942520127595 * p^(...</td>\n",
+       "      <td>-4.0210925162890385 + 0.495959425201276 * p^(3...</td>\n",
        "      <td>1.628638e+02</td>\n",
        "      <td>0.014441</td>\n",
        "      <td>4.535001</td>\n",
@@ -1013,7 +1013,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Irecv', 'type': 'function'}</th>\n",
        "      <td>MPI_Irecv</td>\n",
-       "      <td>0.020640239201914115 + 0.0009474689031858106 *...</td>\n",
+       "      <td>0.02064023920191448 + 0.0009474689031858106 * ...</td>\n",
        "      <td>1.147505e-02</td>\n",
        "      <td>0.003239</td>\n",
        "      <td>2.299890</td>\n",
@@ -1023,7 +1023,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Isend', 'type': 'function'}</th>\n",
        "      <td>MPI_Isend</td>\n",
-       "      <td>-0.10548527169972584 + 0.004772194045454105 * ...</td>\n",
+       "      <td>-0.10548527169972771 + 0.0047721940454541045 *...</td>\n",
        "      <td>1.151804e+00</td>\n",
        "      <td>0.009142</td>\n",
        "      <td>3.781670</td>\n",
@@ -1033,7 +1033,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Wait', 'type': 'function'}</th>\n",
        "      <td>MPI_Wait</td>\n",
-       "      <td>58.43177001577173 + 0.1080527747143478 * p^(3/...</td>\n",
+       "      <td>58.431770015771825 + 0.1080527747143478 * p^(3...</td>\n",
        "      <td>2.687218e+05</td>\n",
        "      <td>2.301295</td>\n",
        "      <td>46.138279</td>\n",
@@ -1043,7 +1043,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Waitall', 'type': 'function'}</th>\n",
        "      <td>MPI_Waitall</td>\n",
-       "      <td>-28.32003046236931 + 0.5649498323219665 * p^(1...</td>\n",
+       "      <td>-28.32003046236923 + 0.5649498323219662 * p^(1...</td>\n",
        "      <td>1.854066e+03</td>\n",
        "      <td>7.732766</td>\n",
        "      <td>40.064332</td>\n",
@@ -1053,7 +1053,7 @@
        "    <tr>\n",
        "      <th>{'name': 'LagrangeNodal', 'type': 'function'}</th>\n",
        "      <td>LagrangeNodal</td>\n",
-       "      <td>-501.72149964371744 + 103.60284423963036 * p^(...</td>\n",
+       "      <td>-501.7214996437158 + 103.6028442396303 * p^(3/4)</td>\n",
        "      <td>1.307338e+06</td>\n",
        "      <td>0.052532</td>\n",
        "      <td>9.539895</td>\n",
@@ -1063,7 +1063,7 @@
        "    <tr>\n",
        "      <th>{'name': 'CalcForceForNodes', 'type': 'function'}</th>\n",
        "      <td>CalcForceForNodes</td>\n",
-       "      <td>-483.43658919501917 + 94.44404618163023 * p^(3/4)</td>\n",
+       "      <td>-483.4365891950169 + 94.44404618163016 * p^(3/4)</td>\n",
        "      <td>7.615735e+05</td>\n",
        "      <td>0.028787</td>\n",
        "      <td>6.373836</td>\n",
@@ -1073,7 +1073,7 @@
        "    <tr>\n",
        "      <th>{'name': 'CalcVolumeForceForElems', 'type': 'function'}</th>\n",
        "      <td>CalcVolumeForceForElems</td>\n",
-       "      <td>-8.92482471480874 + 18.08714277493425 * p^(1)</td>\n",
+       "      <td>-8.92482471480722 + 18.087142774934247 * p^(1)</td>\n",
        "      <td>4.205287e+04</td>\n",
        "      <td>0.007481</td>\n",
        "      <td>3.251360</td>\n",
@@ -1083,7 +1083,7 @@
        "    <tr>\n",
        "      <th>{'name': 'CalcHourglassControlForElems', 'type': 'function'}</th>\n",
        "      <td>CalcHourglassControlForElems</td>\n",
-       "      <td>-18.40764170193447 + 15.299434861302805 * p^(1)</td>\n",
+       "      <td>-18.407641701933926 + 15.299434861302803 * p^(1)</td>\n",
        "      <td>3.933893e+04</td>\n",
        "      <td>0.005348</td>\n",
        "      <td>3.043918</td>\n",
@@ -1093,7 +1093,7 @@
        "    <tr>\n",
        "      <th>{'name': 'CalcFBHourglassForceForElems', 'type': 'function'}</th>\n",
        "      <td>CalcFBHourglassForceForElems</td>\n",
-       "      <td>-3.4541479782963016 + 1.9523476557370119 * p^(...</td>\n",
+       "      <td>-3.454147978296478 + 1.9523476557370125 * p^(3...</td>\n",
        "      <td>4.555233e+02</td>\n",
        "      <td>0.007429</td>\n",
        "      <td>2.856552</td>\n",
@@ -1103,7 +1103,7 @@
        "    <tr>\n",
        "      <th>{'name': 'IntegrateStressForElems', 'type': 'function'}</th>\n",
        "      <td>IntegrateStressForElems</td>\n",
-       "      <td>-3.221035780650815 + 1.315518813127615 * p^(3/...</td>\n",
+       "      <td>-3.2210357806509404 + 1.3155188131276156 * p^(...</td>\n",
        "      <td>9.669293e+02</td>\n",
        "      <td>0.003183</td>\n",
        "      <td>2.237380</td>\n",
@@ -1113,7 +1113,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Irecv', 'type': 'function'}</th>\n",
        "      <td>MPI_Irecv</td>\n",
-       "      <td>0.07139784821037212 + 0.0012913170911470282 * ...</td>\n",
+       "      <td>0.07139784821037228 + 0.0012913170911470285 * ...</td>\n",
        "      <td>1.217607e-02</td>\n",
        "      <td>0.038615</td>\n",
        "      <td>4.834306</td>\n",
@@ -1123,7 +1123,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Isend', 'type': 'function'}</th>\n",
        "      <td>MPI_Isend</td>\n",
-       "      <td>-0.13477165012240291 + 0.00698057589679385 * p...</td>\n",
+       "      <td>-0.13477165012239908 + 0.0069805758967938485 *...</td>\n",
        "      <td>4.308979e-01</td>\n",
        "      <td>0.005076</td>\n",
        "      <td>2.843889</td>\n",
@@ -1133,17 +1133,17 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Wait', 'type': 'function'}</th>\n",
        "      <td>MPI_Wait</td>\n",
-       "      <td>200.41963360000003</td>\n",
+       "      <td>200.4196336</td>\n",
        "      <td>6.491636e+04</td>\n",
        "      <td>23.569468</td>\n",
        "      <td>57.976850</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.232703</td>\n",
+       "      <td>0.363086</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Waitall', 'type': 'function'}</th>\n",
        "      <td>MPI_Waitall</td>\n",
-       "      <td>-60.47462228639042 + 15.850591840236685 * p^(2/3)</td>\n",
+       "      <td>-60.474622286390584 + 15.850591840236689 * p^(...</td>\n",
        "      <td>2.200269e+05</td>\n",
        "      <td>124572.019480</td>\n",
        "      <td>70.449504</td>\n",
@@ -1153,7 +1153,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Irecv', 'type': 'function'}</th>\n",
        "      <td>MPI_Irecv</td>\n",
-       "      <td>-0.05528188247031351 + 0.005354748856582521 * ...</td>\n",
+       "      <td>-0.0552818824703146 + 0.005354748856582524 * p...</td>\n",
        "      <td>7.789738e-01</td>\n",
        "      <td>0.144243</td>\n",
        "      <td>13.336394</td>\n",
@@ -1163,7 +1163,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Isend', 'type': 'function'}</th>\n",
        "      <td>MPI_Isend</td>\n",
-       "      <td>-0.03796477449325698 + 0.008521820556730945 * ...</td>\n",
+       "      <td>-0.037964774493255325 + 0.008521820556730947 *...</td>\n",
        "      <td>9.730921e+00</td>\n",
        "      <td>0.178043</td>\n",
        "      <td>17.445265</td>\n",
@@ -1173,7 +1173,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Wait', 'type': 'function'}</th>\n",
        "      <td>MPI_Wait</td>\n",
-       "      <td>-98.9890761035539 + 23.566587859308175 * log2(...</td>\n",
+       "      <td>-98.98907610355371 + 23.566587859308143 * log2...</td>\n",
        "      <td>9.984181e+03</td>\n",
        "      <td>93.165856</td>\n",
        "      <td>73.120318</td>\n",
@@ -1183,7 +1183,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Waitall', 'type': 'function'}</th>\n",
        "      <td>MPI_Waitall</td>\n",
-       "      <td>-90.56809038671462 + 5.542383651985729 * log2(...</td>\n",
+       "      <td>-90.56809038671449 + 5.542383651985727 * log2(...</td>\n",
        "      <td>6.315946e+04</td>\n",
        "      <td>2.822066</td>\n",
        "      <td>37.003734</td>\n",
@@ -1193,7 +1193,7 @@
        "    <tr>\n",
        "      <th>{'name': 'TimeIncrement', 'type': 'function'}</th>\n",
        "      <td>TimeIncrement</td>\n",
-       "      <td>108.79336164114453 + 0.4165985180061981 * p^(5...</td>\n",
+       "      <td>108.79336164114473 + 0.4165985180061981 * p^(5...</td>\n",
        "      <td>5.231852e+06</td>\n",
        "      <td>0.681284</td>\n",
        "      <td>34.858860</td>\n",
@@ -1203,7 +1203,7 @@
        "    <tr>\n",
        "      <th>{'name': 'MPI_Allreduce', 'type': 'function'}</th>\n",
        "      <td>MPI_Allreduce</td>\n",
-       "      <td>108.66456642370485 + 0.4164978218650326 * p^(5...</td>\n",
+       "      <td>108.6645664237055 + 0.4164978218650327 * p^(5/...</td>\n",
        "      <td>5.232565e+06</td>\n",
        "      <td>0.681661</td>\n",
        "      <td>34.862073</td>\n",
@@ -1265,51 +1265,51 @@
        "\n",
        "                                                                              Total time_extrap-model  \\\n",
        "node                                                                                                    \n",
-       "{'name': 'MPI_Allreduce', 'type': 'function'}       -0.002483573830186518 + 4.672313710732994e-09 ...   \n",
-       "{'name': 'MPI_Bcast', 'type': 'function'}           0.0055946227666686076 + 1.1211777143604542e-05...   \n",
-       "{'name': 'MPI_Comm_dup', 'type': 'function'}        0.20714199930961935 + 0.0003794872132338879 * ...   \n",
-       "{'name': 'MPI_Comm_free', 'type': 'function'}       2.974896446151339e-05 + 2.9632810629043057e-06...   \n",
-       "{'name': 'MPI_Comm_split', 'type': 'function'}      0.03409920994697278 + 4.861767349462249e-07 * ...   \n",
-       "{'name': 'MPI_Gather', 'type': 'function'}          1.111071710084141e-05 + 1.8759317720297382e-09...   \n",
-       "{'name': 'MPI_Initialized', 'type': 'function'}     -1.2157511991338818e-06 + 4.4736500077363466e-...   \n",
-       "{'name': 'main', 'type': 'function'}                   5.3345854025489245 + 50.44722398708033 * p^(1)   \n",
-       "{'name': 'MPI_Barrier', 'type': 'function'}         -3.8613044063814517 + 0.722886713004767 * log2...   \n",
-       "{'name': 'MPI_Irecv', 'type': 'function'}           0.0001693042315778418 + 5.34448636184851e-05 *...   \n",
-       "{'name': 'MPI_Isend', 'type': 'function'}           -0.7378766027055425 + 0.011372374310428847 * p...   \n",
-       "{'name': 'MPI_Reduce', 'type': 'function'}          0.00895051336452584 + 2.1551606902927186e-05 *...   \n",
-       "{'name': 'MPI_Wait', 'type': 'function'}            -0.08273770855385464 + 0.0008985035981343795 *...   \n",
+       "{'name': 'MPI_Allreduce', 'type': 'function'}       -0.002483573830186528 + 4.6723137107329955e-09...   \n",
+       "{'name': 'MPI_Bcast', 'type': 'function'}           0.00559462276666876 + 1.1211777143604536e-05 *...   \n",
+       "{'name': 'MPI_Comm_dup', 'type': 'function'}        0.20714199930961907 + 0.00037948721323388787 *...   \n",
+       "{'name': 'MPI_Comm_free', 'type': 'function'}       2.9748964461513302e-05 + 2.9632810629043057e-0...   \n",
+       "{'name': 'MPI_Comm_split', 'type': 'function'}      0.03409920994697361 + 4.861767349462246e-07 * ...   \n",
+       "{'name': 'MPI_Gather', 'type': 'function'}          1.1110717100841163e-05 + 1.875931772029739e-09...   \n",
+       "{'name': 'MPI_Initialized', 'type': 'function'}     -1.215751199133331e-06 + 4.473650007736346e-06...   \n",
+       "{'name': 'main', 'type': 'function'}                    5.334585402553237 + 50.44722398708032 * p^(1)   \n",
+       "{'name': 'MPI_Barrier', 'type': 'function'}         -3.8613044063814534 + 0.7228867130047674 * log...   \n",
+       "{'name': 'MPI_Irecv', 'type': 'function'}           0.0001693042315778342 + 5.3444863618485094e-05...   \n",
+       "{'name': 'MPI_Isend', 'type': 'function'}           -0.7378766027055431 + 0.011372374310428852 * p...   \n",
+       "{'name': 'MPI_Reduce', 'type': 'function'}          0.008950513364525844 + 2.1551606902927193e-05 ...   \n",
+       "{'name': 'MPI_Wait', 'type': 'function'}            -0.08273770855385452 + 0.0008985035981343793 *...   \n",
        "{'name': 'MPI_Waitall', 'type': 'function'}                                                 0.0118324   \n",
-       "{'name': 'lulesh.cycle', 'type': 'function'}            6.361023656688513 + 50.40069659189234 * p^(1)   \n",
-       "{'name': 'LagrangeLeapFrog', 'type': 'function'}     -588.7139217783116 + 118.0201880558252 * p^(4/5)   \n",
-       "{'name': 'CalcTimeConstraintsForElems', 'type':...   0.29121283787713886 + 0.1546446810459539 * p^(1)   \n",
-       "{'name': 'LagrangeElements', 'type': 'function'}       55.80235706834194 + 12.219839260204246 * p^(1)   \n",
-       "{'name': 'ApplyMaterialPropertiesForElems', 'ty...      6.909192329599132 + 3.812718071421943 * p^(1)   \n",
-       "{'name': 'EvalEOSForElems', 'type': 'function'}         7.437812732121217 + 3.719775901083089 * p^(1)   \n",
-       "{'name': 'CalcEnergyForElems', 'type': 'function'}      7.361621962060903 + 2.352394804115736 * p^(1)   \n",
-       "{'name': 'CalcLagrangeElements', 'type': 'funct...  -3.807204897434129 + 1.503218405548106 * p^(3/...   \n",
-       "{'name': 'CalcKinematicsForElems', 'type': 'fun...  -3.191063749981487 + 1.4407396344757235 * p^(3...   \n",
-       "{'name': 'CalcQForElems', 'type': 'function'}       6.9792314208886985 + 2.7990338043031584 * p^(3...   \n",
-       "{'name': 'CalcMonotonicQForElems', 'type': 'fun...  -4.021092516288966 + 0.49595942520127595 * p^(...   \n",
-       "{'name': 'MPI_Irecv', 'type': 'function'}           0.020640239201914115 + 0.0009474689031858106 *...   \n",
-       "{'name': 'MPI_Isend', 'type': 'function'}           -0.10548527169972584 + 0.004772194045454105 * ...   \n",
-       "{'name': 'MPI_Wait', 'type': 'function'}            58.43177001577173 + 0.1080527747143478 * p^(3/...   \n",
-       "{'name': 'MPI_Waitall', 'type': 'function'}         -28.32003046236931 + 0.5649498323219665 * p^(1...   \n",
-       "{'name': 'LagrangeNodal', 'type': 'function'}       -501.72149964371744 + 103.60284423963036 * p^(...   \n",
-       "{'name': 'CalcForceForNodes', 'type': 'function'}   -483.43658919501917 + 94.44404618163023 * p^(3/4)   \n",
-       "{'name': 'CalcVolumeForceForElems', 'type': 'fu...      -8.92482471480874 + 18.08714277493425 * p^(1)   \n",
-       "{'name': 'CalcHourglassControlForElems', 'type'...    -18.40764170193447 + 15.299434861302805 * p^(1)   \n",
-       "{'name': 'CalcFBHourglassForceForElems', 'type'...  -3.4541479782963016 + 1.9523476557370119 * p^(...   \n",
-       "{'name': 'IntegrateStressForElems', 'type': 'fu...  -3.221035780650815 + 1.315518813127615 * p^(3/...   \n",
-       "{'name': 'MPI_Irecv', 'type': 'function'}           0.07139784821037212 + 0.0012913170911470282 * ...   \n",
-       "{'name': 'MPI_Isend', 'type': 'function'}           -0.13477165012240291 + 0.00698057589679385 * p...   \n",
-       "{'name': 'MPI_Wait', 'type': 'function'}                                           200.41963360000003   \n",
-       "{'name': 'MPI_Waitall', 'type': 'function'}         -60.47462228639042 + 15.850591840236685 * p^(2/3)   \n",
-       "{'name': 'MPI_Irecv', 'type': 'function'}           -0.05528188247031351 + 0.005354748856582521 * ...   \n",
-       "{'name': 'MPI_Isend', 'type': 'function'}           -0.03796477449325698 + 0.008521820556730945 * ...   \n",
-       "{'name': 'MPI_Wait', 'type': 'function'}            -98.9890761035539 + 23.566587859308175 * log2(...   \n",
-       "{'name': 'MPI_Waitall', 'type': 'function'}         -90.56809038671462 + 5.542383651985729 * log2(...   \n",
-       "{'name': 'TimeIncrement', 'type': 'function'}       108.79336164114453 + 0.4165985180061981 * p^(5...   \n",
-       "{'name': 'MPI_Allreduce', 'type': 'function'}       108.66456642370485 + 0.4164978218650326 * p^(5...   \n",
+       "{'name': 'lulesh.cycle', 'type': 'function'}            6.361023656692823 + 50.40069659189231 * p^(1)   \n",
+       "{'name': 'LagrangeLeapFrog', 'type': 'function'}    -588.7139217783108 + 118.02018805582516 * p^(4/5)   \n",
+       "{'name': 'CalcTimeConstraintsForElems', 'type':...    0.2912128378771511 + 0.1546446810459539 * p^(1)   \n",
+       "{'name': 'LagrangeElements', 'type': 'function'}       55.80235706834264 + 12.219839260204242 * p^(1)   \n",
+       "{'name': 'ApplyMaterialPropertiesForElems', 'ty...      6.909192329599409 + 3.812718071421941 * p^(1)   \n",
+       "{'name': 'EvalEOSForElems', 'type': 'function'}         7.437812732121435 + 3.719775901083088 * p^(1)   \n",
+       "{'name': 'CalcEnergyForElems', 'type': 'function'}      7.361621962061049 + 2.352394804115736 * p^(1)   \n",
+       "{'name': 'CalcLagrangeElements', 'type': 'funct...  -3.8072048974344073 + 1.5032184055481064 * p^(...   \n",
+       "{'name': 'CalcKinematicsForElems', 'type': 'fun...  -3.191063749981473 + 1.4407396344757237 * p^(3...   \n",
+       "{'name': 'CalcQForElems', 'type': 'function'}       6.979231420888447 + 2.7990338043031593 * p^(3/...   \n",
+       "{'name': 'CalcMonotonicQForElems', 'type': 'fun...  -4.0210925162890385 + 0.495959425201276 * p^(3...   \n",
+       "{'name': 'MPI_Irecv', 'type': 'function'}           0.02064023920191448 + 0.0009474689031858106 * ...   \n",
+       "{'name': 'MPI_Isend', 'type': 'function'}           -0.10548527169972771 + 0.0047721940454541045 *...   \n",
+       "{'name': 'MPI_Wait', 'type': 'function'}            58.431770015771825 + 0.1080527747143478 * p^(3...   \n",
+       "{'name': 'MPI_Waitall', 'type': 'function'}         -28.32003046236923 + 0.5649498323219662 * p^(1...   \n",
+       "{'name': 'LagrangeNodal', 'type': 'function'}        -501.7214996437158 + 103.6028442396303 * p^(3/4)   \n",
+       "{'name': 'CalcForceForNodes', 'type': 'function'}    -483.4365891950169 + 94.44404618163016 * p^(3/4)   \n",
+       "{'name': 'CalcVolumeForceForElems', 'type': 'fu...     -8.92482471480722 + 18.087142774934247 * p^(1)   \n",
+       "{'name': 'CalcHourglassControlForElems', 'type'...   -18.407641701933926 + 15.299434861302803 * p^(1)   \n",
+       "{'name': 'CalcFBHourglassForceForElems', 'type'...  -3.454147978296478 + 1.9523476557370125 * p^(3...   \n",
+       "{'name': 'IntegrateStressForElems', 'type': 'fu...  -3.2210357806509404 + 1.3155188131276156 * p^(...   \n",
+       "{'name': 'MPI_Irecv', 'type': 'function'}           0.07139784821037228 + 0.0012913170911470285 * ...   \n",
+       "{'name': 'MPI_Isend', 'type': 'function'}           -0.13477165012239908 + 0.0069805758967938485 *...   \n",
+       "{'name': 'MPI_Wait', 'type': 'function'}                                                  200.4196336   \n",
+       "{'name': 'MPI_Waitall', 'type': 'function'}         -60.474622286390584 + 15.850591840236689 * p^(...   \n",
+       "{'name': 'MPI_Irecv', 'type': 'function'}           -0.0552818824703146 + 0.005354748856582524 * p...   \n",
+       "{'name': 'MPI_Isend', 'type': 'function'}           -0.037964774493255325 + 0.008521820556730947 *...   \n",
+       "{'name': 'MPI_Wait', 'type': 'function'}            -98.98907610355371 + 23.566587859308143 * log2...   \n",
+       "{'name': 'MPI_Waitall', 'type': 'function'}         -90.56809038671449 + 5.542383651985727 * log2(...   \n",
+       "{'name': 'TimeIncrement', 'type': 'function'}       108.79336164114473 + 0.4165985180061981 * p^(5...   \n",
+       "{'name': 'MPI_Allreduce', 'type': 'function'}       108.6645664237055 + 0.4164978218650327 * p^(5/...   \n",
        "\n",
        "                                                    Total time_RSS_extrap-model  \\\n",
        "node                                                                              \n",
@@ -1518,7 +1518,7 @@
        "{'name': 'MPI_Isend', 'type': 'function'}                            19.620193  \n",
        "{'name': 'MPI_Reduce', 'type': 'function'}                            1.295318  \n",
        "{'name': 'MPI_Wait', 'type': 'function'}                              1.000543  \n",
-       "{'name': 'MPI_Waitall', 'type': 'function'}                          16.324158  \n",
+       "{'name': 'MPI_Waitall', 'type': 'function'}                           0.508850  \n",
        "{'name': 'lulesh.cycle', 'type': 'function'}                          0.157454  \n",
        "{'name': 'LagrangeLeapFrog', 'type': 'function'}                      0.076241  \n",
        "{'name': 'CalcTimeConstraintsForElems', 'type':...                    0.027639  \n",
@@ -1542,7 +1542,7 @@
        "{'name': 'IntegrateStressForElems', 'type': 'fu...                    0.022230  \n",
        "{'name': 'MPI_Irecv', 'type': 'function'}                             0.051334  \n",
        "{'name': 'MPI_Isend', 'type': 'function'}                             0.028019  \n",
-       "{'name': 'MPI_Wait', 'type': 'function'}                              0.232703  \n",
+       "{'name': 'MPI_Wait', 'type': 'function'}                              0.363086  \n",
        "{'name': 'MPI_Waitall', 'type': 'function'}                          70.913096  \n",
        "{'name': 'MPI_Irecv', 'type': 'function'}                             0.133853  \n",
        "{'name': 'MPI_Isend', 'type': 'function'}                             0.182547  \n",
@@ -1563,13 +1563,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e338d38",
+   "id": "5038594f",
    "metadata": {
     "papermill": {
-     "duration": 0.002055,
-     "end_time": "2023-12-09T20:58:41.400711",
+     "duration": 0.003126,
+     "end_time": "2023-12-22T05:36:05.783408",
      "exception": false,
-     "start_time": "2023-12-09T20:58:41.398656",
+     "start_time": "2023-12-22T05:36:05.780282",
      "status": "completed"
     },
     "tags": []
@@ -1583,19 +1583,19 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "d538f63a",
+   "id": "32d42ba9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:41.405258Z",
-     "iopub.status.busy": "2023-12-09T20:58:41.405170Z",
-     "iopub.status.idle": "2023-12-09T20:58:42.314291Z",
-     "shell.execute_reply": "2023-12-09T20:58:42.313948Z"
+     "iopub.execute_input": "2023-12-22T05:36:05.790682Z",
+     "iopub.status.busy": "2023-12-22T05:36:05.790550Z",
+     "iopub.status.idle": "2023-12-22T05:36:06.809282Z",
+     "shell.execute_reply": "2023-12-22T05:36:06.808856Z"
     },
     "papermill": {
-     "duration": 0.915539,
-     "end_time": "2023-12-09T20:58:42.318332",
+     "duration": 1.027741,
+     "end_time": "2023-12-22T05:36:06.814241",
      "exception": false,
-     "start_time": "2023-12-09T20:58:41.402793",
+     "start_time": "2023-12-22T05:36:05.786500",
      "status": "completed"
     },
     "scrolled": true,
@@ -1619,55 +1619,55 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Allreduce', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Bcast', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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znAPV1gTeFIbnV76/n1G9kS+i8ie1cRGMxbSvlg+XvVeScBhySaAMw67rFjdtosy6d/acsluYpooHWFRN5pYsm/LEeRL0YbsjpzW/oGozalprSXKxrcwzzW0vlghWaORkLKCSQDtzjJxnGTVFfCNt5Egk1G/mvGeJ0vpGj86Pys7AuE24G5uqnO9s5zWrpemw6VYLaQvJIAzyPJIQXkd2LMzYAGSzE8ADngCgC5RRRQAUUUUAFcP/wAJPrX9u/2Er2b3UkyRJdPYSwJGdkzv+7aTdIuIeHVgpLdeDXbsNykZIyMZHUVzsnhCKe5W8uNX1KbUIipgvGMIkgxuGFAjCch2B3Kcg/SgDHPi7Uhpk81pb2UQ0mxkub6IxsRJ5ckkZSLDDZnyJCCd2MqMdTXdAhgCOh5rnJvBWnS2sVstzeRReQba5COv+lxlizLKSp6szEldp+duea6SgAooooAKKKKAILy7gsLOa7uX2QwoXdsZwB7d/pXF2/iXX9UmuNMFrp1veBrl2SeNpEEUawkRsA4y5M4BYHHyng8V2t1aW99bSW13bxXFvIMPFKgdWHoQeDXNr8P9GtpJ5dM83S5pncvJYpFGdjqqtGPkI2nYp6ZB5BFAFWHxhe3qx6lbRW6aWstlDLFIjGZmuRGQVYMAAvnR8FTnDcjiu0rBPhKwF5FLFLcQ26GFms42XyZGhx5TNlS2V2r0YA7FznFb1ABRRRQAUUUUAYfifU9Q0y3sWsEH7668ueQ2Ut15Uflu27y4yGPzKoz0G6shPE93cTLeQvp91plteWlnJIkLB5ZJ1j/exneQij7QnykMcbua6i/s5ryFUg1G6sWDZMluIySMdD5iMMfhnise18FadZywGCe7W3jeKV7XepjmljA2SPldxYYU8EAlRkcUARaH4hv7+/sftSWwtNTt5rm1ESMHiVHQBXJYhiyuDkBcEEc9a6isXTPDVrpd8LqO4uZRGkkdvDKylLZHYMyphQcEqv3icBQBgVtUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFYXjDVZNF8OtfxRGVo7q1UoGZSwa4jUgbSDnDHjoehBGQd2qWpaZFqkdvHNLKscNxHcFEIAkaNg6hsgnAZVPGOg5xkEA4jUPEmtPbWsDXVilxK+nXMc9sHSNVmm27HG8lhx1BXcMjAq7L4p1mGHzJhaJa2k88N9fR2ryxjYyhSEEgZAQWycvtKkGumXQNGS2ltl0iwW3mcSSRC2QK7D+IjGCfc0N4f0VktkbSLApa/8e6m2TEPOfk4+XnnigDmptc1p1a30maxF9LqNxbxQXEEk25UfBkJ80bEUcnjGSoABIBtaR4g1vU9ddf7MVdJW7uLUynYChiZlDZ80s24p93y1wGzkgZOzdeHNDvnD3ejadcOCzBpbVHILHLHkdzyfWp10nTV1I6iun2gviMG5EK+aRjH3sZ6e9AFyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/9k=\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Comm_dup', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Comm_free', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Comm_split', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Gather', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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kkm1uFFFYtz4u0Cz1YaXcanDHeEgbGzgE9i2NoP1NDko7sqnSqVW1Ti3bXRX0NqisufxJodtnzdXslI7eepP5A1V/wCEv0h+LV7m7b0trWR8/iFxWipzfRmDqwW7RvUVg/29qE//AB5+HL9/9q4ZIB+pJ/Sjd4puuiaXYIfUvO4/9BFP2b6tL5i9quib+RvVXu7+zsI995dQ26+ssgX+deU6b42j1rxLHpd9qWqNbzSiGGe3dYFdicDKKu4An/aPWvSLTwzotlJ5kWnwtL182YeY/wD302TWcJ0Zq8ZX9P8Ag/5HVisHi8LJRrU+VtXV3/lf8yufFdtcHbpdne6k3QNBCRHn3dsL/Ok2+JtQ6vZ6TEeyj7RL+Zwo/Wt/GBgUVpzpfCvv1/4H4HLySfxP7tP+D+Jhw+FdPMqzX5m1Kcch7194H0T7o/KttVVFCooVQMAAYApaKmU5S3ZUYRjsgoooqSgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDG1v8A5C3hz/sIv/6S3FbNY2t/8hbw5/2EX/8ASW4rZoAKKKKACiiigArD8VQyy6TC8cMk6QXtvPLFGhdmjSVWbCjliMbsDk7eMmtyigDhXt7i48L6ssdldAT6h9ujtmt3VngE6s67SB8zhHOw/N8/I5qKNZYtbbXpNPvm0uTUpZEiFnIZV3WscYkMO3eAWSQfdz8+TwSa7+igDzCHStRtNKGnT6fdNd3en6fDbMkLOsDRyMSrOAQnl5DZJGe2SK19LtLka5Y2ps7lJ7PU7+6uLhoWEbwymUoBIRtYnzIuATjZzjAruKKAOd8WJeG1smingW1GoWXmxtAWdj9qixtfeAv4qfwroqy/EFldX+lCKyWFrhLm3nVZpCiN5cySEFgrEZCkdDUX2nxN/wBAjSP/AAaSf/I9AHn/AI98Ba/rfitr+wVJ7edUX5pQvkYABGCenU8Z6mu1s/B9vFZQRXV/qU8qRqrt9tlAZgOSBngVb+0+Jv8AoEaR/wCDST/5Ho+0+Jv+gRpH/g0k/wDkes6VNUpynB6s7sXj6uLoU8PVS5ae2n5kf/CHaGfv2ssn/XS5lb+bVwuofCCS58RvPbXsMWlyyb2Q7vMQE5KqMYPsSa777T4m/wCgRpH/AINJP/kej7T4m/6BGkf+DST/AOR6qtH2ySqa2IwGNq4CUp4aybVnoi/BptjbY+z2VvFj/nnEq/yFWqxvtPib/oEaR/4NJP8A5Ho+0+Jv+gRpH/g0k/8Akerbb3ONJLY2aK5yPWPEUmq3GnjRtL82CCKdmOqSbSrtIoA/cdf3Z/MVa+0+Jv8AoEaR/wCDST/5HpDMyy+HPh+w18axDFN5qv5kcLODFG3qBjPHbmusrG+0+Jv+gRpH/g0k/wDkej7T4m/6BGkf+DST/wCR6mMIw+FWN6+JrYhp1pOTStqbNFY32nxN/wBAjSP/AAaSf/I9H2nxN/0CNI/8Gkn/AMj1RgbNFY32nxN/0CNI/wDBpJ/8j0fafE3/AECNI/8ABpJ/8j0AbNFc5Yax4i1G2eeLRtLVVnlgIbVJM7o5GjJ/1HTKnHtVr7T4m/6BGkf+DST/AOR6ANmisb7T4m/6BGkf+DST/wCR6PtPib/oEaR/4NJP/kegDZorG+0+Jv8AoEaR/wCDST/5Ho+0+Jv+gRpH/g0k/wDkegDZorG+0+Jv+gRpH/g0k/8Akej7T4m/6BGkf+DST/5HoA2aK5zTdY8RappVnqEOjaWsV1Ak6K+qSbgrKGAOIOvNWvtPib/oEaR/4NJP/kegDZorG+0+Jv8AoEaR/wCDST/5Ho+0+Jv+gRpH/g0k/wDkegDZorG+0+Jv+gRpH/g0k/8Akej7T4m/6BGkf+DST/5HoA2aKxvtPib/AKBGkf8Ag0k/+R6q6lrHiLS9KvNQm0bS2itYHndU1STcVVSxAzB14oA6Oisb7T4m/wCgRpH/AINJP/kej7T4m/6BGkf+DST/AOR6ANmisb7T4m/6BGkf+DST/wCR6PtPib/oEaR/4NJP/kegDZorG+0+Jv8AoEaR/wCDST/5Ho+0+Jv+gRpH/g0k/wDkegDZorG+0+Jv+gRpH/g0k/8Akeqt1rHiK0ubGB9G0steTmBCuqSYDCN5Mn9x0xGfxIoA6Oisb7T4m/6BGkf+DST/AOR6PtPib/oEaR/4NJP/AJHoANb/AOQt4c/7CL/+ktxWzXPvb67f6rpUt5Zadb29nctO7Q3zyu2YZIwAphUdZAevaugoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArF1K/wBQs/EWjW8bWpsL2V4JFaNjKGEUkgIbdgD5AMbT1PIrarI1TQjqmpWF7/al9amxkMkcUAi2MxVlJbfGx5V2HBH580Ac1H4v1GOygv5raydtV08XdgqIymMGSJESVix3f8fEZyAv8XHeul0PULy6l1GzvzA91Y3AiaWCMokgaNJAwUsxX7+MZPTPeqUXgrTkt5rd7i8lhNuba3R3X/RIywbbEQoIwVQgtuPyLzxWrpWlJpcdxi4nuZ7mXzp7ifbvkbaFGdqqowqqMADpQBfooooAKKKKACuU8S+ItQ8NT/apDaXNmUmkFpHEyzrHFC0jOX3EEAqAflH3xznAPV1gTeFIbnV76/n1G9kS+i8ie1cRGMxbSvlg+XvVeScBhySaAMw67rFjdtosy6d/acsluYpooHWFRN5pYsm/LEeRL0YbsjpzW/oGozalprSXKxrcwzzW0vlghWaORkLKCSQDtzjJxnGTVFfCNt5Egk1G/mvGeJ0vpGj86Pys7AuE24G5uqnO9s5zWrpemw6VYLaQvJIAzyPJIQXkd2LMzYAGSzE8ADngCgC5RRRQAUUUUAFcP/wk+tf27/YSvZvdSTJEl09hLAkZ2TO/7tpN0i4h4dWCkt14Nduw3KRkjIxkdRXOyeEIp7lby41fUptQiKmC8YwiSDG4YUCMJyHYHcpyD9KAMc+LtSGmTzWlvZRDSbGS5vojGxEnlySRlIsMNmfIkIJ3Yyox1Nd0CGAI6Hmucm8FadLaxWy3N5FF5BtrkI6/6XGWLMspKnqzMSV2n5255rpKACiiigAooooAgvLuCws5ru5fZDChd2xnAHt3+lcXb+Jdf1Sa40wWunW94GuXZJ42kQRRrCRGwDjLkzgFgcfKeDxXa3Vpb31tJbXdvFcW8gw8UqB1YehB4Nc2vw/0a2knl0zzdLmmdy8likUZ2Oqq0Y+QjadinpkHkEUAVYfGF7erHqVtFbppay2UMsUiMZma5EZBVgwAC+dHwVOcNyOK7SsE+ErAXkUsUtxDboYWazjZfJkaHHlM2VLZXavRgDsXOcVvUAFFFFABRRRQBh+J9T1DTLexawQfvrry55DZS3XlR+W7bvLjIY/MqjPQbqyE8T3dxMt5C+n3WmW15aWckiQsHlknWP8AexneQij7QnykMcbua6i/s5ryFUg1G6sWDZMluIySMdD5iMMfhnise18FadZywGCe7W3jeKV7XepjmljA2SPldxYYU8EAlRkcUARaH4hv7+/sftSWwtNTt5rm1ESMHiVHQBXJYhiyuDkBcEEc9a6isXTPDVrpd8LqO4uZRGkkdvDKylLZHYMyphQcEqv3icBQBgVtUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFYXjDVZNF8OtfxRGVo7q1UoGZSwa4jUgbSDnDHjoehBGQd2qWpaZFqkdvHNLKscNxHcFEIAkaNg6hsgnAZVPGOg5xkEA4jUPEmtPbWsDXVilxK+nXMc9sHSNVmm27HG8lhx1BXcMjAq7L4p1mGHzJhaJa2k88N9fR2ryxjYyhSEEgZAQWycvtKkGumXQNGS2ltl0iwW3mcSSRC2QK7D+IjGCfc0N4f0VktkbSLApa/8e6m2TEPOfk4+XnnigDmptc1p1a30maxF9LqNxbxQXEEk25UfBkJ80bEUcnjGSoABIBtaR4g1vU9ddf7MVdJW7uLUynYChiZlDZ80s24p93y1wGzkgZOzdeHNDvnD3ejadcOCzBpbVHILHLHkdzyfWp10nTV1I6iun2gviMG5EK+aRjH3sZ6e9AFyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP//Z\" /></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Initialized', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'main', 'type': 'function'}</th>\n",
-       "      <td><img 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+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Barrier', 'type': 'function'}</th>\n",
-       "      <td><img 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+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Irecv', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Isend', 'type': 'function'}</th>\n",
-       "      <td><img 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B0AxvUUAYEnhkTafqsMl2Tc3939r+0CP/VupXyuM8hBHGOvOD0zSQaJqttBdPDqdkb69uDPdSy2LNG3yLGAieaCuFRerNnn1roKKAOYl8K3DaLZaHFqES6XbwwxOHtd07+WQcq+8KucD+A47dsP/AOENs4Nfh1eyuLmCWMXLeU1zNJG0kxDFthk2gZBJUDByOmBXSUUAc5o+g6vpOm3MJ1WxuLyaQSm7ewcF5M/M0g875uAAACoUAAcACr+jaR/ZllPDPMlxLczyTzMsWxCznkKmTgexJ7kk5rUooAwNK8H6NpGr3eo2unWEcsrq0RitERoAECEKR64JOMdTTdY8MyanfTyx3ywW15DFBewmHeZUjZmARtw2Eh2UkhuMYwRmuhooAxtT0i+vLi2vLW/gtr22aQRSNbGRPLcAFWXeMnIU5BHIHHaqR8MXlu+gR6fqVvFaaOgUJPaNK8p2GMksJFAyp/unnnnpXTUUAYSaPqieJr3VRqVp5NxbJbrB9ibcgQyMh3+bgnMhz8oyABx1piaDqJGrXE2qxDUb+1W2Sa3tTGkIUPtYIXYs2ZCcluwHGK6CigDnNV8Mz3Vhpenade29rp9iFDWs9q0yThABGrYkT5VxnHIJxngYNjUdH1DU5rdJ9Rtlsopobho47QiQvGyvw5kIVSyjjaTgkZ71t0UAYzeHbe5n1l79zcJqipC6DMeyFUwI9wOfvNI2Rj7/ALVVsPDE+ivqkuk6kyy310k+b4zXQRViSPb80oJOVJ3ZBwQvIUV0dFAHKz+FdQGkadY2Wq20TWt215JJPZNIJJDIZMKqyLsUFmGOeMDPHM8vhiSbVnna/UWEt2l9LbCE7mmRFUYfdwvyI2NpOR15xXR0UAcnD4ITS5r6fQ9SubGWawjsrfzpZblbfYzncFeTB4YADgLg4+8RUkPhe9t9O06GHULOO50+dpYXFk5jcMjK29DKWZyXZt+8HJ+ueoooA41vAki2X2OHVFW3ms0s7oSW25mVXd8xEMBGSZG7MBhcdOdCPwdYW/iS21i2mu4miNw7wG6mZHeVlZmCl9qjIJKgYJI9BXRUUAZ2kaX/AGXDch5vPmubqS5ll27clj8oxk/dUKo9lFX1jRGdlRVLncxAxuOAMn14AH4U6igDktH8N6gmo/ab+5RLaDVLu9t7ZYhvLSGRVYuGI27JCdu0HJ5PGK6wgMCDyDwaWigDj18Las+kaHYjULWKHSkEZgntWmS4aPCxSNtkToFDbeRuOf4RWtDpmpw/2hPDd2kV5d3Qk3yQNKgjVFRV27lPRd3XgsetbVFAHN2nh2a0n0eNphLFZT3N7JLt2bppd4wq84XE0mBk4wo5610RjQyLIUUuoKhscgHGRn8B+Qp1FAHP6n4Ze+1ebU4b0Q3BS1EO6HeqNC8zZYbhuDCYjHGMZz6c/rXhbV7WxnXSXe6vtRt7mC7lEMYjzKxYHDSqYwCzAECTjqCcZ9AooA5yDw9qMXiCDUX1K1ltreFIILeSzYtCu0ByriQDcxH3ipwMD1zmr4d1q3utJSymhgWxe9AuJIxKnlyMDGCm5STg44PVc9Dg9g86q2xQXf8Aur/X0pvkvLzM3H9xen4+tOwrmBpOjNp2o2a25Mtvp+mrZQSMeXJYF2bH/XOPp6npWlrGjjWdEv8AT5pSGureSENjhNykZA9s1pgBQAAAB2FLRfsFu5z95pWr6jfaLfi8s7Q2f72W3e1aYs7IVcBxIoAwxx8p5GeelXNKsJba+1a8nwHvLoOig52oqKi/ntLf8Cx2rUopDMDUf+Pvwt/1/H/0knrfrA1H/j78Lf8AX8f/AEknrfoAKKKKACiiigArIPhrTXvbq8liMs89wbkO+CYXMCQHZxx8iDrnkn2xpfZ0/vS/9/W/xo+zp/el/wC/rf40AUNI0G10UsbaSZt0ENufMIPyxKVU8Ac4PP8ASq9sZdCjj0yz0bUru3RiwuFkt9vzMWP3pFbgk/w/nWv9nT+9L/39b/Gj7On96X/v63+NAGEfB1oY9Pga9vDa2MiSxwEREF1k8wMW2bwc4+6wyBg98wp4YiWC40ltTvhpjp5MNoVTbEvDfI+zPGMAMWwOK6P7On96X/v63+NURpD/ANpG6bVL94eotC6eUpxjIIXf78tQBQ1Pw5ZzXj3iXtzbX0t3HcJLEybkYIIiFDKQQV6gg889hjU0fSodE0qDTreSWSKHdhpm3OcsWOTgZ5NVPEBa006GSGSRXa9tIyd5PyvcRqw5PcMRWp9nT+9L/wB/W/xoAloqL7On96X/AL+t/jR9nT+9L/39b/GgCWiovs6f3pf+/rf40fZ0/vS/9/W/xoAloqL7On96X/v63+NH2dP70v8A39b/ABoAloqL7On96X/v63+NH2dP70v/AH9b/GgCWiovs6f3pf8Av63+NH2dP70v/f1v8aAJaKi+zp/el/7+t/jR9nT+9L/39b/GgCWiovs6f3pf+/rf40fZ0/vS/wDf1v8AGgCWiovs6f3pf+/rf40fZ0/vS/8Af1v8aAJaKi+zp/el/wC/rf40fZ0/vS/9/W/xoAloqL7On96X/v63+NH2dP70v/f1v8aAJaKi+zp/el/7+t/jR9nT+9L/AN/W/wAaAJaKi+zp/el/7+t/jR9nT+9L/wB/W/xoAloqL7On96X/AL+t/jR9nT+9L/39b/GgCWiovs6f3pf+/rf40fZ0/vS/9/W/xoAloqL7On96X/v63+NH2dP70v8A39b/ABoAloqneS2Wn2zXN5cmCFervMwH86xBcaprZxpccthZH/l9uSxdx/0zjJ/Vvyq4wb16ESmo6dTpJJUi+8eT0A5JqPbLN94mNPQfeP49qjt9PSCNQ0080gGGleQ7m+uMD8hU32dP70v/AH9b/Gp22K33HpGka7UUAU6ovs6f3pf+/rf40fZ0/vS/9/W/xpDJaKi+zp/el/7+t/jR9nT+9L/39b/GgCWiovs6f3pf+/rf40fZ4z1Mh/7aN/jQBi6j/wAffhb/AK/j/wCkk9b9YutALqvhsAYA1FwB/wButxW1QAUUUUAFFFFABRRRQAUUUUAFFFFAGN4o/wCQTB/2EbH/ANKoq2axvFH/ACCYP+wjY/8ApVFWzQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFZOo+ILayuPsdvHJe6gR8trb8sPdj0Qe5pxi5OyJlJRV2apIVSzEAAZJPasGXxDLfytbeH7cXjqdr3Tkrbxn/e/jPsv500aJe6uwl1+4BhzldPt2IiH++3Vz+Q9q3ooo4IliijWONBhUQYAHsKv3Y+b/Aj35+S/H/gGPZ+HYxcrfapO2o3y8q8owkX+4nRfryfetuiiplJy3LjFR2CiiipKCiiigAooooAKKKKAMbW/wDkLeHP+wi//pLcVs1ja3/yFvDn/YRf/wBJbitmgAooooAKKKKACiiigAooooAKw7nxGsPimy0WK2MomLLNPvwIW8tnVcY+YkKSRkYBU9xW5XOz+DrB/EFpq8M13DLDcvcyxi6mKSuyFPu79q9R0HIGOhoAs+KP+QTB/wBhGx/9Koq2a53xZYWc1rZXktpA91DqFkIp2jBeMG6iztbGR17V0VABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFNd0ijaSR1RFGWZjgAe5oAdXGp8T/Db6rJYCW4ypKrMIspIw7LgknPbitFtcu9Xcw+H4VeLOG1CcEQr/ALg6ufpx71zuj/Cey0rXrfUn1KWdLdxLHCYgvzjkEtnpnnGKitGrBx5V6ndl7wNSFX6zJp2922zf9W8t9Tof+J1r/wDz00fTj9PtMo/lGPzP0rW07S7LSrfybKBYlJyxHLOfVieSfc1coraU21ZaI86MEnd6sKKKKgsKKKKACiiigAooooAKKKKACiiigDG1v/kLeHP+wi//AKS3FbNY2t/8hbw5/wBhF/8A0luK2aACiiigAooooAKKKKACiiuP8dz3NkNLvo7sRW8FwjSRG+W28wiWNursqsNiyrtJx84OOOADsKKx/DEz3OgQ3El1FcmaSaUPFcCdVVpWIQOOG2AhOP7tbFAGN4o/5BMH/YRsf/SqKtmsbxR/yCYP+wjY/wDpVFWzQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFVdQ1Kz0u2NxezpDGOAW6sfQDqT7CsbdrHiD7vm6Rpp/iPFzKPb/AJ5j8z9KuMG1d6IiU0nZasuaj4ggtLn7FaxPfaiRxbQdV93bog9zVVNBudUkWfxDOsyg7ksYciBP97u5+vHtWtp2mWelW3kWUCxJnLEcsx9WJ5J9zVunzqPwff1/4BPI5az+7p/wRFVUQIihVUYAAwAKWiiszUKKKKACiiigAooooAKKKKACiiigAooooAKKKKAMbW/+Qt4c/wCwi/8A6S3FbNY2t/8AIW8Of9hF/wD0luK2aACiiigAooooAKKKKACuP8YSS2+uaNPG8kAWK5U3CabJe7cmL5CqD5QcZ3H+5gdTXYVyHiGK6m12GFdC1q8s/Kkleey1I248z92qgfv04wGyCOvI6saAN/RJmn0eCV5mmZt2ZGtWtifmP/LNuV/Hr171RuvEMln4ntNIkhtGF2f3Yju91wF2MfMaHYMJlSu7cecevFvw/DcW+iwxXUEkEgeQiKW4ad1QuxQM7MxLbSufmIzkDjFQ3ekX19q1rPcX8H2K1nFxFDHbFZdwUrgyFyCPmPRQT0z1yAHij/kEwf8AYRsf/SqKtmud8WWFnNa2V5LaQPdQ6hZCKdowXjBuos7Wxkde1dFQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRWdqmtWelBEmZpLiTiK2hXdLIfZf69KaTk7IUpKKuzRJwMnpWBNr819M9poEK3cinbJdOSLeI/U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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Reduce', 'type': 'function'}</th>\n",
-       "      <td><img 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JPbkmuxooAw7rQ7q81GzaW+h/s+0nSeKEW7GYsq4G6VnORyc/KCRxnrmDStB1fT5NRmn1Wxurq8JYztYMGBz8in96QUUZAUbepOckk9HRQBy/wDwit55hvf7UgGpm+F6XFofILCEw7TFvyflOc787gD0GKrT/DywuNLjt3u7pbsFC9xDPLErhZzMV8tHCgZZwvUrkYPArsaKAM/VtGsdZ017K9toLiMqdn2iIShGwQGAbuM9etJomi2Wg6ZFZWVtbwqqKJDDCsYkYAAsQO5xWjRQBz2i6Hq2ltfzXGqWV3dXZLm4+wsjbv4Q3705RRwFG31zkkmafSNRk8WW2rpqFqtrDbvb/ZjaMXKuUZj5nmAZzGMfL0J69a26KAMCLQ9S/tGTUp9Rs3vBam2tzHYlI1UsGJdfMJc5UYwy459aqXfhW+uvD+uWP9qWyXetF/tM4sz5aq0QiOyPzMg7VByWPOe3A6qigDl9W8K3Wrq0kt/apdXFi9heSLZEiSJjn92pk+Rhk8ksOeQcCpV8LOurCb7cDpwu/tv2Uw5fzvL2f6zd93PzY253d8cV0dFAHJ6d4Nk06BYVvLSSO3sDp9nHJZZRYiQf3o3/AL0/KBxt/i9aW28Gtp8EUun3drbail01yZEssQHdGIighDjC7VXHzZyuSTkg9XRQByieE723uNIFvqtu1npsQVILmzaTdNzumysijcQSBkELkkdeLT+GfO8SrrMr2IkhZngMVlslLlCgMsm4mQBWbAAXr7CuhooA57RNB1TStPvop9Vtbi7udzm8WyZHMpGN7gyMGA+UBRtACgDim3/hmaTwjbeH9NvYbVIUjjMs9uZiypjsHX5iQCTk9/XNdHRQBzGoeFrzUbiSSXU4VS6ht47xRaHLGJi2YiX/AHeST1346jnmopvA8VzPNFLdqNNea5uBBHBtl8y4R0kzJu5X945A2jnbydtdZRQBzOkeD4LC3lhm+xoG2Yk0u2bT5G25/wBY0cmX69OB7U+HwxNDqSzLqCGzguZ7y1tzb/NHPMHDFm3fMuZZCFwPvdeK6OigDH0rSLm01O91K+u4bi7uoooT9ntzDGqRlyvylmJOZGySegAwMVrLGiM7KiqXO5iBjccAZPrwAPwp1FAHJaJ4b1CO9W41C5Rbe21S9vbe1WIbt0ryqrGQMQVKSE7cAgtyeMV1boskbIwyrAgj2p1FAHHr4Y1eXTNCtTqNpFHpcao0E9q0yTyR4WOU7ZE6BdwU8AnPUDGtDpmpwLfzQXdpDeXd2Zi8kDTIIwoRVwGQ52qp68Enr1raooA5yy8Oy2dxo8ZmEsNi9zdPJjbumlLcKvZQJZMDPACjmuhMaGRZCil1BUNjkA4yM/gPyFOooA57UvDL3mry6pBfCG5/0doN0O9UaIy8sNw3BhMwxkYxnNc9rnhTV7fTrmDSHe5u9Ttri3vJTDGI8yuz7vmkBjAMr4wJOOoJAJ9CooA56LQNRj8Srqb6lazW8cSwwW8lmxaFMDfscSAbmIyWKngAdqy4/Dmt213o8dlPDbrYJexC4kjEq7HdDGCm5STtHY8FfQ89lJKkWNx5PRRyTUe2Wb758tP7oPJ+pp2FcwdK0b+ytStkhzLBYaZFYW5J+ZsH52bsOFj/AFrQ1rSG1nQ7+wlmCvc27xIf4UYqQDjvg81poixrtRQB6CnUX7Bbuc7Lo2q3uu2OrS3tpHFBEu2xltWk8lyP3jK4kUbiCVDFTgZwOTm9pNhLa3eq3c+BJe3fmhQc7UVEjX8wm7/gWO1alFIZgaj/AMffhb/r+P8A6ST1v1gaj/x9+Fv+v4/+kk9b9ABRRRQAUUUUAYS+EtMjmjeASQJGbby4oyAiCAtsAGOnzHP4dKs31uLbUF1iG0u7y5WE2wggaMfKWDE/OyjqB/F+FX/s6f3pf+/rf40fZ0/vS/8Af1v8aAMhtJttd+0XOo6fdW5ubVrKS1uGjOYyc5+RmGef734Uln4YjsnYJqd+9vJzPbSGNkncqFZmOzcC2MkKQM5OOTWx9nT+9L/39b/Gue1XXjp2oTQx2bzWtokMl7ObtlMayOVG1cHfgKWOSvGMZPFADU8D2YjkSXUdSn3WyWsTSSJmFEcOm3CDlWAOWyTjnNSx+D4IXe4j1XUVvnuWuTebojJuaNI2XBTZtIjXjbwRxjAxY1W+m03UdMhS0MtteXHkPMbx1aNirEYXB3fdPcVU/t4/219m+xv9g+2fYPtX2tt3nbN/3Mfcz8ud2d3bHNAFptBFteteQalexiaaKS4gBjMc7jau5spuBIVc7SoOOnWtysbxAWtNOhkhkkV2vbSMneT8r3EasOT3DEVqfZ0/vS/9/W/xoAloqL7On96X/v63+NH2dP70v/f1v8aAJaKi+zp/el/7+t/jR9nT+9L/AN/W/wAaAJaKi+zp/el/7+t/jR9nT+9L/wB/W/xoAloqL7On96X/AL+t/jR9nT+9L/39b/GgCWiovs6f3pf+/rf40fZ0/vS/9/W/xoAloqL7On96X/v63+NH2dP70v8A39b/ABoAloqL7On96X/v63+NH2dP70v/AH9b/GgCWiovs6f3pf8Av63+NH2dP70v/f1v8aAJaKi+zp/el/7+t/jR9nT+9L/39b/GgCWiovs6f3pf+/rf40fZ0/vS/wDf1v8AGgCWiovs6f3pf+/rf40fZ0/vS/8Af1v8aAJaKi+zp/el/wC/rf40fZ0/vS/9/W/xoAloqL7On96X/v63+NH2dP70v/f1v8aAJaKi+zp/el/7+t/jR9nT+9L/AN/W/wAaAJaKi+zp/el/7+t/jR9nT+9L/wB/W/xoAloqL7On96T/AL+t/jWFcaytxO9nocMmoXKna8gmYQQn/afPJ9hk/SqjFy2JlNR3OhZlRSzEADuah8ySb/VDYn99h1+gqjp2lXMSF9Sv5LudjnA+SOP2UdfxJNaH2dP70v8A39b/ABodk+4K7XYWOFI8kZLHqx5JqSovs6f3pf8Av63+NH2dP70v/f1v8akoloqL7On96X/v63+NH2dP70v/AH9b/GgCWiovs6f3pf8Av63+NH2eM9TIf+2jf40AYuo/8ffhb/r+P/pJPW/WLrQC6r4bAGANRcAf9utxW1QAUUUUAFFFFABRRRQAVz2reGZNR1CaaO+WG1u0hjvYDDuMixuWG1tw2ZDFTkNxjGDzXQ0UAYOs6Pq2p/2a0Gp2UD2c4uGMli8gkcAgYAlXaMMe57cimf8ACMyf219p+3L9g+2fb/svk/N52zZ/rN33M/Njbnd3xxXQ0UAc74ssLOa1sryW0ge6h1CyEU7RgvGDdRZ2tjI69q6KsbxR/wAgmD/sI2P/AKVRVs0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRVe9vrXTrVrm8nSCFerOcfh7n2ppN6ITaWrLFZmp67Z6Y6wsXnu5P9XawLvkf8Ow9zgVQ+16xr3Fij6ZYH/l6mT99IP8AYQ/dHu3PtWnpmjWWko32aMmWTmWeQ7pJD6sx5NXyxj8X3GfNKXw7dzM/svU9c+bWpfstmemn2z8sP+mkg6/RcD61u29tBaQJBbQpFCgwqIuAPwqWilKblp0KjBR16hRRRUFhRRRQAUUUUAFFFFAGNrf/ACFvDn/YRf8A9JbitmsbW/8AkLeHP+wi/wD6S3FbNABRRRQAUUUUAFFFFABRRWbe69pun38Vlc3DJPJswBE7Ku9tqF2AKpubIG4jJGBQBpUVm6lr2m6TPFDe3DRvINw2xO4VcgbnKghFyQNzYHvSDxDpjat/ZguH+0+YYh+5fyzIF3lBJjYW287c5wOlAEPij/kEwf8AYRsf/SqKtmsbxR/yCYP+wjY/+lUVbNABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABQSAMk4ArL1PXrTTpVtgHub5x+7tIBukb3I/hHucCvNbHx5F4q8Tw6bq8csWnTyCKGCCTCM5OAJDjLAnHcD2ok4wt7R2vt/X9I3oYWviVN4eHNyK78v67K78jv5vEEl7M9roFut7Kp2vcscW8R92/iPstS2Xh6NLpb7U521C/XlZJRhIvaNOi/Xr71rQwxW0KQwRpHEgwqIuAB7CpK0c7aR0/M5FTu7z1/IKKKKzNAooooAKKKKACiiigAooooAKKKKAMbW/+Qt4c/wCwi/8A6S3FbNY2t/8AIW8Of9hF/wD0luK2aACiiigAooooAKKK4/x3Pc2Q0u+juxFbwXCNJEb5bbzCJY26uyqw2LKu0nHzg444AOwrj9e0jUrnUtSitrNpoNUhtojcCRALby3beWBIY/KwK7Qecg461seGJnudAhuJLqK5M0k0oeK4E6qrSsQgccNsBCcf3a2KAOM8R6Zql++pG102SQavpX9nHdJGPsrZk+d/m5XEpPy7j8nTmoodA1BPGC3n2KZduoPcNdfaFNs0JhKACLdkS9AW2A9fmwdtdxRQBzviy2le1sp1vZ4449Qsg1uqpskzdRcsSpbj2YV0VY3ij/kEwf8AYRsf/SqKtmgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKCQASTgDvWBN4glvpntdAgW8lU7XunOLeI+7fxH2X86qMXLYmU1Hc1r7ULTTbZrm9uEghX+Jz19h6n2FY32jWNf4tFfStPP/LxKv8ApEo/2FP3B7nn2qxY+Ho47pb7UZ21DUB0llGFj9o06L/P3raqrxjtq/6/rUi0p/Fov6/rT7yjpmj2WkxMtpFh3OZJXO6SQ+rMeTWDZfDnw/Ya+NYhim81X8yOFnBijb1AxnjtzXWUVlOKm7z1Z10MRVw8ZRoycVJWduqCiiimYhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGNrf/IW8Of9hF//AEluK2axtb/5C3hz/sIv/wCktxWzQAUUUUAF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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Wait', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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lado66jENMtFtwVa1zO5hYMCJN+1clR/BxzgjjFuTRb+51mK/uNQtttr5xtEitCrIXGPnYud4A7ALk89hW5RQBy83ha+u5bi8udVgOoSPbMjRWhWFfIdmXdGZCWyXOTuHRcYxTj4a1BNHnsIdTtB9sed7x5LEuGMpyfLXzAEAyeu/PU55z01FAHLz+BtOe+sLuGe9iltriOeTF3NibZEYlyocKDjbkgcgEHqasPoepzai+oSapardR2s1vZtFZFRF5hU7nBkPmYKLwNo6+vHQUUAY/hzSLnRNN+yXF1b3Lbi5lit2jaRjyzOWd9zE854+lV4/Buix+I21hdM08SGNAoFogKyB2bzA2PvHI568da6CigDE1vQ7nUbmK6sb9bK5W3ltWd4PNBjk2k4G5cMCgIPIHOQc0XegPJp1paWl1Hb/YHiexZoS/llF24cbhvBUkcbevWtuigDkpPBlwLbbbassVzPFcRXkxttwlE8nmMUXcNhBJ25LAA8hqsL4TMepBorxU0z7TFeNaeTljLGiouJN3C/IjY2k5HXBxXS0UAcpp/hC4sUjV9QtrgW1g9haiSzyPLYqSZhv/en5F6berf3qS28GPYwwy2N3aW99HeG7JSyxbEmLytohDgqAuMfNnOTzkiusooA5aTwndNoVvoaalAuniNUn3WmZXIfcSjbwE9htbb2qZfDEyasJ11BfsUd3JfQ23kfMs7oykl93K5d227QcnrgYro6KAMCHRdVS7u9Qk1OzbUJoYoUZbFlhVUZm5TzSWY7jzuGOMDrms/hOZtJa0Nzp0s011JdTSXOnCVA7d44y/yEdiS3fOc11FFAHJP4JCLHa2l+ItO8m1hkikg8yTbbnKbJNw2575VuckYJq/Ho+qx+Jb/VRqdn5d1bR26Q/Ym3RiMyMhLebgnMrZ+UZAGMVvUUAc2PCa3tzcXWtXf2m4lMJU2XmWax+WH2kbZC27942Tu5G3jioYfCd5Z+GF0Gy1WMWrxyRzvdW7zuwcn7paX5cAnruHt69VRQBlpowXWbW/a4Z0tLM20ETDJBYqWctnkkIo6cc+tZ9t4Ykt9ZutWWbT47145I4GgsPLA3spLS/OTK3yqM5Xv0zXSUUAZWj6VPYTX11d3cdxdXkiySGGExRjaoUYUsxzgckk5+gArTSNIwQiKoJLHaMZJ6mnUUAcl4T8N6hp8Omz6pcoXs7aSGG2SIAx+Yys25wxD42ADAHGc5PNdBrGnJq+iX+mSMUS8tpLdmHYOpXP61dooA5680rWdSuNDvTe2dpJYkTTQPatNukMbI4DiRcDDnHHUA+1VrvwxqN5oy2Q1C2gM15Jc3qPbtMlwrMzCI4dDtAKg+oUA8Eg9VRQBjpp10+v2V7cNEVs7F4d0a7BJJIyFiFySoAiGBk/f9q1hGgkaQIodgFLY5IGcDP4n8zTqKAOcuvDE7atNqlpqCQ3bXYuYvNtzIi/uBCysAylsgZyCMHHXnPP6n4R1S1gGmaS0ssN7DDFeTyRx7QUkZi+TIGXO5sgI46AbTk16HRQBz9toN+niO81O71C1uYbhTEsRtGWSGHHEaP5mAM8k7csfoMYi+Ftab7BardRWsUGjPps1w0QlDjcqgqNykMVXcCcgZwQa7Z5wG2IDI/oO31NN8lpOZ2yP7i9P/AK9OwrmRptg9trV9cwoDGbe3s4PmyPLiDtuJ9d0rDH+yPWpde0mXU9GmiRw90rJNAGOE8yN1dQfYsoBNbAAAwBgUtFwt3MD+xNRbxUdYk1C2ktwgjhtpLRi0CYG8K4kxuY9W2ngAdBzb0Owlsba5a4wJ7q7luHCnIUM3yj6hQoPvmtSikMwNR/4+/C3/AF/H/wBJJ636wNR/4+/C3/X8f/SSet+gAooooAKKKKAMJfCWmRzRvAJIEjNt5cUZARBAW2ADHT5jn8OlWb63FtqC6xDaXd5crCbYQQNGPlLBifnZR1A/i/Cr/wBnT+9L/wB/W/xo+zp/el/7+t/jQBjyaTDr1/perXtpe2Vzp0khihkeP5gwAO7YWBGQrDBHKiqtv4G0+ziRbO9v7eRLmW4WZHQuPMG1k5UgKFCqONwCjmui+zp/el/7+t/jR9nT+9L/AN/W/wAaAOXs/BNnbWq28Ut/CYorOCCYPEXQW2fLblSC3zHOQQewFdFbW80M3zyCQbMF9uCxz1PP9Km+zp/el/7+t/jR9nT+9L/39b/GgBtypKod7AB0+XjB+YVPWN4gLWmnQyQySK7XtpGTvJ+V7iNWHJ7hiK1Ps6f3pf8Av63+NAEtFRfZ0/vS/wDf1v8AGj7On96X/v63+NAEtFRfZ0/vS/8Af1v8aPs6f3pf+/rf40AS0VF9nT+9L/39b/Gj7On96X/v63+NAEtFRfZ0/vS/9/W/xo+zp/el/wC/rf40AS0VF9nT+9L/AN/W/wAaPs6f3pf+/rf40AS0VF9nT+9L/wB/W/xo+zp/el/7+t/jQBLRUX2dP70v/f1v8aPs6f3pf+/rf40AS0VF9nT+9L/39b/Gj7On96X/AL+t/jQBLRUX2dP70v8A39b/ABo+zp/el/7+t/jQBLRUX2dP70v/AH9b/Gj7On96X/v63+NAEtFRfZ0/vS/9/W/xo+zp/el/7+t/jQBLRUX2dP70v/f1v8aPs6f3pf8Av63+NAEtFRfZ0/vS/wDf1v8AGj7On96X/v63+NAEtFRfZ0/vS/8Af1v8aPs6f3pf+/rf40AS0VF9nT+9L/39b/Gj7On96X/v63+NAEtFZ+oXen6Vb+fe3TQoThcysSx9AAck+wrKQazrThrcTaRY5z5krFriQeyEkIPrk+1XGDavsiJTSdt2dDJMkZwclj0Uck0zZLN/rDsT+4p5P1NNisYokADSs2OXMjZb3ODUn2dP70v/AH9b/Gp9Ct9x6IqLtVQB6CnVF9nT+9L/AN/W/wAaPs6f3pf+/rf40hktFRfZ0/vS/wDf1v8AGj7On96X/v63+NAEtFRfZ0/vS/8Af1v8aPs8Z6mQ/wDbRv8AGgDF1H/j78Lf9fx/9JJ636xdaAXVfDYAwBqLgD/t1uK2qACiiigAooooAKKKKACiiigAori/BV/oEN54gstMvdOWIaoDHDbzJj5oIRkAHuwb6kHvmmeF7jQr/wAXX1xoUmnwQwQvA8Nu6CS6feu6V0HOFI2qx5O9uxBIBveKP+QTB/2EbH/0qirZrnfFj3gtbJYoIGtTqFl5sjTlXU/aosbU2EN+LD8a6KgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKxLvxEn2lrHSYG1G9XhljOI4v99+g+nJ9qqMXLYmUlHc2JporeJpZpEjjQZZ3bAA9zWCdavtYJj0C3HkdDqFypEY/3F6uffge9Oh8PSXsq3Wv3AvpVO5LZRtt4j7L/Efds1vAAAAAADoBVe7Hzf4Ee/PyX4/8AydP8P21ncfbLiSS91Ajm6uOWHso6IPYVr0UVEpOTuy4xUVZBRRRSKCiiigAooooAKKKKAMbW/8AkLeHP+wi/wD6S3FbNY2t/wDIW8Of9hF//SW4rZoAKKKKACiiigAooooAKKKKACiiigDG8Uf8gmD/ALCNj/6VRVs1jeKP+QTB/wBhGx/9Koq2aACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKhuru3sbd7i6mSGFBlndsAUJXBu2rJqztT1uy0rYkztJcSf6q2hXfLJ9FH8zxWd/aGq698ulRmxsT1vrhPncf9M4z/wChN+VaOmaJZaVveFGkuJP9bczNvlkPux/l0rTlUfj+4y53L4PvM77Bq2u/Nqkh0+xP/Llbv+8cf9NJB0+i/nW3aWdtYWy29pBHDCnREXAqeilKbenQqMFHXqFFFFQWFFFFABRRRQAUUUUAFFFFABRRRQBja3/yFvDn/YRf/wBJbitmsbW/+Qt4c/7CL/8ApLcVs0AFFFFABRRRQAUUUUAFFeb+Kb290/xReodVhgW9spIIHl1VIFtdwiCOY2YHKskzblUk7wM+npFAHO+FUeCfXrU3N1PHb6lsjNzO0rKpghYjcxJxlmOOgzWT4Yu7l9T0mZ7y5mk1Kyup7yKSZnWORJYwNqk4TaXZMKBnHOSK6a08O6HYTNLZ6Np9tI7B2eG1RCWHQkgdasW+m2FpdT3NtZW0NxcHM0scSq8p9WIGT+NAGP4suZUtbKBbKeSOTULItcKybI8XUXDAsG59lNdFWN4o/wCQTB/2EbH/ANKoq2aACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAorP1PWbLSVT7Q7NNJxFBEu+SQ+iqOT/Ks37Bquv/NqjtYWB6WUD/vJB/00cdP91fzq1C6u9EZynZ2WrJrrxCZbl7HRYPt94pw7BsQwn/bf19hk0Wvh4SXCXus3H9oXinKBlxDCf9hOn4nJrWtbS3sbdLe1hSGFBhURcAVNTc7aQ0/MFC+s9fyCuTsviN4fv9fGjwyzeaz+XHMyARSN6A5zz24rrK8/034U6dpviWPVFvZXghlE0NsUxtYHIy2eQD7Vy1XVTXs16nq4GOBcKn1ttO3u27/1by3PQKKKK2POCiiigAooooAKKKKACiiigAooooAKKKKAMbW/+Qt4c/7CL/8ApLcVs1ja3/yFvDn/AGEX/wDSW4rZoAKKKKACiiigAooooA8/vrqez8V61tuZrJJJYmAXQp7sS/uUG/zFGM8bdo6bc9Sa9Arz26tNSu59SVPDWvCZGEVlKdaeOIgRIFaQLcgkbs5KjJHX5s16FQAUVy+l+IdQu9UtTcJbfYL+S5jtljRhJGYmIBdixDblVjwBt4HPWoLXxTfzeMP7LeOEW7XUtvt+zuCAkZYMJifLdjgZjA3AEk/dNAGt4o/5BMH/AGEbH/0qirZrnfFl/Zw2tlZy3cCXU2oWRigaQB5A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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Waitall', 'type': 'function'}</th>\n",
@@ -1675,127 +1675,127 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'lulesh.cycle', 'type': 'function'}</th>\n",
-       "      <td><img 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+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'LagrangeLeapFrog', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'CalcTimeConstraintsForElems', 'type': 'function'}</th>\n",
-       "      <td><img 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+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'LagrangeElements', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'ApplyMaterialPropertiesForElems', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'EvalEOSForElems', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'CalcEnergyForElems', 'type': 'function'}</th>\n",
-       "      <td><img 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+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'CalcLagrangeElements', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'CalcKinematicsForElems', 'type': 'function'}</th>\n",
-       "      <td><img 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9kuidUge9VdzEgAPJwwyPmzzySMmotU8f2lmmswW1s0uoaday3AhM0LeYIyAx+WQlcFlOGCnB4BIIq9J4pS3u5oJ7K7W4CW/l2YSMys8rSgLuEhQnEZPUAAZyc8AFa78E29zZ6bp+60NjZwpCTLZiS5YA/Ntl3AJuxg4U9TjHaxB4Xmi1mK6fUEe0gvZr6KH7PiXzJFdSGk3cqA7YG0HhRnAqwviVDrFvpR068S8khWaSNnhBhVmZQW/efN9w52bsceorG0Xxfdz6eb2/BmMkghhtLbTJYWaQuVULLI+x+ASSMAdSRQBdsvCkzwX0GtX0N5Fc3YvFFnFLaNHIMchllJwNqkcjBHftXtvBUum29uunapiaF7slr2N7lGW4cORtaTO4bVGc8/MSPmNX7bxbb3l9aWVvp9+9xP5hkTbGPs/lyCOTzCXxwWH3d2RyM1haR4v1a78K22sTvA8s6WbGL+yri3RDNJGrYkdyJAA5wV9j0oA6Kz8OR2c+ikXLyQaTZm2hjdclmKovmFs9QqkdP42p/iTw5Y+JdJnsry3tnkeJ0hmmgWUwlhjcueh6dCOlZT/ABG0Jbi+iV5JDaRXEhMbxMZPJBLgKH3A/KcbgoOOD0qwnjO3N0YJdL1KApNDFK0qR7Y/OIWJjhzwzHHGSP4gtAG7Z2Fpp1t9nsLWC1hBJEcEYRQT3wMCsnQ9H1XS0vhcalZXDXNz9oBjsmjCFj84wZWzkDA6YPJ3dKsaLr8OuNcG2tp0hhdo/OkaPDsrFSAFcspyOjBTWtQBy0ngLSX1YXYe7Fu1tdwTWpvJykn2hkLkfvMKPlbIA5LA8FRTE8DxWr3s1lqFyk811DdQNcyy3AhaNVXBDyEsCAwPIOGxngV1lFAHPJ4YP9n2lrNe+Zs1D+0LsiLAuH3tIABn5QHKEdeEA5zmrGo6RfalfwGW/gXT4biO4WFLY+duTBA8zfjBI5+XOCRnmtmigDk5vA9tfXGpy35s/wDTbaa1/wBDsxAdsuNzOdzeY/yrg8DrxzVu10PVbN7u6i1Sy+33LRh3+wMIQiKQAIxKDu5+9u7AYwK6GigDj7n4e2F1pKW0l3dLdgKGuYp5YlYCYykeWjhcZZgOu0Ec8CrreDNPXXrPVbea8ieCaSeSM3czLKzLtztL7R78c9DXR0UAZFtpFxZRanJBdxG9vrprgzSwFlXhVRSoYEhUVV+8MkE8ZxVI+FpJtG1Kxub5Gk1O6We8eODYjL8ivGqbjtDJHtJJPLE89K6SigDB1bRNR1DXLG9i1G1S1tBuS0ntGlHm5P7zIkXkLwMg45PXGJJ9Hv7vWLW8uNQgNvZyvNbRR2pVwzIyAO5c7gA54CrkgelbVFAGNpej3Vtqtzqd/eQXFzNBHbgW1sYEVELEZBdiTlzznp0HXOusaKzMqKGc5YgcscY5/CnUUAclpXhvUBqb3F9colpFqtxfQWwiBdiwZVJcN93Dk7duc98cV1tFFAHLQ+HtUHhO20FL21t7eK2axk8y2MzPEPkRlO9QrFADyGGT7czxeFLeeO4XUWlkZrtZ4XhupI3QJGIk+dCrZ2gkjpljXRUUAcvpnhZtLTw/ZJO81rpZlnaWR2LNKUaNQAxJA2ySHrxgetdMY0MiyFFLqCobHIBxkZ/AfkKdRQBz+p+GXvtXm1OG9ENwUtRDuh3qjQvM2WG4bgwmIxxjGc+nP614W1e1sZ10l3ur7Ube5gu5RDGI8ysWBw0qmMAswBAk46gnGfQKKAOcg8PajF4gg1F9StZba3hSCC3ks2LQrtAcq4kA3MR94qcDA9c5q+Hdat7vSUsZoYFsXvQtxJGJU8uRgYwU3KScHHB6rnocHspJUiGWOPQdzUeJZuuYo/QfeP8AhTsK5g6Xov8AZWpWaREzQ6fpq2UBJG5yWBdmxwP9XH+ZrQ1jSDrWiX+nzy7ftVvJCCvRCykZ98ZrTSNY12ooAp1F+wW7nP3mlavqN9ot+LyztDZfvJbd7VpiXZCrgOJFAGGOPlPIzz0q5pVhLbX2rXk+A95dB0UHO1FRUX89pb/gWO1alFIZgaj/AMffhb/r+P8A6ST1v1gaj/x9+Fv+v4/+kk9b9ABRRRQAUUUUAMmhjuYJIJkDxSKUdD0ZSMEVzOg/D/Q/DuqPqNms7zkEJ5zhhED128Dtxzmuk+zp/el/7+t/jR9nT+9L/wB/W/xqXCMmm1qjenia1KEqcJNRluu5ISFBJ6DmsvVLK11SC3kM9xbzQSGWC5t1/eRMAVJwVIxgkEMCOauyxLHE7qk8jKpIRJTlj6DLAZPuQK4wT+Km0mG3/sPVIp5Lybz5hcWxeKDzGZNi+dtLFSq89MEnPGaMDQn8JWk92JLjWtUleSS3lmyYgJmik3xlisYwM8YGAR2zzVmHw3aQ3Qex1G+tbhZLh3ki2HzBLM0rodyFSA7HGBkDv1zVvLfXTevDaW04tro2zxzGZMWgVh5quN2WO0cbdwyeoAzT9NtdcOqQW95FMltbPcNJdGZStyrMfKCgHcCFPzZC8r/FnNAFibwrYxzpdxS3EdyLtpjKjAFlkcF424wUzg4PIIBBzRB4O0+3082STXRjMVpFksucW5BT+HqcDP6YpPElvPBFaXdtqN5Cq3trE8CspSVXuI0bduUsOGPQit37On96X/v63+NAGK/hSB7O/sBqN+mn3sc8bWimPZH5ud5UlN3VmIBYgZ6YwKnufDlndS3MjyTg3ElrI+1hwYHDpjjuRz7dMVp/Z0/vS/8Af1v8aPs6f3pf+/rf40Ac3/wgenMJkkvb+SB7a5tUgZ0CxRzsGfbhAc5UYLEn61am8KQXEr3E2oXz3rLCFusxh0aIyFWACBc/vWBBUgjt1ztfZ0/vS/8Af1v8aPs6f3pf+/rf40AZF14aS+nsZL3U724FpIkqo6wgPIjbgxIjDA5AyFKggYxgnLz4btBolvpkc9zGttIJYJ1ZfMjcMWDDI2nqRgggg4IrU+zp/el/7+t/jR9nT+9L/wB/W/xoAzNN8OWmmXcd3HNcSTrHKjvKwJkMsgkdmwBzuHbAA4A6VUs/CEdrosGkPq+o3NjbpAkMcohGwQsrJgrGCfuAHOeM9+a3vs6f3pf+/rf40fZ0/vS/9/W/xoAxX8KQPZ39gNRv00+9jnja0Ux7I/NzvKkpu6sxALEDPTGBU9z4cs7qW5keScG4ktZH2sODA4dMcdyOfbpitP7On96X/v63+NH2dP70v/f1v8aAM3T/AA/DYatcam13cXN1PGIi0yxrtQEkD5EXPXq2T79c69RfZ0/vS/8Af1v8aPs6f3pf+/rf40AS0VF9nT+9L/39b/Gj7On96X/v63+NAEtFRfZ0/vS/9/W/xo+zp/el/wC/rf40AS0VF9nT+9L/AN/W/wAaPs6f3pf+/rf40AS0VF9nT+9L/wB/W/xo+zp/el/7+t/jQBLRUX2dP70v/f1v8aPs6f3pf+/rf40AS0VF9nT+9L/39b/Gj7On96X/AL+t/jQBLRUX2dP70v8A39b/ABo+zp/el/7+t/jQBLRUX2dP70v/AH9b/Gj7On96X/v63+NAEtFRfZ0/vS/9/W/xo+zp/el/7+t/jQBLRUX2dP70v/f1v8ax9Q1i1tbk2NnHcX+o4/49oZW+X3ds4QfX8qqMXJ2RMpKKuzcJAGScAVD5ry8Qjj++3T8PWszTtL1Bna41a9Ls33bWFiIo/wAT8zH3yB7Vq/Z4x3k/7+t/jQ0k+4Jtq+wscKodxJZz1ZutSVF9nT+9L/39b/Gj7On96X/v63+NSUS0VF9nT+9L/wB/W/xo+zp/el/7+t/jQBLRUX2dP70v/f1v8aPs8Z6mQ/8AbRv8aAMXUf8Aj78Lf9fx/wDSSet+sXWgF1Xw2AMAai4A/wC3W4raoAKKKKACiiigAooooAKKKKACiiigDG8Uf8gmD/sI2P8A6VRVs1jeKP8AkEwf9hGx/wDSqKtmgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKRmCqWYgADJJ7UALVTUdUs9Kt/PvZ1iQnCg8s59FA5J9hWVJr1xqUrW3h+FbjB2vey5EEf0PVz7Dj3qzp3h+C0uPtt3K99qJHNzP1X2ReiD2FacijrP7uv/AADLnctIff0/4JUxrOv9fN0jTj2H/HzKP5Rj8z9K19P0yz0q2FvZQLFHnJx1Y+pPUn3NW6KUptqy0RUYJO71YUUUVBYUUUUAFFFFABRRRQBja3/yFvDn/YRf/wBJbitmsbW/+Qt4c/7CL/8ApLcVs0AFFFFABRRRQAUUUUAMmaRIJGhjEkoUlEZtoZscAnBx9a5iDxXf3dnZrbaRFJqVwbotbG7IRVt5PLciTZk5YpgbR97nGK6eUSGJxEypIVOxnXcAexIyMj2yPrXJWnhLV7OGF49ctFvYXuvLnXT2C+XcSeZIpUynJDhSDnjbgg0ATJ40W5MN1aWPm6WRa+dctNtdDcbdmE2ncAHQsdwwG4zirOmeJpNQ1KGGSxWG0uvP+xzibc0nlNtO5do25+8uC2QOcHiqqeC1tjDa2l95WlgWvnWzQ7nc2+3Zh9w2ghEDDacheMZq1pnhmTT9ShmkvlmtLXz/ALHAIdrR+a247m3Hdj7q4C4B5yeaALHij/kEwf8AYRsf/SqKtmud8WWFnNa2V5LaQPdQ6hZCKdowXjBuos7Wxkde1dFQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUVDdXdvY2z3F1MkMKDLO7YArw/RNf8AGGr+M47f7ffRm4kAmjXOyKInlgrcLheQcelZVKqpuN1u7HoYHL5YyNSUZJcivr1/q257Fqeu2umyLbAPc30g/d2kA3SN7n+6Pc4FUV0a+1phLr8oW3zldOgY+WP+ujdXPtwPrWnpmj2WkxstrEd7nMkzndJIfVmPJq/XTzqPwff/AFseVyOX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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'CalcQForElems', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'CalcMonotonicQForElems', 'type': 'function'}</th>\n",
-       "      <td><img 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+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Irecv', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Isend', 'type': 'function'}</th>\n",
-       "      <td><img 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+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Wait', 'type': 'function'}</th>\n",
-       "      <td><img 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+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Waitall', 'type': 'function'}</th>\n",
-       "      <td><img 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gj+zra0+wX4+12091Ik15G9yuydiWXa0mc428huSCSPmNXfDOvXWtFxcxwrts7SceWCPmlQsw5J4yOP610NAHK23hS9sr7SWh1SCSx0u2jt7e3ubRnZdq7WkDCRRvZeMlTtGcDk5s3nhy41a9d9VvoZbUQzwRw29sYm2SjaQ7l23YX0CjPPYV0NFAHLQeFLmO2vBNd6fcz3FvBagS6dmDyoixUNF5nzMd7c7gOBgDHL18L3droH9kaff2kMMwm+1GSx3hzISTsUOoQDJABDADHXHPTUUAc/JoN+uuaVeW+pW62mn27W4hmtWkkkVtm4mTzAM/uxg7TjJzmrHiPw3p/iXTmtry2tnkxiKaaBZTFkgnbnpnA6GtiigDOm0PTpNCudGitYraxuIZIWit0EahXBDYAGAeTVXTNHv7O5uL271GG6vp1hheUWxRfJjLEKF3n5iXc7s4y3TAxW3RQBzl34Yl1a7nfVr2GaBrS4s0jtrYxERzYDbmLtuOFHQKM5OOmIoPBkU0rzatdG4mxAsbWXmWYjEQkCkbJM7sSuCc4xgYGK6iigDj28Cr/wjVjoa3VvJDAjq813am4lG7+KJnc+UwGcH5h044q3P4J01tX0/UbaS6t3tbsXTxpdTbJSIWiA2b9q8FecchSp4Y10tFAHJS+CEuptRnnntIp7q3kt0eyshDgOyszSfM3mMSiZJwMAjAyau22iataG8uYtUsvt95MskztYN5O1UCACMSgg8Alix9MdMdBRQByf/AAiV/CdIjt9Wt2tNOTIgubMyK82SfM+WRQMA4VcEL15IGNK50i/vNYtbq41C3+zWczT28UdqVfcUZAHfecrhzwFXPHPrtUUAc7omh6vpNpeiXVbK6vbljIbo2LqzSf3nHmncAMAKNuAAKrweFr6PwpZ6JPeaVdm1KqslzpZkQoq4H7sy/fB53Zx7V1VFAHIXHgSL7MtrZ3iwwSWEWn3Jkg8ySSKNmYFG3DY+XfJweSDjgVaTwo0WsC6ivIktUvZNQSEW/wC88942QkybuV+YnG0HoM44rpaKAMbS9HurbVbnU7+8guLmaCO3AtrYwIqIWIyC7EnLnnPToOuddY0VmZUUM5yxA5Y4xz+FOooA5LSvDeoDU3uL65RLSLVbi+gthEC7FgyqS4b7uHJ27c5744rraKKAOS/4RrVG8K6To0F9a2h06SLDy2zT+YsEimI8SJjIRc9epHHWrv8AwjMd895JqzGSSa7WeNrWeSExhIwi4ZWDD+NsZPLnrXQUUAcvpvhVtLTQbKOd5rbTZprppZZGZmkZHRVwxJxiVz1/hHrXTGNDIshRS6gqGxyAcZGfwH5CnUUAc/qnhltQ1SbUYr0QzmO1EOYt6o8MkjgkbhuDeaQRx06+mDrHhjV7O1lfSne71C+huYbqRYYxGfNYMCA0qlMHgEeZxnIY4rvSwUZYgD1NQ+eX4hQv/tHhaLCuYFt4c1KDWrO+fU7WW3tLdIIbeSzZjEAoDsriQDc394qcDj1zmPourW15pcWnyxIbK5vHWZo/OXy5SWQMm5T/ABbeCOVz0OK7LyGk/wBdISP7q8CplRUXCqAPQU9A1Oa0nw7cWN/YZl222n6cbSJ8gvIzsrSE9h/qk6ep9K1dV0iHVNFv9OYlReW0lu0h5IDqVJ/WtGii4WOal0bWb/VNM1K4vrOMW0Kk2Ulq0qpMR87qwkXLYJUEg4GfU1o6ZYS2+patez4DXc6mNQc4jRFQZ9yQx+jAdq1KKQzA1H/j78Lf9fx/9JJ636wNR/4+/C3/AF/H/wBJJ636ACiiigAooooAZNDHcwSQTIHikUo6HoykYIrmdB+H+h+HdUfUbNZ3nIITznDCIHrt4HbjnNdJ9nT+9L/39b/Gj7On96X/AL+t/jUuEZNNrVG9PE1qUJU4SajLddyWqOoaVbanLZPc7iLSZplQY2uWikiIYEcjbK3pzj6VZ+zp/el/7+t/jR9nT+9L/wB/W/xqjAx18JaZHNG8AkgSM23lxRkBEEBbYAMdPmOfw6VZvrcW2oLrENpd3lysJthBA0Y+UsGJ+dlHUD+L8Kv/AGdP70v/AH9b/Gj7On96X/v63+NAGHeaVDrt1pWr3tpe2Vzp05MUMjx/MGKg7thYEZCsMEcqKht/A2n2cSLZ3t/byJcy3CzI6Fx5g2snKkBQoVRxkBRzV7xAWtNOhkhkkV2vbSMneT8r3EasOT3DEVqfZ0/vS/8Af1v8aAOfi8Eafb2MFtBd3sRgt7O3imDIXQWxby25UgsdxzkEH0FOXwbaDUUuzf37BJ5blYC0YjEkkbI7cJk5Dk8ng9MDit77On96X/v63+NH2dP70v8A39b/ABoAoaRoNropY20kzboIYD5hB+WJSqngDnB5/pWpUX2dP70v/f1v8aPs6f3pf+/rf40AS0VF9nT+9L/39b/Gj7On96X/AL+t/jQBLRUX2dP70v8A39b/ABo+zp/el/7+t/jQBLRUX2dP70v/AH9b/Gj7On96X/v63+NAEtFRfZ0/vS/9/W/xo+zp/el/7+t/jQBLRUX2dP70v/f1v8aPs6f3pf8Av63+NAEtFRfZ0/vS/wDf1v8AGj7On96X/v63+NAEtFRfZ0/vS/8Af1v8aPs6f3pf+/rf40AS0VF9nT+9L/39b/Gj7On96X/v63+NAEtFRfZ0/vS/9/W/xo+zp/el/wC/rf40AS0VF9nT+9L/AN/W/wAaPs6f3pf+/rf40AS0VF9nT+9L/wB/W/xo+zp/el/7+t/jQBLRUX2dP70v/f1v8aPs6f3pf+/rf40AS0Vk6lqmmaUVS5uZPPf7kEbu8j/RQc1QCa7qxBhVtHtT/HK5kuGH+7nav45NWqbau9EQ6iTstWdG8iRjLsAKi8yWT/VptX+8/wDhUVvp0cKKHlnmkA5lkkO4/lgfkKn+zp/el/7+t/jU6IrViC3XO6QmRv8Aa6D8KmqL7On96X/v63+NH2dP70v/AH9b/GkOxLRUX2dP70v/AH9b/Gj7On96X/v63+NAEtFRfZ0/vS/9/W/xo+zp/el/7+t/jQBLRUX2dP70v/f1v8aPs8Z6mQ/9tG/xoAxdR/4+/C3/AF/H/wBJJ636xdaAXVfDYAwBqLgD/t1uK2qACiiigAooooAKKKKACsHxlPe2nhPUb3T76SzuLSB51dI0fdtUnaQ6kYJxnjP0reqjq+kWeuafJYX6zPbScOkU8kW4YwQSjAkc9M4oAxLq91Ky8WFr2bUItIkkhhtjEluYC7DG18gyjLkAEYGcc1BoOr6lcalpstzeNNBqkNzKLcxoBbeW67ApADH5WIbcTzgjHStpPDemrcwTsLuZ4CrRie+mlQMowG2u5UsP7xGc85zUlloOm6ffy3ttbsk8m/JMrsq723OEUkqm5gCdoGSMmgDP8WXsUVrZWrJOZJdQsirLbuyDF1F95wNq/iRXRVjeKP8AkEwf9hGx/wDSqKtmgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArxPQNA8YwfESK5uYbtWW43XN0wPlyR5+YBuhBHQfT0r1jUfEFjp0wtiz3F433bW2XfIfwHQe5wKpfZdc1nm9n/ALKtD/y72zbpmH+1J0X/AID+dTUw3tOWUnZI7cFmssHGpTpxUudWd+n9di7qOv2Gmyi3d2nu2+7a26+ZK3/AR0+pwKpeTr+sf6+UaPaH/lnCQ9ww93+6n4ZPvWpp2k2GkxGOytkiDcsw5Zz6sx5J+tXa25ox+FfeebySl8T+S/r/ACM/TdF0/SQxtLcLI/35mJaR/wDeY8mtCiiobbd2aKKirIKKKKQwooooAKKKKACiiigAooooAxtb/wCQt4c/7CL/APpLcVs1ja3/AMhbw5/2EX/9JbitmgAooooAKKKKACiiigAoorJ8S6tc6HoVzqdtZR3f2ZGlkjefyvkUEkg7WyeOBjv1FAGtRWNqGsXmnanaxy2ELWNxcJbJMtyfNLsM58vZjaCDn5s4BOOKg8O+JH165vU8uwjS2mlhKRX3mzgpIyAvHsGzO0kcnqKAJ/FH/IJg/wCwjY/+lUVbNY3ij/kEwf8AYRsf/SqKtmgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKo6lrFjpMateThGc4jjUbnkPoqjk1mZ13XOm7R7E9zhrlx/KP9TVqDau9ERKok7LVl7UtesdLdYZXaW6f/AFdrAu+V/oo/mcCqP2fXNa5upTpNmf8AlhbsGncf7T9F+i8+9aWm6NY6SjC0gCu/MkrHdJIfVmPJq/T5ox+FfNk8kpfE/kinp2k2OkwmKytkiB5Zhyzn1ZjyT9auUUVDbbuzRJJWQUUUUhhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGNrf8AyFvDn/YRf/0luK2axtb/AOQt4c/7CL/+ktxWzQAUUUUAFFFFABRRVTVI7iXSL2OznWC6eB1hmbpG5U7WP0ODQBbrJ8SaVc63oF3plrdw2puo2ieWWAy4RgQcAOvPPXP4VzHgjUZLzUVtvt0RS0juswnVUu5JQ9wGjYhXY4RPl3Nzl8V3tAHOR6FrD+IoNVvNWsbhYYxGkH2B1Eef9YyHzjhm6ZIbA49c2bbRrz+3o9Uvr23maCGSCCO3tTFhXZWO8l23H5F6bR1OOmNqigDnfFlhZzWtleS2kD3UOoWQinaMF4wbqLO1sZHXtXRVjeKP+QTB/wBhGx/9Koq2aACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKCQBknAFYE3iGS9me10C3W9lU7XuWOLeI+7fxH2WqjFy2JlNR3Ni7vLawtmuLueOGFOru2BWJ/aOra38ukwmxsz/y/XSfMw9Y4z/NsfSp7Tw6n2lb3VZ21G9XlWkGI4v8AcToPryfetuqvGO2r/D+v6sRaU99F+P8AXp95l6boNlpsjXAD3F44+e6u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/></td>\n",
+       "      <td><img 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cEkED5RgCo7bwR/Z1tafYL8fa7ae6kSa8je5XZOxLLtaTOcbeQ3JBJHzGrvhnXrrWi4uY4V22dpOPLBHzSoWYck8ZHH9a6GgDlbbwpe2V9pLQ6pBJY6XbR29vb3Nozsu1drSBhIo3svGSp2jOByc2bzw5cateu+q30MtqIZ4I4be2MTbJRtIdy7bsL6BRnnsK6GigDloPClzHbXgmu9PuZ7i3gtQJdOzB5URYqGi8z5mO9udwHAwBjmGTwZcpp1hYWWo2kVtBO9zcQS2JaO4kZty/Ksi7UUk4TJHC5zjnr6KAMTUNH1DU7m3WfULcWUM8NwYo7UiUvGwYYkLkBSy8jbnBIzzmn+I/Den+JdOa2vLa2eTGIppoFlMWSCduemcDoa2KKAM6bQ9Ok0K50aK1itrG4hkhaK3QRqFcENgAYB5NVdM0e/s7m4vbvUYbq+nWGF5RbFF8mMsQoXefmJdzuzjLdMDFbdFAHOXfhiXVrud9WvYZoGtLizSO2tjERHNgNuYu244UdAozk46Yig8GRTSvNq10bibECxtZeZZiMRCQKRskzuxK4JzjGBgYrqKKAOPbwKv/CNWOhrdW8kMCOrzXdqbiUbv4omdz5TAZwfmHTjirc/gnTW1fT9RtpLq3e1uxdPGl1NslIhaIDZv2rwV5xyFKnhjXS0UAclL4IS6m1Geee0inureS3R7KyEOA7KzNJ8zeYxKJknAwCMDJq7baJq1oby5i1Sy+33kyyTO1g3k7VQIAIxKCDwCWLH0x0x0FFAHJ/8ACJX8J0iO31a3a005MiC5szIrzZJ8z5ZFAwDhVwQvXkgY0rnSL+81i1urjULf7NZzNPbxR2pV9xRkAd95yuHPAVc8c+u1RQBzuiaHq+k2l6JdVsrq9uWMhujYurNJ/eceadwAwAo24AAqvB4Wvo/Clnok95pV2bUqqyXOlmRCirgfuzL98HndnHtXVUUAchceBIvsy2tneLDBJYRafcmSDzJJIo2ZgUbcNj5d8nB5IOOBVpfCjxawLqG8iS2S9k1BIfs3z+e8bISX3cr8xOMA9BuxxXS0UAYXhvRb/Rorhb7ULa+lnfzZJ0tWikkc9WcmRgeMAAAAAADittY0VmZUUM5yxA5Y4xz+FOooA5LSvDeoDU3uL65RLSLVbi+gthEC7FgyqS4b7uHJ27c5744rraKKAOS/4RrVG8K6To0F9a2h06SLDy2zT+YsEimI8SJjIRc9epHHWrv/AAjMd895JqzGSSa7WeNrWeSExhIwi4ZWDD+NsZPLnrXQUUAcvpvhVtLTQbKOd5rbTZprppZZGZmkZHRVwxJxiVz1/hHrXTGNDIshRS6gqGxyAcZGfwH5CnUUAc/qnhltQ1SbUYr0QzmO1EOYt6o8MkjgkbhuDeaQRx06+mDrHhjV7O1lfSne71C+huYbqRYYxGfNYMCA0qlMHgEeZxnIY4rvSwUZYgD1NQ+eX4hQv/tHhaLCuYFt4c1KDWrO+fU7WW3tLdIIbeSzZjEAoDsriQDc394qcDj1zmPourW15pcWnyxIbK5vHWZo/OXy5SWQMm5T/Ft4I5XPQ4rsvIaT/XSEj+6vAqZUVFwqgD0FPQNTmtJ8O3Fjf2GZdttp+nG0ifILyM7K0hPYf6pOnqfStXVdIh1TRb/TmJUXltJbtIeSA6lSf1rRoouFjmpdG1m/1TTNSuL6zjFtCpNlJatKqTEfO6sJFy2CVBIOBn1NaOmWEtvqWrXs+A13OpjUHOI0RUGfckMfowHatSikMwNR/wCPvwt/1/H/ANJJ636wNR/4+/C3/X8f/SSet+gAooooAKKKKAGTQx3MEkEyB4pFKOh6MpGCK5nQfh/ofh3VH1GzWd5yCE85wwiB67eB245zXSfZ0/vS/wDf1v8AGj7On96X/v63+NS4Rk02tUb08TWpQlThJqMt13Jao6hpVtqctk9zuItJmmVBja5aKSIhgRyNsrenOPpVn7On96X/AL+t/jR9nT+9L/39b/GqMDHXwlpkc0bwCSBIzbeXFGQEQQFtgAx0+Y5/DpVm+txbagusQ2l3eXKwm2EEDRj5SwYn52UdQP4vwq/9nT+9L/39b/Gj7On96X/v63+NAGHeaVDrt1pWr3tpe2Vzp05MUMjx/MGKg7thYEZCsMEcqKht/A2n2cSLZ3t/byJcy3CzI6Fx5g2snKkBQoVRxkBRzV7xAWtNOhkhkkV2vbSMneT8r3EasOT3DEVqfZ0/vS/9/W/xoA5+LwRp9vYwW0F3exGC3s7eKYMhdBbFvLblSCx3HOQQfQU5fBtoNRS7N/fsEnluVgLRiMSSRsjtwmTkOTyeD0wOK3vs6f3pf+/rf40fZ0/vS/8Af1v8aAKGkaDa6KWNtJM26CGA+YQfliUqp4A5wef6VqVF9nT+9L/39b/Gj7On96X/AL+t/jQBLRUX2dP70v8A39b/ABo+zp/el/7+t/jQBLRUX2dP70v/AH9b/Gj7On96X/v63+NAEtFRfZ0/vS/9/W/xo+zp/el/7+t/jQBLRUX2dP70v/f1v8aPs6f3pf8Av63+NAEtFRfZ0/vS/wDf1v8AGj7On96X/v63+NAEtFRfZ0/vS/8Af1v8aPs6f3pf+/rf40AS0VF9nT+9L/39b/Gj7On96X/v63+NAEtFRfZ0/vS/9/W/xo+zp/el/wC/rf40AS0VF9nT+9L/AN/W/wAaPs6f3pf+/rf40AS0VF9nT+9L/wB/W/xo+zp/el/7+t/jQBLRUX2dP70v/f1v8aPs6f3pf+/rf40AS0VF9nT+9L/39b/Gj7On96X/AL+t/jQBLRWTqWqaZpRVLm5k89/uQRu7yP8ARQc1QCa7qxBhVtHtT/HK5kuGH+7nav45NWqbau9EQ6iTstWdG8iRjLsAKi8yWT/VptX+8/8AhUVvp0cKKHlnmkA5lkkO4/lgfkKn+zp/el/7+t/jU6IrViC3XO6QmRv9roPwqaovs6f3pf8Av63+NH2dP70v/f1v8aQ7EtFRfZ0/vS/9/W/xo+zp/el/7+t/jQBLRUX2dP70v/f1v8aPs6f3pf8Av63+NAEtFRfZ0/vS/wDf1v8AGj7PGepkP/bRv8aAMXUf+Pvwt/1/H/0knrfrF1oBdV8NgDAGouAP+3W4raoAKKKKACiiigAooooAKwfGU97aeE9RvdPvpLO4tIHnV0jR921SdpDqRgnGeM/St6qOr6RZ65p8lhfrM9tJw6RTyRbhjBBKMCRz0zigDEur3UrLxYWvZtQi0iSSGG2MSW5gLsMbXyDKMuQARgZxzUGg6vqVxqWmy3N400GqQ3MotzGgFt5brsCkAMflYhtxPOCMdK2k8N6atzBOwu5ngKtGJ76aVAyjAba7lSw/vEZzznNSWWg6bp9/Le21uyTyb8kyuyrvbc4RSSqbmAJ2gZIyaAM/xZexRWtlask5kl1CyKstu7IMXUX3nA2r+JFdFWN4o/5BMH/YRsf/AEqirZoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8T0DQPGMHxEiubmG7VluN1zdMD5ckefmAboQR0H09K9Y1HxBY6dMLYs9xeN921tl3yH8B0HucCqX2XXNZ5vZ/7KtD/y72zbpmH+1J0X/gP51NTDe05ZSdkjtwWaywcalOnFS51Z36f12Luo6/YabKLd3ae7b7trbr5krf8AAR0+pwKpeTr+sf6+UaPaH/lnCQ9ww93+6n4ZPvWpp2k2GkxGOytkiDcsw5Zz6sx5J+tXa25ox+FfeebySl8T+S/r/Iz9N0XT9JDG0twsj/fmYlpH/wB5jya0KKKhtt3ZooqKsgooopDCiiigAooooAKKKKACiiigDG1v/kLeHP8AsIv/AOktxWzWNrf/ACFvDn/YRf8A9JbitmgAooooAKKKKACiiigAoorJ8S6tc6HoVzqdtZR3f2ZGlkjefyvkUEkg7WyeOBjv1FAGtRWNqGsXmnanaxy2ELWNxcJbJMtyfNLsM58vZjaCDn5s4BOOKg8O+JH165vU8uwjS2mlhKRX3mzgpIyAvHsGzO0kcnqKAJ/FH/IJg/7CNj/6VRVs1jeKP+QTB/2EbH/0qirZoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiqOpaxY6TGrXk4RnOI41G55D6Ko5NZmdd1zpu0exPc4a5cfyj/U1ag2rvRESqJOy1Ze1LXrHS3WGV2lun/1drAu+V/oo/mcCqP2fXNa5upTpNmf+WFuwadx/tP0X6Lz71pabo1jpKMLSAK78ySsd0kh9WY8mr9PmjH4V82TySl8T+SKenaTY6TCYrK2SIHlmHLOfVmPJP1q5RRUNtu7NEklZBRRRSGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAY2t/8AIW8Of9hF/wD0luK2axtb/wCQt4c/7CL/APpLcVs0AFFFFABRRRQAUUVU1SO4l0i9js51gungdYZm6RuVO1j9Dg0AW6yfEmlXOt6Bd6Za3cNqbqNonllgMuEYEHADrzz1z+Fcx4I1GS81Fbb7dEUtI7rMJ1VLuSUPcBo2IV2OET5dzc5fFd7QBzkehaw/iKDVbzVrG4WGMRpB9gdRHn/WMh844ZumSGwOPXNm20a8/t6PVL69t5mghkggjt7UxYV2VjvJdtx+Rem0dTjpjaooA53xZYWc1rZXktpA91DqFkIp2jBeMG6iztbGR17V0VY3ij/kEwf9hGx/9Koq2aACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKCQBknAFYE3iGS9me10C3W9lU7XuWOLeI+7fxH2WqjFy2JlNR3Ni7vLawtmuLueOGFOru2BWJ/aOra38ukwmxsz/y/XSfMw9Y4z/NsfSp7Tw6n2lb3VZ21G9XlWkGI4v9xOg+vJ9626q8Y7av8P6/qxFpT30X4/16feZem6DZabI1wA9xeOPnurht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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'LagrangeNodal', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'CalcForceForNodes', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'CalcVolumeForceForElems', 'type': 'function'}</th>\n",
-       "      <td><img 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+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'CalcHourglassControlForElems', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'CalcFBHourglassForceForElems', 'type': 'function'}</th>\n",
-       "      <td><img 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uSSUyFzuTcMrzgDdxhcHiqcvhDUWtrSCLV7Xy47qW8uIZrFminmd96naJVIVSSQpJycEkkVOPFjz6hbWcdhPazDUEs7qO7VdyhoHlBUo5B6Lzkjk/hGvxA0hob9ikoksjEJIlmgcnzH8tfmWQqvzcHcy46nAoAt6h4Z/tTWrW+u2sTHbvHJ8llidmQhlHmljhN4DbQvbGar2XhObT7tJba/gSK2Fz9ij+y8xtM25vMO/5wD0AC+5JGatr4oibUrPT/7PuxdXMSzFDJB+6QsVDH9583Qk7N2Bj1GcjSfF11Jb3N5fnzI1uHt4bW30yaNnfzzEgWZ38tycc4wBnJIANAF1vC148r376pD/AGr9qS6jlS0IhQrG0WDHvJOUdwTvzyOmAKbceFtQGm6ba2Oq20T2t217NJcWTS+dMzs5IAkXaCzucc9RzxzMPGVo1xb2y2F+biUTmWPbGDbiFkWQyEvjA8xTlScjpnjLZ/GllaaRHqd5ZXdpbzuiWpuXhj+0bgWG0mTC/KpPzlT+PFACy+FS+syXaXcaWs17HfzQi3/eNMiKgxJu4UhFyNpPXnBq14h8Mad4jt40vLS1kljkjdZZrdZGVVkVyoz0DBSD9e9YbeM5dS1DTl0iZI7S6t5JC7adLeNvSXyyuYX2qAc/NkqcZBxV+fxxp1tb3VzLaXy20MM88U2xStysJxJ5fzZz6bguRyMigDeis4rKw+y6dDb2iIpESJEBGhOT91ccZOcAjNZugaRqWj6ebW41C0uSbmSffHZtHxJI0jLgyNzljg9h2PWsm58Tao/iIWEcT2EQtYZ/Lm0uW7kJeSVfmMEm2MYjBG71PoQFn8e2elpKNT2Cb7XcxRRxyRx5jiYKWJldQTyvAOTngcHABctvC7waxdasJdOS8eKSOFoNP8tRvIJaX5yZW+VRnK9+maguvCup3FjqCLrMEV3qNwkl1JHZssbRqoXywok3LuAALb84zjHGHv45sPOKwWGoXEO+GMXEUabC8yK8QGWB+bco6YBPOBzUqeMrSWazt47G8a6uWlTyCYlaIxSeW4O6QBiG7IWyOfSgB+oaFqV/of8AZI1CwgtpLc204i08j5SMfugZcJheBncMjPtTJPBlgddstVgmu4pYLhriRPtcxSVihT7u/aO2eOQMHg10dFAGBP4Whu9O1u1up/MfVZjK8nlj5MKqxgAk5ChF9icnAzTbfQ9VtFvJ4dTsft15OJZpHsGMWBGEAWMSgg4UZJY557Yx0NFAHLzeE7hvD0GgQ6jCmnC3WCffabpXwckowcKnt8p29q110s/8JBLqzz7mNqttDHs/1Q3FnOc87js9PuCtGigDDtdG1FL+51C71K3kvZLX7NE0FoY0QZJDMpdi5yfUDGfU1D/wjElrpujwaZepb3Wlp5cc00JlWQFNr7lDKSScNnPUd+RXRUUAcw3he9gGgRafqdvFb6Rg7bi0aV5m8to2JYSKBkOT06+3FXI9I1JPFc+rnULQ20tutv8AZxZsHCqXZT5nmYzuc5+XoMcda26KAMDQPDSaNeXV65svtFwixkWVmLaIKpY527mJYljkk84HTFbqxojOyoqlzuYgY3HAGT68AD8KdRQByWj+G9QTUftN/coltBql3e29ssQ3lpDIqsXDEbdkhO3aDk8njFdYQGBB5B4NLRQBysfh3VX8I22gfbrW3gjtDYzF7YzNJGBsV1O9QrFADyGAJ6cc2Y/CtvcpP/aTSvI179pie3upYmTbH5SfOhVs7ByOmWbr1roaKAOY0rws+lJ4esknea10qORzNI7FnlK7FwGJIG15DjOBwB7dKY0MiyFFLqCobHIBxkZ/AfkKdRQBz2peGXvNXl1SC+ENz/o7Qbod6o0Rl5YbhuDCZhjIxjOa57XPCmr2+nXMGkO9zd6nbXFveSmGMR5ldn3fNIDGAZXxgScdQSAT6FRQBz0WgajH4lXU31K1mt44lhgt5LNi0KYG/Y4kA3MRksVPAA7Vlx+HNbtrvR47KeG3WwS9iFxJGJV2O6GMFNyknaOx4K+h57KSVIhljjPQdzUeJZvvZiT0H3j/AIU7CuYWlaN/ZOpWyQ5kgsNMisLbJ+ZsH52bsOFj/Wr2taQ+taHf2EswV7mB4kI+6jFSAffB5rURFjXaigCnUX7BbuYF5pWrahqmj6it5aWi2a7pLZ7VpWLMpVwHEigDBwPlPIzz0q3pNhLa3eq3c+BJe3fmhQc7UVEjX8wm7/gWO1alFIZgaj/x9+Fv+v4/+kk9b9YGo/8AH34W/wCv4/8ApJPW/QAUUUUAFFFFAHJ2Xw58P2GvjV4YpvNV/MjhZwYo29QMZ47c11lRfZ0/vS/9/W/xo+zp/el/7+t/jUxhGHwqxvXxNbENOtJyaVtSDU9Ng1axa1uC6jcsiSRtteN1YMrKexBAP88isO/8Ex6nHMt5rurSPPbSWksgMCs8L4ynEQAHGcgA8nJNdH9nT+9L/wB/W/xo+zp/el/7+t/jVGBjS+FbVrqW7gvLy2u5Ln7SJ4ihZD5KQlQGUqVKxrkEHnnjjGhY6XFY3V3crNNLLdFDK0hHJVAgPAHUDJ9/SrP2dP70v/f1v8aPs6f3pf8Av63+NAHKP4Dsl0b+zp9S1G5gWxbTYfNaIGCGTarbdsYy2FXlt3T3OdnVPDtnq1wZ55J0k8kRKY2A2FZFkVxx94OikduOhpviAtaadDJDJIrte2kZO8n5XuI1YcnuGIrU+zp/el/7+t/jQBh/8IlbgpMupagt6J3nkvVaPzZS6KjBvk2gbUQcKMbRjFRx+DrGz0O40+2aaUNp8FkgllC8QKRG25V4bJyTg8gYHaug+zp/el/7+t/jR9nT+9L/AN/W/wAaAOX0fwncLJLea1dvNePfLdjZKHHyw+SAzCNAeCx+VV7dcEmey8FwafGywatqQb7JHZxs3kt5UKHKqqmPaepBLAkjqe9dD9nT+9L/AN/W/wAaPs6f3pf+/rf40Ac+vgqyW2sbT7Zdm0tJROsG2La8glMu4ny8r8x6IVGAABirp8N2h0UaYs1wiJObmOZWXzI5PNMoYHGOGPQgjHBzzWn9nT+9L/39b/Gj7On96X/v63+NAGTZ+FrG0uY7lpbi4mEVxHK8xU+d57Rs7MAAM/ulAxgAZGOmI08KxpYwWv8AauosLR0ezkZot9qVVlG07Pm+Vip37sg1tfZ0/vS/9/W/xo+zp/el/wC/rf40AUrXR0t76K9ku7m5uY7drcyTbPnUvuyQqgZyMcADHasuXwRp88NzbyXd61tLDPDFDvTbbLMcvs+XOfTcWwOBgcV0P2dP70v/AH9b/Gj7On96X/v63+NAGbd6C0+svqlvqt9ZTSQRwSLAISrqjOy53xsQcyN0IqBvCtus/wBptL+9s7rzZpPPhMZbErBnTDoylcqpGRkY69c7P2dP70v/AH9b/Gj7On96X/v63+NAGW3hq0d5Xee5Z5buC7diy5LxBAvbodgz9TjFVrvwdaXls1q97eLayXMlzJCBEQ7PIXPJQsuCSAVIOO+ea3fs6f3pf+/rf40fZ0/vS/8Af1v8aAJaKi+zp/el/wC/rf40fZ0/vS/9/W/xoAloqL7On96X/v63+NH2dP70v/f1v8aAJaKi+zp/el/7+t/jR9nT+9L/AN/W/wAaAJaKi+zp/el/7+t/jR9nT+9L/wB/W/xoAloqL7On96X/AL+t/jR9nT+9L/39b/GgCWiovs6f3pf+/rf40fZ0/vS/9/W/xoAloqL7On96X/v63+NH2dP70v8A39b/ABoAloqL7On96X/v63+NH2dP70v/AH9b/GgCWiovs6f3pf8Av63+NY+oaxbW9ybGyjnv9R/594ZWwnu7Zwg+vPtVRi5OyJlJRV2bhIAyTgDuah815eIR8v8Az0bp+HrWZp2l35Zp9WvTIz9LWEkRR/ifmY+5P4Vq/Z09ZP8Av63+NDST7gm2r7Cxwqh3HLOerN1qSovs6f3pf+/rf40fZ0/vS/8Af1v8akoloqL7On96X/v63+NH2dP70v8A39b/ABoAloqL7On96X/v63+NH2eM9TIf+2jf40AYuo/8ffhb/r+P/pJPW/WLrQC6r4bAGANRcAf9utxW1QAUUUUAFFFFABRRRQAyZpEgkaGMSShSURm2hmxwCcHH1rkJPG80Vjpkstnp1tPeXFzbuLrUTFFG0LshxJ5Z3ZK8fKK6+USGJxEypIVOxnXcAexIyMj2yPrXM2fh3XLPR5NPGsaa4llnd3bS2PErs7AAzdizdcjGMg9wCe+8Ty2eoPCLBJLW2a3S8nE+DG0zbV2Lt+cDKkkleDxk8UuneJpL7U4oHsVis7p547S4E25pGhba25No25wxXBOQOcHiqx8GeWYre31App5S1W4hki3yS/ZyNpD7ht3BVDfKcgcYPNWdO8MyWOqRTvfLLZ2rzyWluIdrRtM25tz7juxlguAMA85PNAFjxR/yCYP+wjY/+lUVbNc74ssLOa1sryW0ge6h1CyEU7RgvGDdRZ2tjI69q6KgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKRmVFLMwVQMkk4AFAC1T1HVLLSbfzrydY1Jwq9Wc+igck/SsuTXbnVJGt/D0KzKDte/lyIE9dvdz9OPerWnaBBZ3H225le91Fhhrqfkj2QdEHsK05FH4/u6/8Ay53LSH39P+CU9ms6//AKwy6Rpx/gU/6TKPc9Ix9Mn6VsafptnpdsLeyt0hjHJC9WPqT1J9zVqilKbastEVGCTu9WFFFFQWFFFFABRRRQAUUUUAY2t/8hbw5/2EX/8ASW4rZrG1v/kLeHP+wi//AKS3FbNABRRRQAUUUUAFFFFABRTZZY4YnlldY40Us7scBQOpJ7Cs5/EWhx2cF4+s6ettcEiCY3SBJSDghWzg4PpQBp0VSm1nS7fUI7CfUrOK9lAMdu86rI4JIGFJyckHH0p8WpWE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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'IntegrateStressForElems', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Irecv', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Isend', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Wait', 'type': 'function'}</th>\n",
-       "      <td><img 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+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Waitall', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Irecv', 'type': 'function'}</th>\n",
-       "      <td><img 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+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Isend', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Wait', 'type': 'function'}</th>\n",
-       "      <td><img 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+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Waitall', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'TimeIncrement', 'type': 'function'}</th>\n",
-       "      <td><img 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5cxrxxxnnnhl14DttR06+S+u5TqF355+02ry26IZUVTiNZOVxGhKsTkg+tTXfjzTLCAm8hntrlbhrZrWeSGNwwRXPzNII8bXQ/e/iHfihPHWmytG0FrezWzRW8z3KInlxLOxVC2WDdQc4BxQBNP4fv8A+0NFls9SgjttMXb5dxbvNJLlSrZkMo/h6ZBIPJz0ofw7dXWuapdX17bTadqNmllLaJbPG/lqZSP3ol6/vmyQo6DGKs+HdauNbt7uSfTprQQXc9urOyFZAkrpxtYnI2jOQOTxkc1s0ActbeA9Mhnvnln1CWO4uI7iMHULgNCyRhBh/M3E9eeOCB2FNi8I3Vp4YTQbPU4hbNHLFO91bvcO6uedpaT5eCeu4d8evV0UAYV1oV5dT2cTahENNtJYZo4hbnzyY8YBlL4wSOfkyQSM81J4i8Naf4ksfIvLW1eRSPLmmt1kMY3AkDPTOMda2aKAM640PT5tAutEit47WxuIZITHboIwocEEqAMA8k/Wq2maRfWdzcXt3qENzfXHlJJItsY08qPOFC7zg5ZznJ5bpjitqigDmrDwbaaRq51DTbi4iK2ht4oZriaaNCW3bsNJyP8AZ49iKhtvCd8mkRWtzqVpNc22oNf2sy2LKiSM7uQyGU7h+8YDDKRx3Ga6uigDlpfCl4/h6TRY9StltroT/a2ayLMxmdnfy/nAQZc4BDY4697F34YS91+21CdrMxWzI6BbPFwxUfKGmLElQ3zYCjkDnrnoaKAOW03wreabJDIuqQSNaWklpZl7Q8K7oxMvz/vD8i8jZ1Y9Twl34Vvrrw/rlj/alsl3rRf7TOLM+WqtEIjsj8zIO1QcljzntwOqooA5bVvC19q6y+dqtuj3dj9hvNtmSGTLHdEDIfLb5jyS44U44qU+FS+qGSS9VtN+1NeC0MPzea0ZQ5k3cp8xbG3OT1xxXSUUAcgvghpbBra+1JbkxW0NtaN9m2iNYnEiFxuO9iypu+6CF4AyaluvCuoT6Lf2keq2sd1qF2Lm5mNkxj4VFASMSAr/AKtckscnd68dVRQBzmreGJNcms2v5dOdIlTznFh++YhssI5C58tGwARhjjPzc5CW3heaDV4bl9QR7W3vLi9hi+z4kEkocMGk3fMg8xsDaOi8/LXSUUAYvh7SNR0n7d9uv7W7+03DXA8i0aHYzfeHMj5HTHTHPXtsLGiM7KiqXO5iBjccAZPrwAPwp1FAHJaJ4b1CO9W41C5Rbe21S9vbe1WIbt0ryqrGQMQVKSE7cAgtyeMV1boskbIwyrAgj2p1FAHKnw5qdz4RttBkvbWCAWf2G5LWxleRANm9G3gKSozyrYJ9qtL4Xt7sXDakZGke+N1E1vdSxNHtj8pPnQq2dg5HTLN1610FFAHMaV4WfSk8PWSTvNa6VHI5mkdizyldi4DEkDa8hxnA4A9ulMaGRZCil1BUNjkA4yM/gPyFOooA57UvDL3mry6pBfCG5/0doN0O9UaIy8sNw3BhMwxkYxnNc9rnhTV7fTrmDSHe5u9Ttri3vJTDGI8yuz7vmkBjAMr4wJOOoJAJ9CooA56LQNRj8Srqb6lazW8cSwwW8lmxaFMDfscSAbmIyWKngAdqy4/Dmt213o8dlPDbrYJexC4kjEq7HdDGCm5STtHY8FfQ89rSMwVSzEADqTQBg6R4fOlX9ttYm0sNMisLZmPzNg5dm/BY/wBas63Yvr2iX+mxSeUtxA8QmIzhiCAR9DV7DXPLZWHsO7f/AFqsAADAGAKewtzn7nSNUvdS0a/ju7OzSzXdJavatKxZlIcBxIoHBwPlPIzz0q5pNhLa3eq3c+BJe3fmhQc7UVEjX8wm7/gWO1alFIZgaj/x9+Fv+v4/+kk9b9YGo/8AH34W/wCv4/8ApJPW/QAUUUUAFFFFAGL4k8Lab4ps47fUFkBiYtHLEwDpnrgkEc4HbtVjQtCsfDumJp+noyxKSxLHLOx6kn14FXfs6f3pf+/rf40fZ0/vS/8Af1v8ankjzc1tTd4ms6KoOT5E726EtYt1bw6neWs7hhLCsiRhJBj94uGDccEAVqNbIykb5RkYyJWyP1rno/D2uIis3iJWniCLC32MhGAzkyL5mWJB/hZAMcDtVGA2Lw5YQWPkRtclE+xyvukXK/ZWUp/Dzkx8+vOMVI3h2AX5ubW8vbO4aeY/aYWjLfvNrOm10ZduUXGRkEdeTT5fD+pmC3jh150PleXdO8AYy5OSyfMAjcsOdwwehIzVhdGvhrHnnVWOn+YZRa+Wd+dm3b5m77ufmxt3Z/ixxQBHP4atPt0V+09y86XkNzlmXDOsRh546bXJ+vtxRB4Vt7OeOayv761lXeJGjMZ85WlaXawZCMBnbBGCATzT/EK/ZrCCeJnWX7baR7i5J2vcRqw59VYj8a1vs6f3pf8Av63+NAGQ3hi2FtFHBd3dvNDdz3cVxGULo8zu7j5lKlf3jDBB4A7jNE/he2uhdG4u7uWS6toLeWRmQMRC7urcKACTI2eMYxgCtf7On96X/v63+NH2dP70v/f1v8aAMifwvbSX09/BeXlrey3JuPPhKEoTFHEVAZSpUrEhwQeRnI4ws3hi1uEuhPdXUj3UdvHLIzLuPksWU8LjJJOeMegFa32dP70v/f1v8aPs6f3pf+/rf40AVNM0lNKe78m5neG4mecQybdsTOzO+0hQ3LMT8xPtitCovs6f3pf+/rf40fZ0/vS/9/W/xoAloqL7On96X/v63+NH2dP70v8A39b/ABoAloqL7On96X/v63+NH2dP70v/AH9b/GgCWiovs6f3pf8Av63+NH2dP70v/f1v8aAJaKi+zp/el/7+t/jR9nT+9L/39b/GgCWiovs6f3pf+/rf40fZ0/vS/wDf1v8AGgCWiovs6f3pf+/rf40fZ0/vS/8Af1v8aAJaKi+zp/el/wC/rf40fZ0/vS/9/W/xoAloqL7On96X/v63+NH2dP70v/f1v8aAJaKi+zp/el/7+t/jR9nT+9L/AN/W/wAaAJaKi+zp/el/7+t/jR9nT+9L/wB/W/xoAloqL7On96X/AL+t/jR9nT+9L/39b/GgCWiovs6f3pf+/rf40fZ0/vS/9/W/xoAloqL7On96X/v63+NH2dP70v8A39b/ABoAloqL7On96X/v63+NZeralb6aY4I0nub+biC1jlbc/ueflX1Jpxi5OyFKSirs1pJFiXcx+g7mo1jaVg8wwB91PT6+9UNL028SMzapdNNcuc7I2IjhH91e5+prR+zp/el/7+t/jTejshLVXZLRUX2dP70v/f1v8aPs6f3pf+/rf41JRLRUX2dP70v/AH9b/Gj7PGepkP8A20b/ABoAxdR/4+/C3/X8f/SSet+sXWgF1Xw2AMAai4A/7dbitqgAooooAKKKKACiiigAooooAKKKKAMbxR/yCYP+wjY/+lUVbNY3ij/kEwf9hGx/9Koq2aACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACikJCgkkADkk1zsuo3fiGVrXRpDBYqSs2ogfe9Vh9T/tdB2zVRg5ehEpqPqWNR1qZrttL0eNbjUMfvHb/AFVsD3c+voo5NWNJ0WHTBJM0jXN9NzPdS/fkPp7L6AcCrGnabaaVaLbWcQSMHJPVmPdmPUk+tW6qUlbljt+YowbfNLf8gooorM0CiiigAooooAxtb/5C3hz/ALCL/wDpLcVs1ja3/wAhbw5/2EX/APSW4rZoAKKKKACiiigAooooAKKKKACiiigDG8Uf8gmD/sI2P/pVFWzWN4o/5BMH/YRsf/SqKtmgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACoLy9ttPtJLq7mSGFBlnY/wCefaquq6zb6UiKyvNdTHEFtEMySn2HYepPAqlZ6NcXl1HqWusktwh3QWqnMVt/8U/+0fwrSMNOaWxnKbvyx3/IgFve+KCHvEls9H6rbH5Zbkesn91f9nqe9dFFFHDEsUSKkaDaqqMAD0Ap9FKU76dBxgo69QoooqCwooooAKKKKACiiigDG1v/AJC3hz/sIv8A+ktxWzWNrf8AyFvDn/YRf/0luK2aACiiigAooooAKKK83+23tr4yu9Om1WFXuru2ZHfVUG1VuHcxrCW3AtE0cW0LgkEk+oB6NJv8p/Lxvwdueme1eOaZcWvl6NBqkWlS2shim124SPDRv5cg8u8ZidzecUPzYwQeAK9looA8iuvs39jSfb8eX/Z93/YHmfe3/aJPJ8nP/LTy/s+3HOOnetmPyv8AhLk8zy/+Ei/tcbsf6z7J5HPv5WP+A7/9qvRKKAOd8WPeC1sligga1OoWXmyNOVdT9qixtTYQ34sPxroqxvFH/IJg/wCwjY/+lUVbNABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRXiGi+JPFg8fql9c3CqJ/wDTLeViIYos/McHhQByD9PWsatZU3FWvc9HA5dLGQqTjJLkV9T2+sO/1uWS7fTNFiW5vl4lkb/VW3u5HU/7I5+lVzd33iYmPT2kstKPDXmMSzj0iB+6P9o/hW3Yafa6ZaJa2cKxQr0A7n1J7n3NddlD4t+3+f8AkeTzOfw7d/8AL/MqaVokWnO9zLK91qEw/fXUv3m9gP4V9hWpRRWcpOTuzSMVFWQUUUUhhRRRQAUUUUAFFFFABRRRQBja3/yFvDn/AGEX/wDSW4rZrG1v/kLeHP8AsIv/AOktxWzQAUUUUAFFFFABXn+kXU9vrdzbG5mtkbVZiLb+wp3Uhpic+fjb82d27ou70Fd3coJLWZCsjBkYFY32MeOgbIwffIx6iuHsrfU7u9S6k8O61a3TagXaSfV2+zpEJycmNbg5Plj7oUqT220Ad7RRRQAUUUUAY3ij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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'MPI_Allreduce', 'type': 'function'}</th>\n",
-       "      <td><img 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/></td>\n",
+       "      <td><img 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/></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
@@ -1815,13 +1815,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ff70415",
+   "id": "a4d0aab7",
    "metadata": {
     "papermill": {
-     "duration": 0.009158,
-     "end_time": "2023-12-09T20:58:42.337015",
+     "duration": 0.012158,
+     "end_time": "2023-12-22T05:36:06.838025",
      "exception": false,
-     "start_time": "2023-12-09T20:58:42.327857",
+     "start_time": "2023-12-22T05:36:06.825867",
      "status": "completed"
     },
     "tags": []
@@ -1835,19 +1835,19 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "4fadda2f",
+   "id": "3bf96c50",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:42.355142Z",
-     "iopub.status.busy": "2023-12-09T20:58:42.355029Z",
-     "iopub.status.idle": "2023-12-09T20:58:42.357001Z",
-     "shell.execute_reply": "2023-12-09T20:58:42.356777Z"
+     "iopub.execute_input": "2023-12-22T05:36:06.859858Z",
+     "iopub.status.busy": "2023-12-22T05:36:06.859718Z",
+     "iopub.status.idle": "2023-12-22T05:36:06.862127Z",
+     "shell.execute_reply": "2023-12-22T05:36:06.861780Z"
     },
     "papermill": {
-     "duration": 0.011775,
-     "end_time": "2023-12-09T20:58:42.357637",
+     "duration": 0.014517,
+     "end_time": "2023-12-22T05:36:06.862795",
      "exception": false,
-     "start_time": "2023-12-09T20:58:42.345862",
+     "start_time": "2023-12-22T05:36:06.848278",
      "status": "completed"
     },
     "tags": []
@@ -1859,13 +1859,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4fc3f63",
+   "id": "2c579f1a",
    "metadata": {
     "papermill": {
-     "duration": 0.009087,
-     "end_time": "2023-12-09T20:58:42.375298",
+     "duration": 0.011152,
+     "end_time": "2023-12-22T05:36:06.884763",
      "exception": false,
-     "start_time": "2023-12-09T20:58:42.366211",
+     "start_time": "2023-12-22T05:36:06.873611",
      "status": "completed"
     },
     "tags": []
@@ -1879,19 +1879,19 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "cd78c612",
+   "id": "d6c24f7c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:42.392737Z",
-     "iopub.status.busy": "2023-12-09T20:58:42.392637Z",
-     "iopub.status.idle": "2023-12-09T20:58:42.394731Z",
-     "shell.execute_reply": "2023-12-09T20:58:42.394499Z"
+     "iopub.execute_input": "2023-12-22T05:36:06.907111Z",
+     "iopub.status.busy": "2023-12-22T05:36:06.906951Z",
+     "iopub.status.idle": "2023-12-22T05:36:06.909638Z",
+     "shell.execute_reply": "2023-12-22T05:36:06.909333Z"
     },
     "papermill": {
-     "duration": 0.011255,
-     "end_time": "2023-12-09T20:58:42.395208",
+     "duration": 0.015046,
+     "end_time": "2023-12-22T05:36:06.910356",
      "exception": false,
-     "start_time": "2023-12-09T20:58:42.383953",
+     "start_time": "2023-12-22T05:36:06.895310",
      "status": "completed"
     },
     "tags": []
@@ -1900,7 +1900,7 @@
     {
      "data": {
       "text/plain": [
-       "9.311422624087943"
+       "9.311422624087944"
       ]
      },
      "execution_count": 9,
@@ -1914,13 +1914,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "718837d6",
+   "id": "1b12bf59",
    "metadata": {
     "papermill": {
-     "duration": 0.008475,
-     "end_time": "2023-12-09T20:58:42.412553",
+     "duration": 0.010421,
+     "end_time": "2023-12-22T05:36:06.931414",
      "exception": false,
-     "start_time": "2023-12-09T20:58:42.404078",
+     "start_time": "2023-12-22T05:36:06.920993",
      "status": "completed"
     },
     "tags": []
@@ -1934,19 +1934,19 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "0334bca8",
+   "id": "ae608739",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:42.432761Z",
-     "iopub.status.busy": "2023-12-09T20:58:42.432648Z",
-     "iopub.status.idle": "2023-12-09T20:58:42.475564Z",
-     "shell.execute_reply": "2023-12-09T20:58:42.475296Z"
+     "iopub.execute_input": "2023-12-22T05:36:06.953515Z",
+     "iopub.status.busy": "2023-12-22T05:36:06.953367Z",
+     "iopub.status.idle": "2023-12-22T05:36:07.000637Z",
+     "shell.execute_reply": "2023-12-22T05:36:07.000272Z"
     },
     "papermill": {
-     "duration": 0.054996,
-     "end_time": "2023-12-09T20:58:42.476169",
+     "duration": 0.059313,
+     "end_time": "2023-12-22T05:36:07.001346",
      "exception": false,
-     "start_time": "2023-12-09T20:58:42.421173",
+     "start_time": "2023-12-22T05:36:06.942033",
      "status": "completed"
     },
     "tags": []
@@ -1963,7 +1963,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2001,14 +2001,14 @@
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 2.93238,
-   "end_time": "2023-12-09T20:58:42.797607",
+   "duration": 3.635006,
+   "end_time": "2023-12-22T05:36:07.416599",
    "environment_variables": {},
    "exception": null,
    "input_path": "03_extrap-with-metadata-aggregated.ipynb",
    "output_path": "03_extrap-with-metadata-aggregated.ipynb",
    "parameters": {},
-   "start_time": "2023-12-09T20:58:39.865227",
+   "start_time": "2023-12-22T05:36:03.781593",
    "version": "2.5.0"
   },
   "vscode": {
diff --git a/docs/query_language.ipynb b/docs/query_language.ipynb
index 421ae632..45a6d1e3 100644
--- a/docs/query_language.ipynb
+++ b/docs/query_language.ipynb
@@ -2,13 +2,13 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4b25434e",
+   "id": "74bef2e4",
    "metadata": {
     "papermill": {
-     "duration": 0.003229,
-     "end_time": "2023-12-15T21:10:44.499440",
+     "duration": 0.003305,
+     "end_time": "2023-12-22T05:36:14.061621",
      "exception": false,
-     "start_time": "2023-12-15T21:10:44.496211",
+     "start_time": "2023-12-22T05:36:14.058316",
      "status": "completed"
     },
     "tags": []
@@ -32,19 +32,19 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "a0e40cdd",
+   "id": "94857782",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-15T21:10:44.503461Z",
-     "iopub.status.busy": "2023-12-15T21:10:44.503299Z",
-     "iopub.status.idle": "2023-12-15T21:10:45.081823Z",
-     "shell.execute_reply": "2023-12-15T21:10:45.081438Z"
+     "iopub.execute_input": "2023-12-22T05:36:14.065932Z",
+     "iopub.status.busy": "2023-12-22T05:36:14.065766Z",
+     "iopub.status.idle": "2023-12-22T05:36:14.631304Z",
+     "shell.execute_reply": "2023-12-22T05:36:14.630844Z"
     },
     "papermill": {
-     "duration": 0.581263,
-     "end_time": "2023-12-15T21:10:45.082587",
+     "duration": 0.568827,
+     "end_time": "2023-12-22T05:36:14.632283",
      "exception": false,
-     "start_time": "2023-12-15T21:10:44.501324",
+     "start_time": "2023-12-22T05:36:14.063456",
      "status": "completed"
     },
     "scrolled": true,
@@ -411,13 +411,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b05de32c",
+   "id": "221fc75d",
    "metadata": {
     "papermill": {
-     "duration": 0.001802,
-     "end_time": "2023-12-15T21:10:45.086678",
+     "duration": 0.001826,
+     "end_time": "2023-12-22T05:36:14.636427",
      "exception": false,
-     "start_time": "2023-12-15T21:10:45.084876",
+     "start_time": "2023-12-22T05:36:14.634601",
      "status": "completed"
     },
     "tags": []
@@ -431,19 +431,19 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "befe9e21",
+   "id": "16e47681",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-15T21:10:45.090579Z",
-     "iopub.status.busy": "2023-12-15T21:10:45.090443Z",
-     "iopub.status.idle": "2023-12-15T21:10:45.163143Z",
-     "shell.execute_reply": "2023-12-15T21:10:45.162798Z"
+     "iopub.execute_input": "2023-12-22T05:36:14.640947Z",
+     "iopub.status.busy": "2023-12-22T05:36:14.640744Z",
+     "iopub.status.idle": "2023-12-22T05:36:14.713657Z",
+     "shell.execute_reply": "2023-12-22T05:36:14.713329Z"
     },
     "papermill": {
-     "duration": 0.075635,
-     "end_time": "2023-12-15T21:10:45.163977",
+     "duration": 0.076275,
+     "end_time": "2023-12-22T05:36:14.714462",
      "exception": false,
-     "start_time": "2023-12-15T21:10:45.088342",
+     "start_time": "2023-12-22T05:36:14.638187",
      "status": "completed"
     },
     "tags": []
@@ -464,13 +464,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cff15eab",
+   "id": "599161df",
    "metadata": {
     "papermill": {
-     "duration": 0.001834,
-     "end_time": "2023-12-15T21:10:45.167937",
+     "duration": 0.001888,
+     "end_time": "2023-12-22T05:36:14.718479",
      "exception": false,
-     "start_time": "2023-12-15T21:10:45.166103",
+     "start_time": "2023-12-22T05:36:14.716591",
      "status": "completed"
     },
     "tags": []
@@ -485,19 +485,19 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "b041d2d3",
+   "id": "401971d2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-15T21:10:45.171908Z",
-     "iopub.status.busy": "2023-12-15T21:10:45.171796Z",
-     "iopub.status.idle": "2023-12-15T21:10:45.173908Z",
-     "shell.execute_reply": "2023-12-15T21:10:45.173601Z"
+     "iopub.execute_input": "2023-12-22T05:36:14.722531Z",
+     "iopub.status.busy": "2023-12-22T05:36:14.722417Z",
+     "iopub.status.idle": "2023-12-22T05:36:14.724478Z",
+     "shell.execute_reply": "2023-12-22T05:36:14.724249Z"
     },
     "papermill": {
-     "duration": 0.004705,
-     "end_time": "2023-12-15T21:10:45.174465",
+     "duration": 0.004719,
+     "end_time": "2023-12-22T05:36:14.725023",
      "exception": false,
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@@ -531,13 +531,13 @@
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+   "id": "13ff70f8",
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@@ -556,19 +556,19 @@
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    "cell_type": "code",
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-   "id": "ccc2108c",
+   "id": "a3b14d98",
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-     "shell.execute_reply": "2023-12-15T21:10:45.193223Z"
+     "iopub.execute_input": "2023-12-22T05:36:14.732333Z",
+     "iopub.status.busy": "2023-12-22T05:36:14.732242Z",
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@@ -718,13 +718,13 @@
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   {
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+   "id": "3b12c51d",
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      "status": "completed"
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     "tags": []
@@ -740,19 +740,19 @@
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-   "id": "d19d5c95",
+   "id": "dd9c9d9a",
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-     "shell.execute_reply": "2023-12-15T21:10:45.207339Z"
+     "iopub.execute_input": "2023-12-22T05:36:14.753040Z",
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      "status": "completed"
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     "scrolled": false,
@@ -855,13 +855,13 @@
   },
   {
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+   "id": "7c9af089",
    "metadata": {
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      "status": "completed"
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     "tags": []
@@ -877,19 +877,19 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "848ce31d",
+   "id": "3f62722b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-15T21:10:45.216217Z",
-     "iopub.status.busy": "2023-12-15T21:10:45.216118Z",
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-     "shell.execute_reply": "2023-12-15T21:10:45.274686Z"
+     "iopub.execute_input": "2023-12-22T05:36:14.766976Z",
+     "iopub.status.busy": "2023-12-22T05:36:14.766867Z",
+     "iopub.status.idle": "2023-12-22T05:36:14.824650Z",
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      "status": "completed"
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     "scrolled": false,
@@ -958,13 +958,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff25e0a6",
+   "id": "748ea064",
    "metadata": {
     "papermill": {
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-     "end_time": "2023-12-15T21:10:45.280161",
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      "status": "completed"
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     "tags": []
@@ -978,19 +978,19 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "e336ea2d",
+   "id": "efdef66f",
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2023-12-15T21:10:45.352384Z"
+     "iopub.execute_input": "2023-12-22T05:36:14.834234Z",
+     "iopub.status.busy": "2023-12-22T05:36:14.834129Z",
+     "iopub.status.idle": "2023-12-22T05:36:14.907016Z",
+     "shell.execute_reply": "2023-12-22T05:36:14.906715Z"
     },
     "papermill": {
-     "duration": 0.070942,
-     "end_time": "2023-12-15T21:10:45.353267",
+     "duration": 0.075873,
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      "exception": false,
-     "start_time": "2023-12-15T21:10:45.282325",
+     "start_time": "2023-12-22T05:36:14.831761",
      "status": "completed"
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     "tags": []
@@ -1058,13 +1058,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6aa18b6",
+   "id": "a3558ad9",
    "metadata": {
     "papermill": {
-     "duration": 0.002194,
-     "end_time": "2023-12-15T21:10:45.357990",
+     "duration": 0.002249,
+     "end_time": "2023-12-22T05:36:14.912594",
      "exception": false,
-     "start_time": "2023-12-15T21:10:45.355796",
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      "status": "completed"
     },
     "tags": []
@@ -1078,19 +1078,19 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "caa26b96",
+   "id": "2c035688",
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2023-12-15T21:10:45.429780Z"
+     "iopub.execute_input": "2023-12-22T05:36:14.917272Z",
+     "iopub.status.busy": "2023-12-22T05:36:14.917173Z",
+     "iopub.status.idle": "2023-12-22T05:36:14.986830Z",
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      "exception": false,
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@@ -1168,13 +1168,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46ae00e5",
+   "id": "61843265",
    "metadata": {
     "papermill": {
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-     "end_time": "2023-12-15T21:10:45.435666",
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      "exception": false,
-     "start_time": "2023-12-15T21:10:45.433015",
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      "status": "completed"
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     "tags": []
@@ -1188,19 +1188,19 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "fe2bb69f",
+   "id": "21894528",
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2023-12-15T21:10:45.440157Z",
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-     "shell.execute_reply": "2023-12-15T21:10:45.511674Z"
+     "iopub.execute_input": "2023-12-22T05:36:14.997707Z",
+     "iopub.status.busy": "2023-12-22T05:36:14.997604Z",
+     "iopub.status.idle": "2023-12-22T05:36:15.072421Z",
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-     "end_time": "2023-12-15T21:10:45.512704",
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      "exception": false,
-     "start_time": "2023-12-15T21:10:45.437787",
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      "status": "completed"
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     "scrolled": true,
@@ -1276,13 +1276,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "affcd52b",
+   "id": "d9797303",
    "metadata": {
     "papermill": {
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-     "end_time": "2023-12-15T21:10:45.518019",
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      "exception": false,
-     "start_time": "2023-12-15T21:10:45.515587",
+     "start_time": "2023-12-22T05:36:15.075873",
      "status": "completed"
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     "tags": []
@@ -1296,19 +1296,19 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "b8d6f1bb",
+   "id": "81e33af2",
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-     "shell.execute_reply": "2023-12-15T21:10:45.605125Z"
+     "iopub.execute_input": "2023-12-22T05:36:15.083421Z",
+     "iopub.status.busy": "2023-12-22T05:36:15.083319Z",
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-     "end_time": "2023-12-15T21:10:45.606035",
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      "exception": false,
-     "start_time": "2023-12-15T21:10:45.520268",
+     "start_time": "2023-12-22T05:36:15.080705",
      "status": "completed"
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@@ -1404,14 +1404,14 @@
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 2.044899,
-   "end_time": "2023-12-15T21:10:45.914022",
+   "duration": 2.047241,
+   "end_time": "2023-12-22T05:36:15.479638",
    "environment_variables": {},
    "exception": null,
    "input_path": "05_thicket_query_language.ipynb",
    "output_path": "05_thicket_query_language.ipynb",
    "parameters": {},
-   "start_time": "2023-12-15T21:10:43.869123",
+   "start_time": "2023-12-22T05:36:13.432397",
    "version": "2.5.0"
   }
  },
diff --git a/docs/stats-functions.ipynb b/docs/stats-functions.ipynb
index 1d947d03..ee52db75 100644
--- a/docs/stats-functions.ipynb
+++ b/docs/stats-functions.ipynb
@@ -2,13 +2,13 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "a32799d4",
+   "id": "0d3ceeab",
    "metadata": {
     "papermill": {
-     "duration": 0.026964,
-     "end_time": "2023-12-09T20:58:43.635015",
+     "duration": 0.006325,
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      "exception": false,
-     "start_time": "2023-12-09T20:58:43.608051",
+     "start_time": "2023-12-22T05:36:08.408029",
      "status": "completed"
     },
     "tags": []
@@ -32,19 +32,19 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "c93e322c",
+   "id": "c7b2cd1b",
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-     "shell.execute_reply": "2023-12-09T20:58:44.164597Z"
+     "iopub.execute_input": "2023-12-22T05:36:08.427127Z",
+     "iopub.status.busy": "2023-12-22T05:36:08.426986Z",
+     "iopub.status.idle": "2023-12-22T05:36:08.986396Z",
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-     "end_time": "2023-12-09T20:58:44.165535",
+     "duration": 0.566569,
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      "exception": false,
-     "start_time": "2023-12-09T20:58:43.648591",
+     "start_time": "2023-12-22T05:36:08.420690",
      "status": "completed"
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     "tags": []
@@ -398,13 +398,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f2c2ae3",
+   "id": "b30f92cd",
    "metadata": {
     "papermill": {
-     "duration": 0.00503,
-     "end_time": "2023-12-09T20:58:44.175981",
+     "duration": 0.005404,
+     "end_time": "2023-12-22T05:36:08.999330",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.170951",
+     "start_time": "2023-12-22T05:36:08.993926",
      "status": "completed"
     },
     "tags": []
@@ -418,27 +418,27 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "b5eb1881",
+   "id": "22a18d50",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:44.185926Z",
-     "iopub.status.busy": "2023-12-09T20:58:44.185784Z",
-     "iopub.status.idle": "2023-12-09T20:58:44.761067Z",
-     "shell.execute_reply": "2023-12-09T20:58:44.760697Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.010141Z",
+     "iopub.status.busy": "2023-12-22T05:36:09.009950Z",
+     "iopub.status.idle": "2023-12-22T05:36:09.387333Z",
+     "shell.execute_reply": "2023-12-22T05:36:09.386973Z"
     },
     "papermill": {
-     "duration": 0.581305,
-     "end_time": "2023-12-09T20:58:44.761885",
+     "duration": 0.383935,
+     "end_time": "2023-12-22T05:36:09.388245",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.180580",
+     "start_time": "2023-12-22T05:36:09.004310",
      "status": "completed"
     },
     "tags": []
    },
    "outputs": [],
    "source": [
-    "clang = \"../data/quartz/Clang_900_BaseSeq_O3_08388608/\"\n",
-    "gcc = \"../data/quartz/GCC_831_BaseSeq_O3_08388608/\"\n",
+    "clang = \"../data/quartz/clang14.0.6_BaseSeq_8388608/\"\n",
+    "gcc = \"../data/quartz/GCC_10.3.1_BaseSeq_08388608/O3\"\n",
     "\n",
     "# create thickets for each dataset originating from clang and gcc compilers\n",
     "clang_th = th.Thicket.from_caliperreader(clang)\n",
@@ -447,13 +447,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7403a67b",
+   "id": "64645bdd",
    "metadata": {
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-     "end_time": "2023-12-09T20:58:44.771918",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:44.766898",
+     "start_time": "2023-12-22T05:36:09.393749",
      "status": "completed"
     },
     "tags": []
@@ -468,19 +468,19 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "054d9736",
+   "id": "64e4b8b9",
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-     "shell.execute_reply": "2023-12-09T20:58:44.783443Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.409486Z",
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     },
     "papermill": {
-     "duration": 0.007788,
-     "end_time": "2023-12-09T20:58:44.784245",
+     "duration": 0.008401,
+     "end_time": "2023-12-22T05:36:09.412226",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.776457",
+     "start_time": "2023-12-22T05:36:09.403825",
      "status": "completed"
     },
     "tags": []
@@ -513,13 +513,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cb30b9a",
+   "id": "3eb4a3a6",
    "metadata": {
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      "exception": false,
-     "start_time": "2023-12-09T20:58:44.788951",
+     "start_time": "2023-12-22T05:36:09.416985",
      "status": "completed"
     },
     "tags": []
@@ -533,19 +533,19 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "a51bad4d",
+   "id": "e778748a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:44.803334Z",
-     "iopub.status.busy": "2023-12-09T20:58:44.803233Z",
-     "iopub.status.idle": "2023-12-09T20:58:44.845243Z",
-     "shell.execute_reply": "2023-12-09T20:58:44.844982Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.432974Z",
+     "iopub.status.busy": "2023-12-22T05:36:09.432820Z",
+     "iopub.status.idle": "2023-12-22T05:36:09.471643Z",
+     "shell.execute_reply": "2023-12-22T05:36:09.471339Z"
     },
     "papermill": {
-     "duration": 0.047503,
-     "end_time": "2023-12-09T20:58:44.845748",
+     "duration": 0.04509,
+     "end_time": "2023-12-22T05:36:09.472357",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.798245",
+     "start_time": "2023-12-22T05:36:09.427267",
      "status": "completed"
     },
     "tags": []
@@ -593,6 +593,10 @@
        "      <th>Bytes/Rep</th>\n",
        "      <th>Kernels/Rep</th>\n",
        "      <th>Flops/Rep</th>\n",
+       "      <th>Retiring</th>\n",
+       "      <th>Frontend bound</th>\n",
+       "      <th>Backend bound</th>\n",
+       "      <th>Bad speculation</th>\n",
        "      <th>Branch mispredict</th>\n",
        "      <th>Machine clears</th>\n",
        "      <th>Frontend latency</th>\n",
@@ -603,10 +607,6 @@
        "      <th>L1 bound</th>\n",
        "      <th>L2 bound</th>\n",
        "      <th>L3 bound</th>\n",
-       "      <th>Retiring</th>\n",
-       "      <th>Frontend bound</th>\n",
-       "      <th>Backend bound</th>\n",
-       "      <th>Bad speculation</th>\n",
        "      <th>nid</th>\n",
        "      <th>time</th>\n",
        "      <th>time (exc)</th>\n",
@@ -616,12 +616,12 @@
        "      <th>Bytes/Rep</th>\n",
        "      <th>Kernels/Rep</th>\n",
        "      <th>Flops/Rep</th>\n",
-       "      <th>Branch mispredict</th>\n",
-       "      <th>Machine clears</th>\n",
        "      <th>Retiring</th>\n",
        "      <th>Frontend bound</th>\n",
        "      <th>Backend bound</th>\n",
        "      <th>Bad speculation</th>\n",
+       "      <th>Branch mispredict</th>\n",
+       "      <th>Machine clears</th>\n",
        "      <th>Frontend latency</th>\n",
        "      <th>Frontend bandwidth</th>\n",
        "      <th>Memory bound</th>\n",
@@ -686,512 +686,512 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">{'name': 'Base_Seq', 'type': 'function'}</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
        "      <th>0</th>\n",
        "      <td>1</td>\n",
-       "      <td>1816.892754</td>\n",
-       "      <td>0.007212</td>\n",
+       "      <td>1.905843e+12</td>\n",
+       "      <td>4729399.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>500.0</td>\n",
+       "      <td>50.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>201326592.0</td>\n",
+       "      <td>1.073742e+09</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>16777216.0</td>\n",
-       "      <td>0.979491</td>\n",
-       "      <td>0.020509</td>\n",
-       "      <td>0.337277</td>\n",
-       "      <td>0.662723</td>\n",
-       "      <td>0.479179</td>\n",
-       "      <td>0.135116</td>\n",
-       "      <td>0.057817</td>\n",
-       "      <td>0.078872</td>\n",
-       "      <td>-0.029453</td>\n",
-       "      <td>0.025042</td>\n",
-       "      <td>0.283309</td>\n",
-       "      <td>0.524702</td>\n",
-       "      <td>0.224746</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.106787</td>\n",
+       "      <td>0.398106</td>\n",
+       "      <td>0.421387</td>\n",
+       "      <td>0.073720</td>\n",
+       "      <td>0.996068</td>\n",
+       "      <td>0.003932</td>\n",
+       "      <td>0.378715</td>\n",
+       "      <td>0.621285</td>\n",
+       "      <td>0.321128</td>\n",
+       "      <td>0.462123</td>\n",
+       "      <td>0.057753</td>\n",
+       "      <td>-0.107304</td>\n",
+       "      <td>0.008284</td>\n",
+       "      <td>0.015874</td>\n",
        "      <td>1</td>\n",
-       "      <td>1822.338783</td>\n",
-       "      <td>0.006938</td>\n",
+       "      <td>1.947811e+12</td>\n",
+       "      <td>5238545.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>500.0</td>\n",
+       "      <td>50.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>201326592.0</td>\n",
+       "      <td>1.073742e+09</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>16777216.0</td>\n",
-       "      <td>0.966496</td>\n",
-       "      <td>0.033504</td>\n",
-       "      <td>0.147419</td>\n",
-       "      <td>0.327396</td>\n",
-       "      <td>0.446536</td>\n",
-       "      <td>0.078649</td>\n",
-       "      <td>0.227622</td>\n",
-       "      <td>0.772378</td>\n",
-       "      <td>0.264838</td>\n",
-       "      <td>0.519253</td>\n",
-       "      <td>0.043300</td>\n",
-       "      <td>-0.106758</td>\n",
-       "      <td>0.059585</td>\n",
-       "      <td>0.008914</td>\n",
-       "      <td>Base_Seq</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.141325</td>\n",
+       "      <td>0.304490</td>\n",
+       "      <td>0.491996</td>\n",
+       "      <td>0.062190</td>\n",
+       "      <td>0.998104</td>\n",
+       "      <td>0.001896</td>\n",
+       "      <td>0.256295</td>\n",
+       "      <td>0.743705</td>\n",
+       "      <td>0.378793</td>\n",
+       "      <td>0.343142</td>\n",
+       "      <td>0.054910</td>\n",
+       "      <td>-0.072312</td>\n",
+       "      <td>0.051793</td>\n",
+       "      <td>0.014941</td>\n",
+       "      <td>RAJAPerf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
-       "      <td>1807.026719</td>\n",
-       "      <td>0.007288</td>\n",
+       "      <td>1.910519e+12</td>\n",
+       "      <td>4716924.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>500.0</td>\n",
+       "      <td>50.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>201326592.0</td>\n",
+       "      <td>1.073742e+09</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>16777216.0</td>\n",
-       "      <td>0.975355</td>\n",
-       "      <td>0.024645</td>\n",
-       "      <td>0.300659</td>\n",
-       "      <td>0.699341</td>\n",
-       "      <td>0.334966</td>\n",
-       "      <td>0.419780</td>\n",
-       "      <td>0.042976</td>\n",
-       "      <td>-0.032175</td>\n",
-       "      <td>0.056800</td>\n",
-       "      <td>0.009510</td>\n",
-       "      <td>0.251558</td>\n",
-       "      <td>0.388889</td>\n",
-       "      <td>0.275526</td>\n",
-       "      <td>0.084028</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.186494</td>\n",
+       "      <td>0.368687</td>\n",
+       "      <td>0.331337</td>\n",
+       "      <td>0.113481</td>\n",
+       "      <td>0.998489</td>\n",
+       "      <td>0.001511</td>\n",
+       "      <td>0.348729</td>\n",
+       "      <td>0.651271</td>\n",
+       "      <td>0.343237</td>\n",
+       "      <td>0.351991</td>\n",
+       "      <td>0.049307</td>\n",
+       "      <td>0.019792</td>\n",
+       "      <td>-0.031746</td>\n",
+       "      <td>0.010044</td>\n",
        "      <td>1</td>\n",
-       "      <td>1819.339375</td>\n",
-       "      <td>0.006794</td>\n",
+       "      <td>1.961185e+12</td>\n",
+       "      <td>5267980.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>500.0</td>\n",
+       "      <td>50.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>201326592.0</td>\n",
+       "      <td>1.073742e+09</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>16777216.0</td>\n",
-       "      <td>0.968277</td>\n",
-       "      <td>0.031723</td>\n",
-       "      <td>0.199241</td>\n",
-       "      <td>0.409907</td>\n",
-       "      <td>0.370585</td>\n",
-       "      <td>0.020268</td>\n",
-       "      <td>0.329247</td>\n",
-       "      <td>0.670753</td>\n",
-       "      <td>0.241821</td>\n",
-       "      <td>0.360460</td>\n",
-       "      <td>0.035780</td>\n",
-       "      <td>0.029286</td>\n",
-       "      <td>-0.041489</td>\n",
-       "      <td>0.003567</td>\n",
-       "      <td>Base_Seq</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.143311</td>\n",
+       "      <td>0.529034</td>\n",
+       "      <td>0.212929</td>\n",
+       "      <td>0.114726</td>\n",
+       "      <td>0.995477</td>\n",
+       "      <td>0.004523</td>\n",
+       "      <td>0.365615</td>\n",
+       "      <td>0.634385</td>\n",
+       "      <td>0.513736</td>\n",
+       "      <td>0.088737</td>\n",
+       "      <td>0.065878</td>\n",
+       "      <td>-0.007246</td>\n",
+       "      <td>0.041214</td>\n",
+       "      <td>0.018624</td>\n",
+       "      <td>RAJAPerf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>1</td>\n",
-       "      <td>1795.491419</td>\n",
-       "      <td>0.007088</td>\n",
+       "      <td>1.915458e+12</td>\n",
+       "      <td>4721047.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>500.0</td>\n",
+       "      <td>50.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>201326592.0</td>\n",
+       "      <td>1.073742e+09</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>16777216.0</td>\n",
-       "      <td>0.966137</td>\n",
-       "      <td>0.033863</td>\n",
-       "      <td>0.246603</td>\n",
-       "      <td>0.753397</td>\n",
-       "      <td>0.340004</td>\n",
-       "      <td>0.480677</td>\n",
-       "      <td>0.059050</td>\n",
-       "      <td>-0.104797</td>\n",
-       "      <td>0.020943</td>\n",
-       "      <td>0.010510</td>\n",
-       "      <td>0.151999</td>\n",
-       "      <td>0.364938</td>\n",
-       "      <td>0.432142</td>\n",
-       "      <td>0.050920</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.141780</td>\n",
+       "      <td>0.449865</td>\n",
+       "      <td>0.348741</td>\n",
+       "      <td>0.059614</td>\n",
+       "      <td>0.997968</td>\n",
+       "      <td>0.002032</td>\n",
+       "      <td>0.365873</td>\n",
+       "      <td>0.634127</td>\n",
+       "      <td>0.418861</td>\n",
+       "      <td>0.299957</td>\n",
+       "      <td>0.056975</td>\n",
+       "      <td>0.036200</td>\n",
+       "      <td>-0.035784</td>\n",
+       "      <td>0.019623</td>\n",
        "      <td>1</td>\n",
-       "      <td>1815.025167</td>\n",
-       "      <td>0.006930</td>\n",
+       "      <td>1.969487e+12</td>\n",
+       "      <td>5398135.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>500.0</td>\n",
+       "      <td>50.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>201326592.0</td>\n",
+       "      <td>1.073742e+09</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>16777216.0</td>\n",
-       "      <td>0.973584</td>\n",
-       "      <td>0.026416</td>\n",
-       "      <td>0.133949</td>\n",
-       "      <td>0.544286</td>\n",
-       "      <td>0.292846</td>\n",
-       "      <td>0.028919</td>\n",
-       "      <td>0.470574</td>\n",
-       "      <td>0.529426</td>\n",
-       "      <td>0.440375</td>\n",
-       "      <td>0.302979</td>\n",
-       "      <td>0.060758</td>\n",
-       "      <td>0.020334</td>\n",
-       "      <td>-0.016642</td>\n",
-       "      <td>0.011380</td>\n",
-       "      <td>Base_Seq</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.148556</td>\n",
+       "      <td>0.275991</td>\n",
+       "      <td>0.545178</td>\n",
+       "      <td>0.030275</td>\n",
+       "      <td>0.996387</td>\n",
+       "      <td>0.003613</td>\n",
+       "      <td>0.215725</td>\n",
+       "      <td>0.784275</td>\n",
+       "      <td>0.233717</td>\n",
+       "      <td>0.378046</td>\n",
+       "      <td>0.028347</td>\n",
+       "      <td>-0.001815</td>\n",
+       "      <td>0.031521</td>\n",
+       "      <td>0.005584</td>\n",
+       "      <td>RAJAPerf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>1</td>\n",
-       "      <td>1805.198708</td>\n",
-       "      <td>0.006911</td>\n",
+       "      <td>1.907182e+12</td>\n",
+       "      <td>4809500.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>500.0</td>\n",
+       "      <td>50.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>201326592.0</td>\n",
+       "      <td>1.073742e+09</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>16777216.0</td>\n",
-       "      <td>0.969197</td>\n",
-       "      <td>0.030803</td>\n",
-       "      <td>0.574299</td>\n",
-       "      <td>0.425701</td>\n",
-       "      <td>0.452492</td>\n",
-       "      <td>0.542196</td>\n",
-       "      <td>0.061744</td>\n",
-       "      <td>0.105365</td>\n",
-       "      <td>-0.092654</td>\n",
-       "      <td>0.007572</td>\n",
-       "      <td>0.252677</td>\n",
-       "      <td>0.643161</td>\n",
-       "      <td>0.001353</td>\n",
-       "      <td>0.102809</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.150530</td>\n",
+       "      <td>0.435022</td>\n",
+       "      <td>0.378585</td>\n",
+       "      <td>0.035864</td>\n",
+       "      <td>0.997490</td>\n",
+       "      <td>0.002510</td>\n",
+       "      <td>0.380999</td>\n",
+       "      <td>0.619001</td>\n",
+       "      <td>0.392043</td>\n",
+       "      <td>0.276041</td>\n",
+       "      <td>0.040892</td>\n",
+       "      <td>0.100913</td>\n",
+       "      <td>-0.012559</td>\n",
+       "      <td>0.017446</td>\n",
        "      <td>1</td>\n",
-       "      <td>1817.831788</td>\n",
-       "      <td>0.007016</td>\n",
+       "      <td>1.956718e+12</td>\n",
+       "      <td>5477763.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>500.0</td>\n",
+       "      <td>50.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>201326592.0</td>\n",
+       "      <td>1.073742e+09</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>16777216.0</td>\n",
-       "      <td>0.978050</td>\n",
-       "      <td>0.021950</td>\n",
-       "      <td>0.153022</td>\n",
-       "      <td>0.537300</td>\n",
-       "      <td>0.275903</td>\n",
-       "      <td>0.033775</td>\n",
-       "      <td>0.455921</td>\n",
-       "      <td>0.544079</td>\n",
-       "      <td>0.491536</td>\n",
-       "      <td>0.347057</td>\n",
-       "      <td>0.081223</td>\n",
-       "      <td>0.049478</td>\n",
-       "      <td>-0.144951</td>\n",
-       "      <td>0.018449</td>\n",
-       "      <td>Base_Seq</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.145425</td>\n",
+       "      <td>0.326163</td>\n",
+       "      <td>0.439998</td>\n",
+       "      <td>0.088415</td>\n",
+       "      <td>0.998929</td>\n",
+       "      <td>0.001071</td>\n",
+       "      <td>0.341158</td>\n",
+       "      <td>0.658842</td>\n",
+       "      <td>0.341132</td>\n",
+       "      <td>0.212532</td>\n",
+       "      <td>0.044887</td>\n",
+       "      <td>0.038539</td>\n",
+       "      <td>-0.019006</td>\n",
+       "      <td>0.007392</td>\n",
+       "      <td>RAJAPerf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>1</td>\n",
-       "      <td>1807.504658</td>\n",
-       "      <td>0.007204</td>\n",
+       "      <td>1.913321e+12</td>\n",
+       "      <td>4814351.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>500.0</td>\n",
+       "      <td>50.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>201326592.0</td>\n",
+       "      <td>1.073742e+09</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>16777216.0</td>\n",
-       "      <td>0.968343</td>\n",
-       "      <td>0.031657</td>\n",
-       "      <td>0.635900</td>\n",
-       "      <td>0.364100</td>\n",
-       "      <td>0.535118</td>\n",
-       "      <td>0.505700</td>\n",
-       "      <td>0.081133</td>\n",
-       "      <td>0.050643</td>\n",
-       "      <td>-0.098516</td>\n",
-       "      <td>0.015062</td>\n",
-       "      <td>0.419837</td>\n",
-       "      <td>0.703930</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.068883</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.172806</td>\n",
+       "      <td>0.481297</td>\n",
+       "      <td>0.264612</td>\n",
+       "      <td>0.081284</td>\n",
+       "      <td>0.998236</td>\n",
+       "      <td>0.001764</td>\n",
+       "      <td>0.434392</td>\n",
+       "      <td>0.565608</td>\n",
+       "      <td>0.384583</td>\n",
+       "      <td>0.212336</td>\n",
+       "      <td>0.050262</td>\n",
+       "      <td>0.049129</td>\n",
+       "      <td>-0.028625</td>\n",
+       "      <td>0.012247</td>\n",
        "      <td>1</td>\n",
-       "      <td>1810.714631</td>\n",
-       "      <td>0.006808</td>\n",
+       "      <td>1.935555e+12</td>\n",
+       "      <td>5268973.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>500.0</td>\n",
+       "      <td>50.0</td>\n",
        "      <td>8388608.0</td>\n",
-       "      <td>201326592.0</td>\n",
+       "      <td>1.073742e+09</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>16777216.0</td>\n",
-       "      <td>0.966899</td>\n",
-       "      <td>0.033101</td>\n",
-       "      <td>0.182825</td>\n",
-       "      <td>0.569800</td>\n",
-       "      <td>0.191780</td>\n",
-       "      <td>0.055595</td>\n",
-       "      <td>0.540990</td>\n",
-       "      <td>0.459010</td>\n",
-       "      <td>0.378505</td>\n",
-       "      <td>0.413123</td>\n",
-       "      <td>0.048652</td>\n",
-       "      <td>-0.018178</td>\n",
-       "      <td>0.051825</td>\n",
-       "      <td>0.004293</td>\n",
-       "      <td>Base_Seq</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.197519</td>\n",
+       "      <td>0.516548</td>\n",
+       "      <td>0.173009</td>\n",
+       "      <td>0.112924</td>\n",
+       "      <td>0.996392</td>\n",
+       "      <td>0.003608</td>\n",
+       "      <td>0.437671</td>\n",
+       "      <td>0.562329</td>\n",
+       "      <td>0.628368</td>\n",
+       "      <td>0.257576</td>\n",
+       "      <td>0.088260</td>\n",
+       "      <td>0.135000</td>\n",
+       "      <td>-0.150171</td>\n",
+       "      <td>0.025721</td>\n",
+       "      <td>RAJAPerf</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                 Clang               \\\n",
-       "                                                   nid         time   \n",
-       "node                                     profile                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0           1  1816.892754   \n",
-       "                                         1           1  1807.026719   \n",
-       "                                         2           1  1795.491419   \n",
-       "                                         3           1  1805.198708   \n",
-       "                                         4           1  1807.504658   \n",
+       "                                                 Clang                \\\n",
+       "                                                   nid          time   \n",
+       "node                                     profile                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0           1  1.905843e+12   \n",
+       "                                         1           1  1.910519e+12   \n",
+       "                                         2           1  1.915458e+12   \n",
+       "                                         3           1  1.907182e+12   \n",
+       "                                         4           1  1.913321e+12   \n",
        "\n",
-       "                                                                         \\\n",
-       "                                                 time (exc) ProblemSize   \n",
-       "node                                     profile                          \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0         0.007212   8388608.0   \n",
-       "                                         1         0.007288   8388608.0   \n",
-       "                                         2         0.007088   8388608.0   \n",
-       "                                         3         0.006911   8388608.0   \n",
-       "                                         4         0.007204   8388608.0   \n",
+       "                                                                               \\\n",
+       "                                                 time (exc) ProblemSize  Reps   \n",
+       "node                                     profile                                \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0        4729399.0   8388608.0  50.0   \n",
+       "                                         1        4716924.0   8388608.0  50.0   \n",
+       "                                         2        4721047.0   8388608.0  50.0   \n",
+       "                                         3        4809500.0   8388608.0  50.0   \n",
+       "                                         4        4814351.0   8388608.0  50.0   \n",
+       "\n",
+       "                                                                               \\\n",
+       "                                                 Iterations/Rep     Bytes/Rep   \n",
+       "node                                     profile                                \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0            8388608.0  1.073742e+09   \n",
+       "                                         1            8388608.0  1.073742e+09   \n",
+       "                                         2            8388608.0  1.073742e+09   \n",
+       "                                         3            8388608.0  1.073742e+09   \n",
+       "                                         4            8388608.0  1.073742e+09   \n",
        "\n",
        "                                                                        \\\n",
-       "                                                   Reps Iterations/Rep   \n",
+       "                                                 Kernels/Rep Flops/Rep   \n",
        "node                                     profile                         \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0        500.0      8388608.0   \n",
-       "                                         1        500.0      8388608.0   \n",
-       "                                         2        500.0      8388608.0   \n",
-       "                                         3        500.0      8388608.0   \n",
-       "                                         4        500.0      8388608.0   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0               1.0       0.0   \n",
+       "                                         1               1.0       0.0   \n",
+       "                                         2               1.0       0.0   \n",
+       "                                         3               1.0       0.0   \n",
+       "                                         4               1.0       0.0   \n",
        "\n",
        "                                                                           \\\n",
-       "                                                    Bytes/Rep Kernels/Rep   \n",
+       "                                                  Retiring Frontend bound   \n",
        "node                                     profile                            \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0        201326592.0         1.0   \n",
-       "                                         1        201326592.0         1.0   \n",
-       "                                         2        201326592.0         1.0   \n",
-       "                                         3        201326592.0         1.0   \n",
-       "                                         4        201326592.0         1.0   \n",
-       "\n",
-       "                                                              \\\n",
-       "                                                   Flops/Rep   \n",
-       "node                                     profile               \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0        16777216.0   \n",
-       "                                         1        16777216.0   \n",
-       "                                         2        16777216.0   \n",
-       "                                         3        16777216.0   \n",
-       "                                         4        16777216.0   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0        0.106787       0.398106   \n",
+       "                                         1        0.186494       0.368687   \n",
+       "                                         2        0.141780       0.449865   \n",
+       "                                         3        0.150530       0.435022   \n",
+       "                                         4        0.172806       0.481297   \n",
+       "\n",
+       "                                                                \\\n",
+       "                                                 Backend bound   \n",
+       "node                                     profile                 \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0            0.421387   \n",
+       "                                         1            0.331337   \n",
+       "                                         2            0.348741   \n",
+       "                                         3            0.378585   \n",
+       "                                         4            0.264612   \n",
+       "\n",
+       "                                                                  \\\n",
+       "                                                 Bad speculation   \n",
+       "node                                     profile                   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0              0.073720   \n",
+       "                                         1              0.113481   \n",
+       "                                         2              0.059614   \n",
+       "                                         3              0.035864   \n",
+       "                                         4              0.081284   \n",
        "\n",
        "                                                                    \\\n",
        "                                                 Branch mispredict   \n",
        "node                                     profile                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0                0.979491   \n",
-       "                                         1                0.975355   \n",
-       "                                         2                0.966137   \n",
-       "                                         3                0.969197   \n",
-       "                                         4                0.968343   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0                0.996068   \n",
+       "                                         1                0.998489   \n",
+       "                                         2                0.997968   \n",
+       "                                         3                0.997490   \n",
+       "                                         4                0.998236   \n",
        "\n",
        "                                                                 \\\n",
        "                                                 Machine clears   \n",
        "node                                     profile                  \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0             0.020509   \n",
-       "                                         1             0.024645   \n",
-       "                                         2             0.033863   \n",
-       "                                         3             0.030803   \n",
-       "                                         4             0.031657   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0             0.003932   \n",
+       "                                         1             0.001511   \n",
+       "                                         2             0.002032   \n",
+       "                                         3             0.002510   \n",
+       "                                         4             0.001764   \n",
        "\n",
        "                                                                   \\\n",
        "                                                 Frontend latency   \n",
        "node                                     profile                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0               0.337277   \n",
-       "                                         1               0.300659   \n",
-       "                                         2               0.246603   \n",
-       "                                         3               0.574299   \n",
-       "                                         4               0.635900   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0               0.378715   \n",
+       "                                         1               0.348729   \n",
+       "                                         2               0.365873   \n",
+       "                                         3               0.380999   \n",
+       "                                         4               0.434392   \n",
        "\n",
        "                                                                     \\\n",
        "                                                 Frontend bandwidth   \n",
        "node                                     profile                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0                 0.662723   \n",
-       "                                         1                 0.699341   \n",
-       "                                         2                 0.753397   \n",
-       "                                         3                 0.425701   \n",
-       "                                         4                 0.364100   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0                 0.621285   \n",
+       "                                         1                 0.651271   \n",
+       "                                         2                 0.634127   \n",
+       "                                         3                 0.619001   \n",
+       "                                         4                 0.565608   \n",
        "\n",
        "                                                                          \\\n",
        "                                                 Memory bound Core bound   \n",
        "node                                     profile                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0           0.479179   0.135116   \n",
-       "                                         1           0.334966   0.419780   \n",
-       "                                         2           0.340004   0.480677   \n",
-       "                                         3           0.452492   0.542196   \n",
-       "                                         4           0.535118   0.505700   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0           0.321128   0.462123   \n",
+       "                                         1           0.343237   0.351991   \n",
+       "                                         2           0.418861   0.299957   \n",
+       "                                         3           0.392043   0.276041   \n",
+       "                                         4           0.384583   0.212336   \n",
        "\n",
        "                                                                            \\\n",
        "                                                 External Memory  L1 bound   \n",
        "node                                     profile                             \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0              0.057817  0.078872   \n",
-       "                                         1              0.042976 -0.032175   \n",
-       "                                         2              0.059050 -0.104797   \n",
-       "                                         3              0.061744  0.105365   \n",
-       "                                         4              0.081133  0.050643   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0              0.057753 -0.107304   \n",
+       "                                         1              0.049307  0.019792   \n",
+       "                                         2              0.056975  0.036200   \n",
+       "                                         3              0.040892  0.100913   \n",
+       "                                         4              0.050262  0.049129   \n",
+       "\n",
+       "                                                                     GCC  \\\n",
+       "                                                  L2 bound  L3 bound nid   \n",
+       "node                                     profile                           \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0        0.008284  0.015874   1   \n",
+       "                                         1       -0.031746  0.010044   1   \n",
+       "                                         2       -0.035784  0.019623   1   \n",
+       "                                         3       -0.012559  0.017446   1   \n",
+       "                                         4       -0.028625  0.012247   1   \n",
        "\n",
-       "                                                                      \\\n",
-       "                                                  L2 bound  L3 bound   \n",
-       "node                                     profile                       \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0       -0.029453  0.025042   \n",
-       "                                         1        0.056800  0.009510   \n",
-       "                                         2        0.020943  0.010510   \n",
-       "                                         3       -0.092654  0.007572   \n",
-       "                                         4       -0.098516  0.015062   \n",
+       "                                                                           \\\n",
+       "                                                          time time (exc)   \n",
+       "node                                     profile                            \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0        1.947811e+12  5238545.0   \n",
+       "                                         1        1.961185e+12  5267980.0   \n",
+       "                                         2        1.969487e+12  5398135.0   \n",
+       "                                         3        1.956718e+12  5477763.0   \n",
+       "                                         4        1.935555e+12  5268973.0   \n",
+       "\n",
+       "                                                                    \\\n",
+       "                                                 ProblemSize  Reps   \n",
+       "node                                     profile                     \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0         8388608.0  50.0   \n",
+       "                                         1         8388608.0  50.0   \n",
+       "                                         2         8388608.0  50.0   \n",
+       "                                         3         8388608.0  50.0   \n",
+       "                                         4         8388608.0  50.0   \n",
+       "\n",
+       "                                                                               \\\n",
+       "                                                 Iterations/Rep     Bytes/Rep   \n",
+       "node                                     profile                                \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0            8388608.0  1.073742e+09   \n",
+       "                                         1            8388608.0  1.073742e+09   \n",
+       "                                         2            8388608.0  1.073742e+09   \n",
+       "                                         3            8388608.0  1.073742e+09   \n",
+       "                                         4            8388608.0  1.073742e+09   \n",
+       "\n",
+       "                                                                        \\\n",
+       "                                                 Kernels/Rep Flops/Rep   \n",
+       "node                                     profile                         \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0               1.0       0.0   \n",
+       "                                         1               1.0       0.0   \n",
+       "                                         2               1.0       0.0   \n",
+       "                                         3               1.0       0.0   \n",
+       "                                         4               1.0       0.0   \n",
        "\n",
        "                                                                           \\\n",
        "                                                  Retiring Frontend bound   \n",
        "node                                     profile                            \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0        0.283309       0.524702   \n",
-       "                                         1        0.251558       0.388889   \n",
-       "                                         2        0.151999       0.364938   \n",
-       "                                         3        0.252677       0.643161   \n",
-       "                                         4        0.419837       0.703930   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0        0.141325       0.304490   \n",
+       "                                         1        0.143311       0.529034   \n",
+       "                                         2        0.148556       0.275991   \n",
+       "                                         3        0.145425       0.326163   \n",
+       "                                         4        0.197519       0.516548   \n",
        "\n",
        "                                                                \\\n",
        "                                                 Backend bound   \n",
        "node                                     profile                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0            0.224746   \n",
-       "                                         1            0.275526   \n",
-       "                                         2            0.432142   \n",
-       "                                         3            0.001353   \n",
-       "                                         4            0.000000   \n",
-       "\n",
-       "                                                                 GCC  \\\n",
-       "                                                 Bad speculation nid   \n",
-       "node                                     profile                       \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0              0.000000   1   \n",
-       "                                         1              0.084028   1   \n",
-       "                                         2              0.050920   1   \n",
-       "                                         3              0.102809   1   \n",
-       "                                         4              0.068883   1   \n",
-       "\n",
-       "                                                                          \\\n",
-       "                                                         time time (exc)   \n",
-       "node                                     profile                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0        1822.338783   0.006938   \n",
-       "                                         1        1819.339375   0.006794   \n",
-       "                                         2        1815.025167   0.006930   \n",
-       "                                         3        1817.831788   0.007016   \n",
-       "                                         4        1810.714631   0.006808   \n",
-       "\n",
-       "                                                                     \\\n",
-       "                                                 ProblemSize   Reps   \n",
-       "node                                     profile                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0         8388608.0  500.0   \n",
-       "                                         1         8388608.0  500.0   \n",
-       "                                         2         8388608.0  500.0   \n",
-       "                                         3         8388608.0  500.0   \n",
-       "                                         4         8388608.0  500.0   \n",
-       "\n",
-       "                                                                              \\\n",
-       "                                                 Iterations/Rep    Bytes/Rep   \n",
-       "node                                     profile                               \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0            8388608.0  201326592.0   \n",
-       "                                         1            8388608.0  201326592.0   \n",
-       "                                         2            8388608.0  201326592.0   \n",
-       "                                         3            8388608.0  201326592.0   \n",
-       "                                         4            8388608.0  201326592.0   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0            0.491996   \n",
+       "                                         1            0.212929   \n",
+       "                                         2            0.545178   \n",
+       "                                         3            0.439998   \n",
+       "                                         4            0.173009   \n",
        "\n",
-       "                                                                          \\\n",
-       "                                                 Kernels/Rep   Flops/Rep   \n",
-       "node                                     profile                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0               1.0  16777216.0   \n",
-       "                                         1               1.0  16777216.0   \n",
-       "                                         2               1.0  16777216.0   \n",
-       "                                         3               1.0  16777216.0   \n",
-       "                                         4               1.0  16777216.0   \n",
+       "                                                                  \\\n",
+       "                                                 Bad speculation   \n",
+       "node                                     profile                   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0              0.062190   \n",
+       "                                         1              0.114726   \n",
+       "                                         2              0.030275   \n",
+       "                                         3              0.088415   \n",
+       "                                         4              0.112924   \n",
        "\n",
        "                                                                    \\\n",
        "                                                 Branch mispredict   \n",
        "node                                     profile                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0                0.966496   \n",
-       "                                         1                0.968277   \n",
-       "                                         2                0.973584   \n",
-       "                                         3                0.978050   \n",
-       "                                         4                0.966899   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0                0.998104   \n",
+       "                                         1                0.995477   \n",
+       "                                         2                0.996387   \n",
+       "                                         3                0.998929   \n",
+       "                                         4                0.996392   \n",
        "\n",
-       "                                                                           \\\n",
-       "                                                 Machine clears  Retiring   \n",
-       "node                                     profile                            \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0             0.033504  0.147419   \n",
-       "                                         1             0.031723  0.199241   \n",
-       "                                         2             0.026416  0.133949   \n",
-       "                                         3             0.021950  0.153022   \n",
-       "                                         4             0.033101  0.182825   \n",
-       "\n",
-       "                                                                               \\\n",
-       "                                                 Frontend bound Backend bound   \n",
-       "node                                     profile                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0             0.327396      0.446536   \n",
-       "                                         1             0.409907      0.370585   \n",
-       "                                         2             0.544286      0.292846   \n",
-       "                                         3             0.537300      0.275903   \n",
-       "                                         4             0.569800      0.191780   \n",
-       "\n",
-       "                                                                  \\\n",
-       "                                                 Bad speculation   \n",
-       "node                                     profile                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0              0.078649   \n",
-       "                                         1              0.020268   \n",
-       "                                         2              0.028919   \n",
-       "                                         3              0.033775   \n",
-       "                                         4              0.055595   \n",
+       "                                                                 \\\n",
+       "                                                 Machine clears   \n",
+       "node                                     profile                  \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0             0.001896   \n",
+       "                                         1             0.004523   \n",
+       "                                         2             0.003613   \n",
+       "                                         3             0.001071   \n",
+       "                                         4             0.003608   \n",
        "\n",
        "                                                                   \\\n",
        "                                                 Frontend latency   \n",
        "node                                     profile                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0               0.227622   \n",
-       "                                         1               0.329247   \n",
-       "                                         2               0.470574   \n",
-       "                                         3               0.455921   \n",
-       "                                         4               0.540990   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0               0.256295   \n",
+       "                                         1               0.365615   \n",
+       "                                         2               0.215725   \n",
+       "                                         3               0.341158   \n",
+       "                                         4               0.437671   \n",
        "\n",
        "                                                                     \\\n",
        "                                                 Frontend bandwidth   \n",
        "node                                     profile                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0                 0.772378   \n",
-       "                                         1                 0.670753   \n",
-       "                                         2                 0.529426   \n",
-       "                                         3                 0.544079   \n",
-       "                                         4                 0.459010   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0                 0.743705   \n",
+       "                                         1                 0.634385   \n",
+       "                                         2                 0.784275   \n",
+       "                                         3                 0.658842   \n",
+       "                                         4                 0.562329   \n",
        "\n",
        "                                                                          \\\n",
        "                                                 Memory bound Core bound   \n",
        "node                                     profile                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0           0.264838   0.519253   \n",
-       "                                         1           0.241821   0.360460   \n",
-       "                                         2           0.440375   0.302979   \n",
-       "                                         3           0.491536   0.347057   \n",
-       "                                         4           0.378505   0.413123   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0           0.378793   0.343142   \n",
+       "                                         1           0.513736   0.088737   \n",
+       "                                         2           0.233717   0.378046   \n",
+       "                                         3           0.341132   0.212532   \n",
+       "                                         4           0.628368   0.257576   \n",
        "\n",
        "                                                                            \\\n",
        "                                                 External Memory  L1 bound   \n",
        "node                                     profile                             \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0              0.043300 -0.106758   \n",
-       "                                         1              0.035780  0.029286   \n",
-       "                                         2              0.060758  0.020334   \n",
-       "                                         3              0.081223  0.049478   \n",
-       "                                         4              0.048652 -0.018178   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'} 0              0.054910 -0.072312   \n",
+       "                                         1              0.065878 -0.007246   \n",
+       "                                         2              0.028347 -0.001815   \n",
+       "                                         3              0.044887  0.038539   \n",
+       "                                         4              0.088260  0.135000   \n",
        "\n",
        "                                                                          name  \n",
        "                                                  L2 bound  L3 bound            \n",
        "node                                     profile                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'} 0        0.059585  0.008914  Base_Seq  \n",
-       "                                         1       -0.041489  0.003567  Base_Seq  \n",
-       "                                         2       -0.016642  0.011380  Base_Seq  \n",
-       "                                         3       -0.144951  0.018449  Base_Seq  \n",
-       "                                         4        0.051825  0.004293  Base_Seq  "
+       "{'name': 'RAJAPerf', 'type': 'function'} 0        0.051793  0.014941  RAJAPerf  \n",
+       "                                         1        0.041214  0.018624  RAJAPerf  \n",
+       "                                         2        0.031521  0.005584  RAJAPerf  \n",
+       "                                         3       -0.019006  0.007392  RAJAPerf  \n",
+       "                                         4       -0.150171  0.025721  RAJAPerf  "
       ]
      },
      "execution_count": 4,
@@ -1206,13 +1206,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "862d0951",
+   "id": "563d8d59",
    "metadata": {
     "papermill": {
-     "duration": 0.004935,
-     "end_time": "2023-12-09T20:58:44.855918",
+     "duration": 0.007143,
+     "end_time": "2023-12-22T05:36:09.485590",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.850983",
+     "start_time": "2023-12-22T05:36:09.478447",
      "status": "completed"
     },
     "tags": []
@@ -1225,13 +1225,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "256f5beb",
+   "id": "10edaef1",
    "metadata": {
     "papermill": {
-     "duration": 0.004837,
-     "end_time": "2023-12-09T20:58:44.865558",
+     "duration": 0.005448,
+     "end_time": "2023-12-22T05:36:09.496536",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.860721",
+     "start_time": "2023-12-22T05:36:09.491088",
      "status": "completed"
     },
     "tags": []
@@ -1242,13 +1242,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d857c79",
+   "id": "781c31bc",
    "metadata": {
     "papermill": {
-     "duration": 0.004864,
-     "end_time": "2023-12-09T20:58:44.875499",
+     "duration": 0.005395,
+     "end_time": "2023-12-22T05:36:09.507212",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.870635",
+     "start_time": "2023-12-22T05:36:09.501817",
      "status": "completed"
     },
     "tags": []
@@ -1262,13 +1262,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "88b6626a",
+   "id": "301d6e43",
    "metadata": {
     "papermill": {
-     "duration": 0.004894,
-     "end_time": "2023-12-09T20:58:44.885414",
+     "duration": 0.005325,
+     "end_time": "2023-12-22T05:36:09.517843",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.880520",
+     "start_time": "2023-12-22T05:36:09.512518",
      "status": "completed"
     },
     "tags": []
@@ -1280,19 +1280,19 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "447545e7",
+   "id": "72b39cd1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:44.895733Z",
-     "iopub.status.busy": "2023-12-09T20:58:44.895632Z",
-     "iopub.status.idle": "2023-12-09T20:58:44.897188Z",
-     "shell.execute_reply": "2023-12-09T20:58:44.896979Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.528907Z",
+     "iopub.status.busy": "2023-12-22T05:36:09.528749Z",
+     "iopub.status.idle": "2023-12-22T05:36:09.530720Z",
+     "shell.execute_reply": "2023-12-22T05:36:09.530302Z"
     },
     "papermill": {
-     "duration": 0.007475,
-     "end_time": "2023-12-09T20:58:44.897681",
+     "duration": 0.008211,
+     "end_time": "2023-12-22T05:36:09.531369",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.890206",
+     "start_time": "2023-12-22T05:36:09.523158",
      "status": "completed"
     },
     "tags": []
@@ -1306,19 +1306,19 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "f2ddf328",
+   "id": "bcff3f24",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:44.907814Z",
-     "iopub.status.busy": "2023-12-09T20:58:44.907728Z",
-     "iopub.status.idle": "2023-12-09T20:58:44.938014Z",
-     "shell.execute_reply": "2023-12-09T20:58:44.937758Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.544313Z",
+     "iopub.status.busy": "2023-12-22T05:36:09.544181Z",
+     "iopub.status.idle": "2023-12-22T05:36:09.563712Z",
+     "shell.execute_reply": "2023-12-22T05:36:09.563417Z"
     },
     "papermill": {
-     "duration": 0.035974,
-     "end_time": "2023-12-09T20:58:44.938529",
+     "duration": 0.026117,
+     "end_time": "2023-12-22T05:36:09.564318",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.902555",
+     "start_time": "2023-12-22T05:36:09.538201",
      "status": "completed"
     },
     "tags": []
@@ -1368,63 +1368,63 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.044086</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>0.003932</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.045977</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>0.019851</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>0.997907</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>0.998909</td>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>0.997923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.973024</td>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>0.996756</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                        name  \\\n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                    name  \\\n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                    time (exc)_max  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.511030e+08   \n",
        "\n",
        "                                                    Machine clears_max  \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.044086  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.045977  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              1.000000  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.998909  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.973024  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.003932  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.019851  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.997907  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.997923  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.996756  "
       ]
      },
      "execution_count": 6,
@@ -1440,13 +1440,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8e291a5",
+   "id": "6230a7f6",
    "metadata": {
     "papermill": {
-     "duration": 0.005086,
-     "end_time": "2023-12-09T20:58:44.948790",
+     "duration": 0.006065,
+     "end_time": "2023-12-22T05:36:09.575988",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.943704",
+     "start_time": "2023-12-22T05:36:09.569923",
      "status": "completed"
     },
     "tags": []
@@ -1462,19 +1462,19 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "6c8d8dce",
+   "id": "a7bd5c7c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:44.959405Z",
-     "iopub.status.busy": "2023-12-09T20:58:44.959300Z",
-     "iopub.status.idle": "2023-12-09T20:58:44.960911Z",
-     "shell.execute_reply": "2023-12-09T20:58:44.960711Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.587698Z",
+     "iopub.status.busy": "2023-12-22T05:36:09.587576Z",
+     "iopub.status.idle": "2023-12-22T05:36:09.589556Z",
+     "shell.execute_reply": "2023-12-22T05:36:09.589282Z"
     },
     "papermill": {
-     "duration": 0.007588,
-     "end_time": "2023-12-09T20:58:44.961435",
+     "duration": 0.008512,
+     "end_time": "2023-12-22T05:36:09.590145",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.953847",
+     "start_time": "2023-12-22T05:36:09.581633",
      "status": "completed"
     },
     "tags": []
@@ -1487,19 +1487,19 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "09a001c7",
+   "id": "51a54826",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:44.972216Z",
-     "iopub.status.busy": "2023-12-09T20:58:44.972131Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.008682Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.008421Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.605170Z",
+     "iopub.status.busy": "2023-12-22T05:36:09.605050Z",
+     "iopub.status.idle": "2023-12-22T05:36:09.628614Z",
+     "shell.execute_reply": "2023-12-22T05:36:09.628302Z"
     },
     "papermill": {
-     "duration": 0.042517,
-     "end_time": "2023-12-09T20:58:45.009167",
+     "duration": 0.030566,
+     "end_time": "2023-12-22T05:36:09.629296",
      "exception": false,
-     "start_time": "2023-12-09T20:58:44.966650",
+     "start_time": "2023-12-22T05:36:09.598730",
      "status": "completed"
     },
     "tags": []
@@ -1559,66 +1559,66 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.034919</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>0.004523</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.078947</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>0.014205</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>0.992988</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>0.998832</td>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>0.997550</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.978771</td>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>0.994624</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                              \\\n",
-       "                                                                        name   \n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                          \\\n",
+       "                                                                    name   \n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                            Clang  \\\n",
        "                                                   time (exc)_max   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.511030e+08   \n",
        "\n",
        "                                                                  GCC  \n",
        "                                                   Machine clears_max  \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.034919  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.078947  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             1.000000  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.998832  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.978771  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.004523  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.014205  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.992988  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.997550  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.994624  "
       ]
      },
      "execution_count": 8,
@@ -1633,13 +1633,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21aa6789",
+   "id": "9efa3600",
    "metadata": {
     "papermill": {
-     "duration": 0.005476,
-     "end_time": "2023-12-09T20:58:45.019941",
+     "duration": 0.008366,
+     "end_time": "2023-12-22T05:36:09.643548",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.014465",
+     "start_time": "2023-12-22T05:36:09.635182",
      "status": "completed"
     },
     "tags": []
@@ -1654,13 +1654,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b19ecd0",
+   "id": "62fdc70f",
    "metadata": {
     "papermill": {
-     "duration": 0.005251,
-     "end_time": "2023-12-09T20:58:45.030250",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:45.024999",
+     "start_time": "2023-12-22T05:36:09.649944",
      "status": "completed"
     },
     "tags": []
@@ -1672,19 +1672,19 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "548794f9",
+   "id": "04cace0b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.041087Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.040993Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.042621Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.042333Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.667838Z",
+     "iopub.status.busy": "2023-12-22T05:36:09.667709Z",
+     "iopub.status.idle": "2023-12-22T05:36:09.669807Z",
+     "shell.execute_reply": "2023-12-22T05:36:09.669433Z"
     },
     "papermill": {
-     "duration": 0.007761,
-     "end_time": "2023-12-09T20:58:45.043171",
+     "duration": 0.009058,
+     "end_time": "2023-12-22T05:36:09.670564",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.035410",
+     "start_time": "2023-12-22T05:36:09.661506",
      "status": "completed"
     },
     "tags": []
@@ -1697,19 +1697,19 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "86c396c1",
+   "id": "a6de71c8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.065138Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.065036Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.098089Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.097871Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.694857Z",
+     "iopub.status.busy": "2023-12-22T05:36:09.694727Z",
+     "iopub.status.idle": "2023-12-22T05:36:09.712989Z",
+     "shell.execute_reply": "2023-12-22T05:36:09.712706Z"
     },
     "papermill": {
-     "duration": 0.039212,
-     "end_time": "2023-12-09T20:58:45.098608",
+     "duration": 0.024804,
+     "end_time": "2023-12-22T05:36:09.713556",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.059396",
+     "start_time": "2023-12-22T05:36:09.688752",
      "status": "completed"
     },
     "tags": []
@@ -1763,89 +1763,89 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.044086</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.015849</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>0.003932</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>0.001099</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.045977</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.004092</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>0.019851</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>0.997907</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>0.990401</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>0.998909</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.987818</td>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>0.997923</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>0.988505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.973024</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.906853</td>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>0.996756</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>0.954967</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                        name  \\\n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                    name  \\\n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                    time (exc)_max  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.511030e+08   \n",
        "\n",
        "                                                    Machine clears_max  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.044086   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.045977   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.998909   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.973024   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.003932   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.019851   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.997907   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.997923   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.996756   \n",
        "\n",
        "                                                    time (exc)_min  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.467119e+08   \n",
        "\n",
        "                                                    Machine clears_min  \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.015849  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.004092  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.000000  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.987818  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.906853  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.001099  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.000000  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.990401  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.988505  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.954967  "
       ]
      },
      "execution_count": 10,
@@ -1860,13 +1860,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c0145c1",
+   "id": "f93a3e67",
    "metadata": {
     "papermill": {
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-     "end_time": "2023-12-09T20:58:45.109308",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:45.104087",
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      "status": "completed"
     },
     "tags": []
@@ -1882,19 +1882,19 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "7250fb98",
+   "id": "82f30925",
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2023-12-09T20:58:45.121838Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.737880Z",
+     "iopub.status.busy": "2023-12-22T05:36:09.737736Z",
+     "iopub.status.idle": "2023-12-22T05:36:09.739698Z",
+     "shell.execute_reply": "2023-12-22T05:36:09.739443Z"
     },
     "papermill": {
-     "duration": 0.008055,
-     "end_time": "2023-12-09T20:58:45.122593",
+     "duration": 0.00884,
+     "end_time": "2023-12-22T05:36:09.740244",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.114538",
+     "start_time": "2023-12-22T05:36:09.731404",
      "status": "completed"
     },
     "tags": []
@@ -1907,19 +1907,19 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "e26392f0",
+   "id": "21c4c164",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.133554Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.133469Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.166383Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.166152Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.752181Z",
+     "iopub.status.busy": "2023-12-22T05:36:09.752055Z",
+     "iopub.status.idle": "2023-12-22T05:36:09.775970Z",
+     "shell.execute_reply": "2023-12-22T05:36:09.775716Z"
     },
     "papermill": {
-     "duration": 0.039073,
-     "end_time": "2023-12-09T20:58:45.166888",
+     "duration": 0.030608,
+     "end_time": "2023-12-22T05:36:09.776544",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.127815",
+     "start_time": "2023-12-22T05:36:09.745936",
      "status": "completed"
     },
     "tags": []
@@ -1983,94 +1983,94 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.034919</td>\n",
-       "      <td>0.014810</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>0.004523</td>\n",
+       "      <td>0.001071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.078947</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>0.014205</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>0.992988</td>\n",
+       "      <td>0.956393</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.998832</td>\n",
-       "      <td>0.993177</td>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>0.997550</td>\n",
+       "      <td>0.980433</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.978771</td>\n",
-       "      <td>0.905950</td>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>0.994624</td>\n",
+       "      <td>0.770469</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                              \\\n",
-       "                                                                        name   \n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                          \\\n",
+       "                                                                    name   \n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                            Clang  \\\n",
        "                                                   time (exc)_max   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.511030e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_min   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.467119e+08   \n",
        "\n",
        "                                                                  GCC  \\\n",
        "                                                   Machine clears_max   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.034919   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.078947   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.998832   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.978771   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.004523   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.014205   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.992988   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.997550   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.994624   \n",
        "\n",
        "                                                                       \n",
        "                                                   Machine clears_min  \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.014810  \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001071  \n",
        "{'name': 'Algorithm', 'type': 'function'}                    0.000000  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000000  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.993177  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.905950  "
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.956393  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.980433  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.770469  "
       ]
      },
      "execution_count": 12,
@@ -2085,13 +2085,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f973e139",
+   "id": "38d983f0",
    "metadata": {
     "papermill": {
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-     "end_time": "2023-12-09T20:58:45.177759",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:45.172422",
+     "start_time": "2023-12-22T05:36:09.782668",
      "status": "completed"
     },
     "tags": []
@@ -2106,13 +2106,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "739b7c16",
+   "id": "27659e1b",
    "metadata": {
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-     "end_time": "2023-12-09T20:58:45.188682",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:45.183078",
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      "status": "completed"
     },
     "tags": []
@@ -2124,19 +2124,19 @@
   {
    "cell_type": "code",
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-   "id": "ffa29abd",
+   "id": "54f6d630",
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2023-12-09T20:58:45.201191Z"
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     },
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-     "duration": 0.007889,
-     "end_time": "2023-12-09T20:58:45.201952",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:45.194063",
+     "start_time": "2023-12-22T05:36:09.806230",
      "status": "completed"
     },
     "tags": []
@@ -2149,19 +2149,19 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "751e277c",
+   "id": "72b1880c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.213097Z",
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-     "shell.execute_reply": "2023-12-09T20:58:45.248019Z"
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     },
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-     "duration": 0.041537,
-     "end_time": "2023-12-09T20:58:45.248842",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:45.207305",
+     "start_time": "2023-12-22T05:36:09.820962",
      "status": "completed"
     },
     "tags": []
@@ -2219,115 +2219,115 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.044086</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.015849</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.027724</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>0.003932</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>0.001099</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>0.002209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.045977</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.004092</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.012778</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>0.019851</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>0.004722</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>0.998909</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.987818</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>0.998147</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.973024</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.906853</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.953007</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>0.997907</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>0.990401</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>0.996143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>0.997923</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>0.988505</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>0.993871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>0.996756</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>0.954967</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>0.990938</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                        name  \\\n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                    name  \\\n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                    time (exc)_max  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.511030e+08   \n",
        "\n",
        "                                                    Machine clears_max  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.044086   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.045977   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.998909   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.973024   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.003932   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.019851   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.997907   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.997923   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.996756   \n",
        "\n",
        "                                                    time (exc)_min  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.467119e+08   \n",
        "\n",
        "                                                    Machine clears_min  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.015849   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.004092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.987818   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.906853   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.001099   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.000000   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.990401   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.988505   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.954967   \n",
        "\n",
        "                                                    time (exc)_median  \\\n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                 4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}         2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...       4.498193e+08   \n",
        "\n",
        "                                                    Machine clears_median  \n",
        "node                                                                       \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                         0.027724  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                        0.012778  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      NaN  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...               0.998147  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...               0.953007  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                         0.002209  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                        0.004722  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                 0.996143  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                 0.993871  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...               0.990938  "
       ]
      },
      "execution_count": 14,
@@ -2342,13 +2342,13 @@
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      "exception": false,
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@@ -2364,19 +2364,19 @@
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    "cell_type": "code",
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-   "id": "addd1f3a",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:45.265864",
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@@ -2389,19 +2389,19 @@
   {
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-   "id": "739e61e4",
+   "id": "887c773a",
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+     "iopub.status.busy": "2023-12-22T05:36:09.894482Z",
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+     "shell.execute_reply": "2023-12-22T05:36:09.923711Z"
     },
     "papermill": {
-     "duration": 0.045028,
-     "end_time": "2023-12-09T20:58:45.324678",
+     "duration": 0.040568,
+     "end_time": "2023-12-22T05:36:09.924818",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.279650",
+     "start_time": "2023-12-22T05:36:09.884250",
      "status": "completed"
     },
     "tags": []
@@ -2412,7 +2412,13 @@
      "output_type": "stream",
      "text": [
       "/opt/conda/lib/python3.9/site-packages/thicket/stats/median.py:44: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
-      "  for node in pd.unique(thicket.dataframe.reset_index()[\"node\"].tolist()):\n",
+      "  for node in pd.unique(thicket.dataframe.reset_index()[\"node\"].tolist()):\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/opt/conda/lib/python3.9/site-packages/thicket/stats/median.py:44: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
       "  for node in pd.unique(thicket.dataframe.reset_index()[\"node\"].tolist()):\n"
      ]
@@ -2469,122 +2475,122 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.034919</td>\n",
-       "      <td>0.026362</td>\n",
-       "      <td>0.014810</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>0.004523</td>\n",
+       "      <td>0.003061</td>\n",
+       "      <td>0.001071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.078947</td>\n",
-       "      <td>0.012092</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>0.014205</td>\n",
+       "      <td>0.004810</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.027067</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.998832</td>\n",
-       "      <td>0.996824</td>\n",
-       "      <td>0.993177</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.978771</td>\n",
-       "      <td>0.949658</td>\n",
-       "      <td>0.905950</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>0.992988</td>\n",
+       "      <td>0.984356</td>\n",
+       "      <td>0.956393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>0.997550</td>\n",
+       "      <td>0.991464</td>\n",
+       "      <td>0.980433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>0.994624</td>\n",
+       "      <td>0.925987</td>\n",
+       "      <td>0.770469</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                              \\\n",
-       "                                                                        name   \n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                          \\\n",
+       "                                                                    name   \n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                            Clang  \\\n",
        "                                                   time (exc)_max   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.511030e+08   \n",
        "\n",
        "                                                                      \\\n",
        "                                                   time (exc)_median   \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                    0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                   0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...          1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...          0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}               5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...      4.498193e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_min   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.467119e+08   \n",
        "\n",
        "                                                                  GCC  \\\n",
        "                                                   Machine clears_max   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.034919   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.078947   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.998832   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.978771   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.004523   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.014205   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.992988   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.997550   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.994624   \n",
        "\n",
        "                                                                          \\\n",
        "                                                   Machine clears_median   \n",
        "node                                                                       \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                        0.026362   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                       0.012092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.027067   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...              0.996824   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...              0.949658   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        0.003061   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       0.004810   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.984356   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                0.991464   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...              0.925987   \n",
        "\n",
        "                                                                       \n",
        "                                                   Machine clears_min  \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.014810  \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001071  \n",
        "{'name': 'Algorithm', 'type': 'function'}                    0.000000  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000000  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.993177  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.905950  "
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.956393  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.980433  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.770469  "
       ]
      },
      "execution_count": 16,
@@ -2599,13 +2605,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ea5628f6",
+   "id": "d7409065",
    "metadata": {
     "papermill": {
-     "duration": 0.005513,
-     "end_time": "2023-12-09T20:58:45.335947",
+     "duration": 0.006199,
+     "end_time": "2023-12-22T05:36:09.937742",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.330434",
+     "start_time": "2023-12-22T05:36:09.931543",
      "status": "completed"
     },
     "tags": []
@@ -2620,13 +2626,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a459d70",
+   "id": "bfef18ac",
    "metadata": {
     "papermill": {
-     "duration": 0.005802,
-     "end_time": "2023-12-09T20:58:45.347587",
+     "duration": 0.006138,
+     "end_time": "2023-12-22T05:36:09.950485",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.341785",
+     "start_time": "2023-12-22T05:36:09.944347",
      "status": "completed"
     },
     "tags": []
@@ -2638,19 +2644,19 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "ea50bd53",
+   "id": "ed3d94c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.359562Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.359464Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.361127Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.360849Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.963636Z",
+     "iopub.status.busy": "2023-12-22T05:36:09.963514Z",
+     "iopub.status.idle": "2023-12-22T05:36:09.965654Z",
+     "shell.execute_reply": "2023-12-22T05:36:09.965283Z"
     },
     "papermill": {
-     "duration": 0.008225,
-     "end_time": "2023-12-09T20:58:45.361603",
+     "duration": 0.009417,
+     "end_time": "2023-12-22T05:36:09.966298",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.353378",
+     "start_time": "2023-12-22T05:36:09.956881",
      "status": "completed"
     },
     "tags": []
@@ -2663,19 +2669,19 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "7aaab3c9",
+   "id": "208d3fe3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.373533Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.373442Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.410545Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.410311Z"
+     "iopub.execute_input": "2023-12-22T05:36:09.979074Z",
+     "iopub.status.busy": "2023-12-22T05:36:09.978971Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.005257Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.004921Z"
     },
     "papermill": {
-     "duration": 0.043637,
-     "end_time": "2023-12-09T20:58:45.411037",
+     "duration": 0.033436,
+     "end_time": "2023-12-22T05:36:10.005995",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.367400",
+     "start_time": "2023-12-22T05:36:09.972559",
      "status": "completed"
     },
     "tags": []
@@ -2737,141 +2743,141 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.044086</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.015849</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.027724</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.027126</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>0.003932</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>0.001099</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>0.002209</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>0.002266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.045977</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.004092</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.012778</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.017969</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>0.019851</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>0.004722</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>0.007061</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.230749</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>0.998909</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.987818</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>0.998147</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>0.996256</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.973024</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.906853</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.953007</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.948031</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>0.997907</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>0.990401</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>0.996143</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>0.995425</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>0.997923</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>0.988505</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>0.993871</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>0.993798</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>0.996756</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>0.954967</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>0.990938</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>0.981222</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                        name  \\\n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                    name  \\\n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                    time (exc)_max  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.511030e+08   \n",
        "\n",
        "                                                    Machine clears_max  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.044086   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.045977   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.998909   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.973024   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.003932   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.019851   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.997907   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.997923   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.996756   \n",
        "\n",
        "                                                    time (exc)_min  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.467119e+08   \n",
        "\n",
        "                                                    Machine clears_min  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.015849   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.004092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.987818   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.906853   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.001099   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.000000   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.990401   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.988505   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.954967   \n",
        "\n",
        "                                                    time (exc)_median  \\\n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                 4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}         2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...       4.498193e+08   \n",
        "\n",
        "                                                    Machine clears_median  \\\n",
        "node                                                                        \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                         0.027724   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                        0.012778   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...               0.998147   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...               0.953007   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                         0.002209   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                        0.004722   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                 0.996143   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                 0.993871   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...               0.990938   \n",
        "\n",
        "                                                    time (exc)_mean  \\\n",
        "node                                                                  \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                   0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                  0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}           0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...         1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...         0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}               4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}              5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}       1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}       2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     4.492754e+08   \n",
        "\n",
        "                                                    Machine clears_mean  \n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                       0.027126  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                      0.017969  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.230749  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...             0.996256  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...             0.948031  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                       0.002266  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      0.007061  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.995425  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}               0.993798  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...             0.981222  "
       ]
      },
      "execution_count": 18,
@@ -2886,13 +2892,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9140c1ba",
+   "id": "772a1f9f",
    "metadata": {
     "papermill": {
-     "duration": 0.005714,
-     "end_time": "2023-12-09T20:58:45.422741",
+     "duration": 0.006424,
+     "end_time": "2023-12-22T05:36:10.019176",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.417027",
+     "start_time": "2023-12-22T05:36:10.012752",
      "status": "completed"
     },
     "tags": []
@@ -2908,19 +2914,19 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "149cbf87",
+   "id": "8190ad3a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.434953Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.434857Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.436482Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.436264Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.032235Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.032111Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.034104Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.033745Z"
     },
     "papermill": {
-     "duration": 0.008348,
-     "end_time": "2023-12-09T20:58:45.436934",
+     "duration": 0.009454,
+     "end_time": "2023-12-22T05:36:10.034854",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.428586",
+     "start_time": "2023-12-22T05:36:10.025400",
      "status": "completed"
     },
     "tags": []
@@ -2933,19 +2939,19 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "dff88921",
+   "id": "1fc3766b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.448820Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.448728Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.489525Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.489259Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.048031Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.047928Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.073612Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.073180Z"
     },
     "papermill": {
-     "duration": 0.047269,
-     "end_time": "2023-12-09T20:58:45.490047",
+     "duration": 0.032973,
+     "end_time": "2023-12-22T05:36:10.074251",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.442778",
+     "start_time": "2023-12-22T05:36:10.041278",
      "status": "completed"
     },
     "tags": []
@@ -3017,150 +3023,150 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.034919</td>\n",
-       "      <td>0.027250</td>\n",
-       "      <td>0.026362</td>\n",
-       "      <td>0.014810</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>0.004523</td>\n",
+       "      <td>0.002853</td>\n",
+       "      <td>0.003061</td>\n",
+       "      <td>0.001071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.078947</td>\n",
-       "      <td>0.019933</td>\n",
-       "      <td>0.012092</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>0.014205</td>\n",
+       "      <td>0.005249</td>\n",
+       "      <td>0.004810</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.311366</td>\n",
-       "      <td>0.027067</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.998832</td>\n",
-       "      <td>0.996608</td>\n",
-       "      <td>0.996824</td>\n",
-       "      <td>0.993177</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.978771</td>\n",
-       "      <td>0.944047</td>\n",
-       "      <td>0.949658</td>\n",
-       "      <td>0.905950</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>0.992988</td>\n",
+       "      <td>0.978926</td>\n",
+       "      <td>0.984356</td>\n",
+       "      <td>0.956393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>0.997550</td>\n",
+       "      <td>0.990934</td>\n",
+       "      <td>0.991464</td>\n",
+       "      <td>0.980433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>0.994624</td>\n",
+       "      <td>0.922818</td>\n",
+       "      <td>0.925987</td>\n",
+       "      <td>0.770469</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                              \\\n",
-       "                                                                        name   \n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                          \\\n",
+       "                                                                    name   \n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                            Clang  \\\n",
        "                                                   time (exc)_max   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.511030e+08   \n",
        "\n",
        "                                                                    \\\n",
        "                                                   time (exc)_mean   \n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.492754e+08   \n",
        "\n",
        "                                                                      \\\n",
        "                                                   time (exc)_median   \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                    0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                   0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...          1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...          0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}               5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...      4.498193e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_min   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.467119e+08   \n",
        "\n",
        "                                                                  GCC  \\\n",
        "                                                   Machine clears_max   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.034919   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.078947   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.998832   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.978771   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.004523   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.014205   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.992988   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.997550   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.994624   \n",
        "\n",
        "                                                                        \\\n",
        "                                                   Machine clears_mean   \n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.027250   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.019933   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.311366   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.996608   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.944047   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.002853   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.005249   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.978926   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.990934   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.922818   \n",
        "\n",
        "                                                                          \\\n",
        "                                                   Machine clears_median   \n",
        "node                                                                       \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                        0.026362   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                       0.012092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.027067   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...              0.996824   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...              0.949658   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        0.003061   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       0.004810   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.984356   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                0.991464   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...              0.925987   \n",
        "\n",
        "                                                                       \n",
        "                                                   Machine clears_min  \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.014810  \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001071  \n",
        "{'name': 'Algorithm', 'type': 'function'}                    0.000000  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000000  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.993177  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.905950  "
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.956393  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.980433  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.770469  "
       ]
      },
      "execution_count": 20,
@@ -3175,13 +3181,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cea0b57a",
+   "id": "dd0410fb",
    "metadata": {
     "papermill": {
-     "duration": 0.005747,
-     "end_time": "2023-12-09T20:58:45.501868",
+     "duration": 0.006728,
+     "end_time": "2023-12-22T05:36:10.089373",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.496121",
+     "start_time": "2023-12-22T05:36:10.082645",
      "status": "completed"
     },
     "tags": []
@@ -3196,13 +3202,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aec79da5",
+   "id": "196def1e",
    "metadata": {
     "papermill": {
-     "duration": 0.006106,
-     "end_time": "2023-12-09T20:58:45.514004",
+     "duration": 0.006512,
+     "end_time": "2023-12-22T05:36:10.102465",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.507898",
+     "start_time": "2023-12-22T05:36:10.095953",
      "status": "completed"
     },
     "tags": []
@@ -3214,19 +3220,19 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "b0b5825e",
+   "id": "4cd4c4a5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.526557Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.526460Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.528030Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.527834Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.116261Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.116132Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.118074Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.117721Z"
     },
     "papermill": {
-     "duration": 0.008473,
-     "end_time": "2023-12-09T20:58:45.528498",
+     "duration": 0.00953,
+     "end_time": "2023-12-22T05:36:10.118728",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.520025",
+     "start_time": "2023-12-22T05:36:10.109198",
      "status": "completed"
     },
     "tags": []
@@ -3239,19 +3245,19 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "d3772bd1",
+   "id": "19196dcb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.540916Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.540821Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.578497Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.578207Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.132236Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.132123Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.156913Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.156491Z"
     },
     "papermill": {
-     "duration": 0.044398,
-     "end_time": "2023-12-09T20:58:45.578993",
+     "duration": 0.032533,
+     "end_time": "2023-12-22T05:36:10.157636",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.534595",
+     "start_time": "2023-12-22T05:36:10.125103",
      "status": "completed"
     },
     "tags": []
@@ -3317,167 +3323,167 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.044086</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.015849</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.027724</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.027126</td>\n",
-       "      <td>2.549456e-08</td>\n",
-       "      <td>0.000072</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>0.003932</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>0.001099</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>0.002209</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>0.002266</td>\n",
+       "      <td>4.788476e+09</td>\n",
+       "      <td>8.279246e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.045977</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.004092</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.012778</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.017969</td>\n",
-       "      <td>3.652100e-10</td>\n",
-       "      <td>0.000220</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>0.019851</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>0.004722</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>0.007061</td>\n",
+       "      <td>1.595913e+08</td>\n",
+       "      <td>3.895210e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.230749</td>\n",
-       "      <td>1.560100e-10</td>\n",
-       "      <td>0.169282</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>0.998909</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.987818</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>0.998147</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>0.996256</td>\n",
-       "      <td>3.125018e-04</td>\n",
-       "      <td>0.000012</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.973024</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.906853</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.953007</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.948031</td>\n",
-       "      <td>6.953802e-05</td>\n",
-       "      <td>0.000392</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>0.997907</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>0.990401</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>0.996143</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>0.995425</td>\n",
+       "      <td>8.299723e+13</td>\n",
+       "      <td>4.977776e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>0.997923</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>0.988505</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>0.993871</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>0.993798</td>\n",
+       "      <td>8.054587e+13</td>\n",
+       "      <td>8.699840e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>0.996756</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>0.954967</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>0.990938</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>0.981222</td>\n",
+       "      <td>2.198205e+12</td>\n",
+       "      <td>2.352473e-04</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                        name  \\\n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                    name  \\\n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                    time (exc)_max  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.511030e+08   \n",
        "\n",
        "                                                    Machine clears_max  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.044086   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.045977   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.998909   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.973024   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.003932   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.019851   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.997907   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.997923   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.996756   \n",
        "\n",
        "                                                    time (exc)_min  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.467119e+08   \n",
        "\n",
        "                                                    Machine clears_min  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.015849   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.004092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.987818   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.906853   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.001099   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.000000   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.990401   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.988505   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.954967   \n",
        "\n",
        "                                                    time (exc)_median  \\\n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                 4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}         2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...       4.498193e+08   \n",
        "\n",
        "                                                    Machine clears_median  \\\n",
        "node                                                                        \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                         0.027724   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                        0.012778   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...               0.998147   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...               0.953007   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                         0.002209   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                        0.004722   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                 0.996143   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                 0.993871   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...               0.990938   \n",
        "\n",
        "                                                    time (exc)_mean  \\\n",
        "node                                                                  \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                   0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                  0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}           0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...         1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...         0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}               4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}              5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}       1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}       2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     4.492754e+08   \n",
        "\n",
        "                                                    Machine clears_mean  \\\n",
        "node                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                       0.027126   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                      0.017969   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.230749   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...             0.996256   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...             0.948031   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                       0.002266   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      0.007061   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.995425   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}               0.993798   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...             0.981222   \n",
        "\n",
        "                                                    time (exc)_var  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}              2.549456e-08   \n",
-       "{'name': 'Algorithm', 'type': 'function'}             3.652100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.560100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...    3.125018e-04   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...    6.953802e-05   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788476e+09   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             1.595913e+08   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      8.299723e+13   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      8.054587e+13   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    2.198205e+12   \n",
        "\n",
        "                                                    Machine clears_var  \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.000072  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.000220  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.169282  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.000012  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.000392  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                  8.279246e-07  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                 3.895210e-05  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          4.977776e-06  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}          8.699840e-06  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...        2.352473e-04  "
       ]
      },
      "execution_count": 22,
@@ -3492,13 +3498,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff2c30b1",
+   "id": "e1372ff8",
    "metadata": {
     "papermill": {
-     "duration": 0.006049,
-     "end_time": "2023-12-09T20:58:45.591314",
+     "duration": 0.006588,
+     "end_time": "2023-12-22T05:36:10.171395",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.585265",
+     "start_time": "2023-12-22T05:36:10.164807",
      "status": "completed"
     },
     "tags": []
@@ -3514,19 +3520,19 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "242f8d91",
+   "id": "ad5ede2f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.603990Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.603896Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.605566Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.605355Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.189802Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.189675Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.191881Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.191493Z"
     },
     "papermill": {
-     "duration": 0.008655,
-     "end_time": "2023-12-09T20:58:45.606039",
+     "duration": 0.010439,
+     "end_time": "2023-12-22T05:36:10.192654",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.597384",
+     "start_time": "2023-12-22T05:36:10.182215",
      "status": "completed"
     },
     "tags": []
@@ -3539,19 +3545,19 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "bf96aaea",
+   "id": "45781cf7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.619025Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.618933Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.661507Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.661218Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.206807Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.206692Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.236369Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.235991Z"
     },
     "papermill": {
-     "duration": 0.049575,
-     "end_time": "2023-12-09T20:58:45.661999",
+     "duration": 0.037413,
+     "end_time": "2023-12-22T05:36:10.237018",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.612424",
+     "start_time": "2023-12-22T05:36:10.199605",
      "status": "completed"
     },
     "tags": []
@@ -3627,178 +3633,178 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>2.549456e-08</td>\n",
-       "      <td>0.034919</td>\n",
-       "      <td>0.027250</td>\n",
-       "      <td>0.026362</td>\n",
-       "      <td>0.014810</td>\n",
-       "      <td>0.000035</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>4.788476e+09</td>\n",
+       "      <td>0.004523</td>\n",
+       "      <td>0.002853</td>\n",
+       "      <td>0.003061</td>\n",
+       "      <td>0.001071</td>\n",
+       "      <td>0.000001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>3.652100e-10</td>\n",
-       "      <td>0.078947</td>\n",
-       "      <td>0.019933</td>\n",
-       "      <td>0.012092</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>1.595913e+08</td>\n",
+       "      <td>0.014205</td>\n",
+       "      <td>0.005249</td>\n",
+       "      <td>0.004810</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000463</td>\n",
+       "      <td>0.000018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>1.560100e-10</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.311366</td>\n",
-       "      <td>0.027067</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.203608</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>3.125018e-04</td>\n",
-       "      <td>0.998832</td>\n",
-       "      <td>0.996608</td>\n",
-       "      <td>0.996824</td>\n",
-       "      <td>0.993177</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>6.953802e-05</td>\n",
-       "      <td>0.978771</td>\n",
-       "      <td>0.944047</td>\n",
-       "      <td>0.949658</td>\n",
-       "      <td>0.905950</td>\n",
-       "      <td>0.000503</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>8.299723e+13</td>\n",
+       "      <td>0.992988</td>\n",
+       "      <td>0.978926</td>\n",
+       "      <td>0.984356</td>\n",
+       "      <td>0.956393</td>\n",
+       "      <td>0.000145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>8.054587e+13</td>\n",
+       "      <td>0.997550</td>\n",
+       "      <td>0.990934</td>\n",
+       "      <td>0.991464</td>\n",
+       "      <td>0.980433</td>\n",
+       "      <td>0.000026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>2.198205e+12</td>\n",
+       "      <td>0.994624</td>\n",
+       "      <td>0.922818</td>\n",
+       "      <td>0.925987</td>\n",
+       "      <td>0.770469</td>\n",
+       "      <td>0.004714</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                              \\\n",
-       "                                                                        name   \n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                          \\\n",
+       "                                                                    name   \n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                            Clang  \\\n",
        "                                                   time (exc)_max   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.511030e+08   \n",
        "\n",
        "                                                                    \\\n",
        "                                                   time (exc)_mean   \n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.492754e+08   \n",
        "\n",
        "                                                                      \\\n",
        "                                                   time (exc)_median   \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                    0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                   0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...          1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...          0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}               5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...      4.498193e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_min   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.467119e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_var   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}             2.549456e-08   \n",
-       "{'name': 'Algorithm', 'type': 'function'}            3.652100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.560100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...   3.125018e-04   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...   6.953802e-05   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.788476e+09   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            1.595913e+08   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     8.299723e+13   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     8.054587e+13   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   2.198205e+12   \n",
        "\n",
        "                                                                  GCC  \\\n",
        "                                                   Machine clears_max   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.034919   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.078947   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.998832   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.978771   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.004523   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.014205   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.992988   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.997550   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.994624   \n",
        "\n",
        "                                                                        \\\n",
        "                                                   Machine clears_mean   \n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.027250   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.019933   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.311366   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.996608   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.944047   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.002853   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.005249   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.978926   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.990934   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.922818   \n",
        "\n",
        "                                                                          \\\n",
        "                                                   Machine clears_median   \n",
        "node                                                                       \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                        0.026362   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                       0.012092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.027067   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...              0.996824   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...              0.949658   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        0.003061   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       0.004810   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.984356   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                0.991464   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...              0.925987   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_min   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.014810   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001071   \n",
        "{'name': 'Algorithm', 'type': 'function'}                    0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.993177   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.905950   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.956393   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.980433   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.770469   \n",
        "\n",
        "                                                                       \n",
        "                                                   Machine clears_var  \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.000035  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000463  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.203608  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.000003  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.000503  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.000001  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.000018  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000145  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.000026  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.004714  "
       ]
      },
      "execution_count": 24,
@@ -3813,13 +3819,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7fed2c1",
+   "id": "78a753da",
    "metadata": {
     "papermill": {
-     "duration": 0.006197,
-     "end_time": "2023-12-09T20:58:45.674597",
+     "duration": 0.00689,
+     "end_time": "2023-12-22T05:36:10.251202",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.668400",
+     "start_time": "2023-12-22T05:36:10.244312",
      "status": "completed"
     },
     "tags": []
@@ -3834,13 +3840,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e09fac7",
+   "id": "38214f1b",
    "metadata": {
     "papermill": {
-     "duration": 0.006368,
-     "end_time": "2023-12-09T20:58:45.687447",
+     "duration": 0.006868,
+     "end_time": "2023-12-22T05:36:10.264911",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.681079",
+     "start_time": "2023-12-22T05:36:10.258043",
      "status": "completed"
     },
     "tags": []
@@ -3852,19 +3858,19 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "0f9d825c",
+   "id": "1bab7777",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.700486Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.700384Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.701966Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.701766Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.279462Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.279341Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.281350Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.280914Z"
     },
     "papermill": {
-     "duration": 0.008748,
-     "end_time": "2023-12-09T20:58:45.702488",
+     "duration": 0.010197,
+     "end_time": "2023-12-22T05:36:10.282136",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.693740",
+     "start_time": "2023-12-22T05:36:10.271939",
      "status": "completed"
     },
     "tags": []
@@ -3877,19 +3883,19 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "d51e418c",
+   "id": "b0709b73",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.715599Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.715507Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.754355Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.754072Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.302527Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.302386Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.327148Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.326737Z"
     },
     "papermill": {
-     "duration": 0.04593,
-     "end_time": "2023-12-09T20:58:45.754875",
+     "duration": 0.03306,
+     "end_time": "2023-12-22T05:36:10.327732",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.708945",
+     "start_time": "2023-12-22T05:36:10.294672",
      "status": "completed"
     },
     "tags": []
@@ -3959,193 +3965,193 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.044086</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.015849</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.027724</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.027126</td>\n",
-       "      <td>2.549456e-08</td>\n",
-       "      <td>0.000072</td>\n",
-       "      <td>0.000160</td>\n",
-       "      <td>0.008508</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>0.003932</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>0.001099</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>0.002209</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>0.002266</td>\n",
+       "      <td>4.788476e+09</td>\n",
+       "      <td>8.279246e-07</td>\n",
+       "      <td>6.919881e+04</td>\n",
+       "      <td>0.000910</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.045977</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.004092</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.012778</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.017969</td>\n",
-       "      <td>3.652100e-10</td>\n",
-       "      <td>0.000220</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>0.014843</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>0.019851</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>0.004722</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>0.007061</td>\n",
+       "      <td>1.595913e+08</td>\n",
+       "      <td>3.895210e-05</td>\n",
+       "      <td>1.263294e+04</td>\n",
+       "      <td>0.006241</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.230749</td>\n",
-       "      <td>1.560100e-10</td>\n",
-       "      <td>0.169282</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.411439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>0.998909</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.987818</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>0.998147</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>0.996256</td>\n",
-       "      <td>3.125018e-04</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.017678</td>\n",
-       "      <td>0.003459</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.973024</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.906853</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.953007</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.948031</td>\n",
-       "      <td>6.953802e-05</td>\n",
-       "      <td>0.000392</td>\n",
-       "      <td>0.008339</td>\n",
-       "      <td>0.019788</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>0.997907</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>0.990401</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>0.996143</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>0.995425</td>\n",
+       "      <td>8.299723e+13</td>\n",
+       "      <td>4.977776e-06</td>\n",
+       "      <td>9.110282e+06</td>\n",
+       "      <td>0.002231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>0.997923</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>0.988505</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>0.993871</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>0.993798</td>\n",
+       "      <td>8.054587e+13</td>\n",
+       "      <td>8.699840e-06</td>\n",
+       "      <td>8.974735e+06</td>\n",
+       "      <td>0.002950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>0.996756</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>0.954967</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>0.990938</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>0.981222</td>\n",
+       "      <td>2.198205e+12</td>\n",
+       "      <td>2.352473e-04</td>\n",
+       "      <td>1.482635e+06</td>\n",
+       "      <td>0.015338</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                        name  \\\n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                    name  \\\n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                    time (exc)_max  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.511030e+08   \n",
        "\n",
        "                                                    Machine clears_max  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.044086   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.045977   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.998909   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.973024   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.003932   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.019851   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.997907   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.997923   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.996756   \n",
        "\n",
        "                                                    time (exc)_min  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.467119e+08   \n",
        "\n",
        "                                                    Machine clears_min  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.015849   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.004092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.987818   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.906853   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.001099   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.000000   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.990401   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.988505   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.954967   \n",
        "\n",
        "                                                    time (exc)_median  \\\n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                 4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}         2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...       4.498193e+08   \n",
        "\n",
        "                                                    Machine clears_median  \\\n",
        "node                                                                        \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                         0.027724   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                        0.012778   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...               0.998147   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...               0.953007   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                         0.002209   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                        0.004722   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                 0.996143   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                 0.993871   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...               0.990938   \n",
        "\n",
        "                                                    time (exc)_mean  \\\n",
        "node                                                                  \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                   0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                  0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}           0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...         1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...         0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}               4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}              5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}       1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}       2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     4.492754e+08   \n",
        "\n",
        "                                                    Machine clears_mean  \\\n",
        "node                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                       0.027126   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                      0.017969   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.230749   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...             0.996256   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...             0.948031   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                       0.002266   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      0.007061   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.995425   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}               0.993798   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...             0.981222   \n",
        "\n",
        "                                                    time (exc)_var  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}              2.549456e-08   \n",
-       "{'name': 'Algorithm', 'type': 'function'}             3.652100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.560100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...    3.125018e-04   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...    6.953802e-05   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788476e+09   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             1.595913e+08   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      8.299723e+13   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      8.054587e+13   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    2.198205e+12   \n",
        "\n",
        "                                                    Machine clears_var  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.000072   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.000220   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.169282   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.000392   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                  8.279246e-07   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                 3.895210e-05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          4.977776e-06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}          8.699840e-06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...        2.352473e-04   \n",
        "\n",
        "                                                    time (exc)_std  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.000160   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000019   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        0.017678   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.008339   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              6.919881e+04   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             1.263294e+04   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      9.110282e+06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      8.974735e+06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    1.482635e+06   \n",
        "\n",
        "                                                    Machine clears_std  \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.008508  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.014843  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.411439  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.003459  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.019788  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.000910  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.006241  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.002231  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.002950  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.015338  "
       ]
      },
      "execution_count": 26,
@@ -4160,13 +4166,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "362ee21d",
+   "id": "88335052",
    "metadata": {
     "papermill": {
-     "duration": 0.006609,
-     "end_time": "2023-12-09T20:58:45.768031",
+     "duration": 0.006959,
+     "end_time": "2023-12-22T05:36:10.342258",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.761422",
+     "start_time": "2023-12-22T05:36:10.335299",
      "status": "completed"
     },
     "tags": []
@@ -4182,19 +4188,19 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "6e165a8f",
+   "id": "6d328745",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.781393Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.781286Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.782927Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.782733Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.357464Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.357340Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.359299Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.358966Z"
     },
     "papermill": {
-     "duration": 0.008911,
-     "end_time": "2023-12-09T20:58:45.783432",
+     "duration": 0.010525,
+     "end_time": "2023-12-22T05:36:10.359928",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.774521",
+     "start_time": "2023-12-22T05:36:10.349403",
      "status": "completed"
     },
     "tags": []
@@ -4207,19 +4213,19 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "d47f3b66",
+   "id": "64afccb6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.796828Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.796736Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.840783Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.840533Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.374856Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.374757Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.412450Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.412055Z"
     },
     "papermill": {
-     "duration": 0.051298,
-     "end_time": "2023-12-09T20:58:45.841288",
+     "duration": 0.04569,
+     "end_time": "2023-12-22T05:36:10.413190",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.789990",
+     "start_time": "2023-12-22T05:36:10.367500",
      "status": "completed"
     },
     "tags": []
@@ -4299,206 +4305,206 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.000160</td>\n",
-       "      <td>2.549456e-08</td>\n",
-       "      <td>0.034919</td>\n",
-       "      <td>0.027250</td>\n",
-       "      <td>0.026362</td>\n",
-       "      <td>0.014810</td>\n",
-       "      <td>0.005898</td>\n",
-       "      <td>0.000035</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>6.919881e+04</td>\n",
+       "      <td>4.788476e+09</td>\n",
+       "      <td>0.004523</td>\n",
+       "      <td>0.002853</td>\n",
+       "      <td>0.003061</td>\n",
+       "      <td>0.001071</td>\n",
+       "      <td>0.001096</td>\n",
+       "      <td>0.000001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>3.652100e-10</td>\n",
-       "      <td>0.078947</td>\n",
-       "      <td>0.019933</td>\n",
-       "      <td>0.012092</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>1.263294e+04</td>\n",
+       "      <td>1.595913e+08</td>\n",
+       "      <td>0.014205</td>\n",
+       "      <td>0.005249</td>\n",
+       "      <td>0.004810</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.021524</td>\n",
-       "      <td>0.000463</td>\n",
+       "      <td>0.004187</td>\n",
+       "      <td>0.000018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>1.560100e-10</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.311366</td>\n",
-       "      <td>0.027067</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.451230</td>\n",
-       "      <td>0.203608</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.017678</td>\n",
-       "      <td>3.125018e-04</td>\n",
-       "      <td>0.998832</td>\n",
-       "      <td>0.996608</td>\n",
-       "      <td>0.996824</td>\n",
-       "      <td>0.993177</td>\n",
-       "      <td>0.001797</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.008339</td>\n",
-       "      <td>6.953802e-05</td>\n",
-       "      <td>0.978771</td>\n",
-       "      <td>0.944047</td>\n",
-       "      <td>0.949658</td>\n",
-       "      <td>0.905950</td>\n",
-       "      <td>0.022419</td>\n",
-       "      <td>0.000503</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>9.110282e+06</td>\n",
+       "      <td>8.299723e+13</td>\n",
+       "      <td>0.992988</td>\n",
+       "      <td>0.978926</td>\n",
+       "      <td>0.984356</td>\n",
+       "      <td>0.956393</td>\n",
+       "      <td>0.012040</td>\n",
+       "      <td>0.000145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>8.974735e+06</td>\n",
+       "      <td>8.054587e+13</td>\n",
+       "      <td>0.997550</td>\n",
+       "      <td>0.990934</td>\n",
+       "      <td>0.991464</td>\n",
+       "      <td>0.980433</td>\n",
+       "      <td>0.005125</td>\n",
+       "      <td>0.000026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>1.482635e+06</td>\n",
+       "      <td>2.198205e+12</td>\n",
+       "      <td>0.994624</td>\n",
+       "      <td>0.922818</td>\n",
+       "      <td>0.925987</td>\n",
+       "      <td>0.770469</td>\n",
+       "      <td>0.068656</td>\n",
+       "      <td>0.004714</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                              \\\n",
-       "                                                                        name   \n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                          \\\n",
+       "                                                                    name   \n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                            Clang  \\\n",
        "                                                   time (exc)_max   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.511030e+08   \n",
        "\n",
        "                                                                    \\\n",
        "                                                   time (exc)_mean   \n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.492754e+08   \n",
        "\n",
        "                                                                      \\\n",
        "                                                   time (exc)_median   \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                    0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                   0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...          1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...          0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}               5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...      4.498193e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_min   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.467119e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_std   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.000160   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000019   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       0.017678   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.008339   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             6.919881e+04   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            1.263294e+04   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     9.110282e+06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     8.974735e+06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   1.482635e+06   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_var   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}             2.549456e-08   \n",
-       "{'name': 'Algorithm', 'type': 'function'}            3.652100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.560100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...   3.125018e-04   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...   6.953802e-05   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.788476e+09   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            1.595913e+08   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     8.299723e+13   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     8.054587e+13   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   2.198205e+12   \n",
        "\n",
        "                                                                  GCC  \\\n",
        "                                                   Machine clears_max   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.034919   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.078947   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.998832   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.978771   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.004523   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.014205   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.992988   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.997550   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.994624   \n",
        "\n",
        "                                                                        \\\n",
        "                                                   Machine clears_mean   \n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.027250   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.019933   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.311366   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.996608   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.944047   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.002853   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.005249   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.978926   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.990934   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.922818   \n",
        "\n",
        "                                                                          \\\n",
        "                                                   Machine clears_median   \n",
        "node                                                                       \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                        0.026362   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                       0.012092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.027067   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...              0.996824   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...              0.949658   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        0.003061   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       0.004810   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.984356   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                0.991464   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...              0.925987   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_min   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.014810   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001071   \n",
        "{'name': 'Algorithm', 'type': 'function'}                    0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.993177   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.905950   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.956393   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.980433   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.770469   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_std   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.005898   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.021524   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.451230   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.001797   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.022419   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001096   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.004187   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.012040   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.005125   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.068656   \n",
        "\n",
        "                                                                       \n",
        "                                                   Machine clears_var  \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.000035  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000463  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.203608  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.000003  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.000503  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.000001  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.000018  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000145  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.000026  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.004714  "
       ]
      },
      "execution_count": 28,
@@ -4513,13 +4519,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4099af8",
+   "id": "b755e5e3",
    "metadata": {
     "papermill": {
-     "duration": 0.00659,
-     "end_time": "2023-12-09T20:58:45.854613",
+     "duration": 0.007579,
+     "end_time": "2023-12-22T05:36:10.428349",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.848023",
+     "start_time": "2023-12-22T05:36:10.420770",
      "status": "completed"
     },
     "tags": []
@@ -4538,13 +4544,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ccb5806e",
+   "id": "87f71df7",
    "metadata": {
     "papermill": {
-     "duration": 0.006687,
-     "end_time": "2023-12-09T20:58:45.867913",
+     "duration": 0.007401,
+     "end_time": "2023-12-22T05:36:10.443301",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.861226",
+     "start_time": "2023-12-22T05:36:10.435900",
      "status": "completed"
     },
     "tags": []
@@ -4556,19 +4562,19 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "85b88c35",
+   "id": "c1e29869",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.881878Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.881779Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.883409Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.883166Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.458558Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.458435Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.460622Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.460274Z"
     },
     "papermill": {
-     "duration": 0.009264,
-     "end_time": "2023-12-09T20:58:45.883871",
+     "duration": 0.010481,
+     "end_time": "2023-12-22T05:36:10.461276",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.874607",
+     "start_time": "2023-12-22T05:36:10.450795",
      "status": "completed"
     },
     "tags": []
@@ -4581,19 +4587,19 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "eb28fbe2",
+   "id": "4149db71",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.897558Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.897470Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.967954Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.967648Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.476180Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.476078Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.507109Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.506736Z"
     },
     "papermill": {
-     "duration": 0.077912,
-     "end_time": "2023-12-09T20:58:45.968529",
+     "duration": 0.039396,
+     "end_time": "2023-12-22T05:36:10.507932",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.890617",
+     "start_time": "2023-12-22T05:36:10.468536",
      "status": "completed"
     },
     "tags": []
@@ -4667,219 +4673,219 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.044086</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.015849</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.027724</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.027126</td>\n",
-       "      <td>2.549456e-08</td>\n",
-       "      <td>0.000072</td>\n",
-       "      <td>0.000160</td>\n",
-       "      <td>0.008508</td>\n",
-       "      <td>[0.0070557499999999995, 0.007194000000000001, ...</td>\n",
-       "      <td>[0.02044975, 0.027724, 0.032329]</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>0.003932</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>0.001099</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>0.002209</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>0.002266</td>\n",
+       "      <td>4.788476e+09</td>\n",
+       "      <td>8.279246e-07</td>\n",
+       "      <td>6.919881e+04</td>\n",
+       "      <td>0.000910</td>\n",
+       "      <td>[4723135.0, 4792513.5, 4834294.25]</td>\n",
+       "      <td>[0.00157425, 0.002209, 0.002657]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.045977</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.004092</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.012778</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.017969</td>\n",
-       "      <td>3.652100e-10</td>\n",
-       "      <td>0.000220</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>0.014843</td>\n",
-       "      <td>[0.00058425, 0.000598, 0.000603]</td>\n",
-       "      <td>[0.00833475, 0.0127785, 0.019187000000000003]</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>0.019851</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>0.004722</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>0.007061</td>\n",
+       "      <td>1.595913e+08</td>\n",
+       "      <td>3.895210e-05</td>\n",
+       "      <td>1.263294e+04</td>\n",
+       "      <td>0.006241</td>\n",
+       "      <td>[533120.25, 538624.5, 545153.5]</td>\n",
+       "      <td>[0.0025005, 0.0047220000000000005, 0.00878175]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.230749</td>\n",
-       "      <td>1.560100e-10</td>\n",
-       "      <td>0.169282</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.411439</td>\n",
-       "      <td>[0.00022325, 0.0002315, 0.000243]</td>\n",
-       "      <td>[nan, nan, nan]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>0.998909</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.987818</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>0.998147</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>0.996256</td>\n",
-       "      <td>3.125018e-04</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.017678</td>\n",
-       "      <td>0.003459</td>\n",
-       "      <td>[1.29070175, 1.2940295000000002, 1.30458350000...</td>\n",
-       "      <td>[0.99564675, 0.9981475, 0.998563]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.973024</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.906853</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.953007</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.948031</td>\n",
-       "      <td>6.953802e-05</td>\n",
-       "      <td>0.000392</td>\n",
-       "      <td>0.008339</td>\n",
-       "      <td>0.019788</td>\n",
-       "      <td>[0.734351, 0.7368325, 0.74190975]</td>\n",
-       "      <td>[0.940767, 0.9530069999999999, 0.9630295]</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>0.997907</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>0.990401</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>0.996143</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>0.995425</td>\n",
+       "      <td>8.299723e+13</td>\n",
+       "      <td>4.977776e-06</td>\n",
+       "      <td>9.110282e+06</td>\n",
+       "      <td>0.002231</td>\n",
+       "      <td>[1988997772.0, 2001817999.5, 2004877745.25]</td>\n",
+       "      <td>[0.99408025, 0.9961425, 0.9970844999999999]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>0.997923</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>0.988505</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>0.993871</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>0.993798</td>\n",
+       "      <td>8.054587e+13</td>\n",
+       "      <td>8.699840e-06</td>\n",
+       "      <td>8.974735e+06</td>\n",
+       "      <td>0.002950</td>\n",
+       "      <td>[2014797196.0, 2018832431.0, 2027420716.75]</td>\n",
+       "      <td>[0.99133675, 0.993871, 0.9959795]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>0.996756</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>0.954967</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>0.990938</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>0.981222</td>\n",
+       "      <td>2.198205e+12</td>\n",
+       "      <td>2.352473e-04</td>\n",
+       "      <td>1.482635e+06</td>\n",
+       "      <td>0.015338</td>\n",
+       "      <td>[448020898.75, 449819291.0, 450391779.5]</td>\n",
+       "      <td>[0.9703815, 0.990938, 0.99332575]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                        name  \\\n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                    name  \\\n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                    time (exc)_max  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.511030e+08   \n",
        "\n",
        "                                                    Machine clears_max  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.044086   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.045977   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.998909   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.973024   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.003932   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.019851   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.997907   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.997923   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.996756   \n",
        "\n",
        "                                                    time (exc)_min  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.467119e+08   \n",
        "\n",
        "                                                    Machine clears_min  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.015849   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.004092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.987818   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.906853   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.001099   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.000000   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.990401   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.988505   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.954967   \n",
        "\n",
        "                                                    time (exc)_median  \\\n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                 4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}         2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...       4.498193e+08   \n",
        "\n",
        "                                                    Machine clears_median  \\\n",
        "node                                                                        \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                         0.027724   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                        0.012778   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...               0.998147   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...               0.953007   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                         0.002209   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                        0.004722   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                 0.996143   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                 0.993871   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...               0.990938   \n",
        "\n",
        "                                                    time (exc)_mean  \\\n",
        "node                                                                  \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                   0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                  0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}           0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...         1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...         0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}               4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}              5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}       1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}       2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     4.492754e+08   \n",
        "\n",
        "                                                    Machine clears_mean  \\\n",
        "node                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                       0.027126   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                      0.017969   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.230749   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...             0.996256   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...             0.948031   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                       0.002266   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      0.007061   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.995425   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}               0.993798   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...             0.981222   \n",
        "\n",
        "                                                    time (exc)_var  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}              2.549456e-08   \n",
-       "{'name': 'Algorithm', 'type': 'function'}             3.652100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.560100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...    3.125018e-04   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...    6.953802e-05   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788476e+09   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             1.595913e+08   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      8.299723e+13   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      8.054587e+13   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    2.198205e+12   \n",
        "\n",
        "                                                    Machine clears_var  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.000072   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.000220   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.169282   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.000392   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                  8.279246e-07   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                 3.895210e-05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          4.977776e-06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}          8.699840e-06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...        2.352473e-04   \n",
        "\n",
        "                                                    time (exc)_std  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.000160   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000019   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        0.017678   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.008339   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              6.919881e+04   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             1.263294e+04   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      9.110282e+06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      8.974735e+06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    1.482635e+06   \n",
        "\n",
        "                                                    Machine clears_std  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.008508   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.014843   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.411439   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.003459   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.019788   \n",
-       "\n",
-       "                                                                               time (exc)_percentiles  \\\n",
-       "node                                                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}            [0.0070557499999999995, 0.007194000000000001, ...   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                            [0.00058425, 0.000598, 0.000603]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                    [0.00022325, 0.0002315, 0.000243]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  [1.29070175, 1.2940295000000002, 1.30458350000...   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                  [0.734351, 0.7368325, 0.74190975]   \n",
-       "\n",
-       "                                                                       Machine clears_percentiles  \n",
-       "node                                                                                               \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                         [0.02044975, 0.027724, 0.032329]  \n",
-       "{'name': 'Algorithm', 'type': 'function'}           [0.00833475, 0.0127785, 0.019187000000000003]  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                  [nan, nan, nan]  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...              [0.99564675, 0.9981475, 0.998563]  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...      [0.940767, 0.9530069999999999, 0.9630295]  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.000910   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.006241   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.002231   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.002950   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.015338   \n",
+       "\n",
+       "                                                                         time (exc)_percentiles  \\\n",
+       "node                                                                                              \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     [4723135.0, 4792513.5, 4834294.25]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       [533120.25, 538624.5, 545153.5]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}    [1988997772.0, 2001817999.5, 2004877745.25]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}    [2014797196.0, 2018832431.0, 2027420716.75]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     [448020898.75, 449819291.0, 450391779.5]   \n",
+       "\n",
+       "                                                                        Machine clears_percentiles  \n",
+       "node                                                                                                \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                          [0.00157425, 0.002209, 0.002657]  \n",
+       "{'name': 'Algorithm', 'type': 'function'}           [0.0025005, 0.0047220000000000005, 0.00878175]  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}       [0.99408025, 0.9961425, 0.9970844999999999]  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                 [0.99133675, 0.993871, 0.9959795]  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...               [0.9703815, 0.990938, 0.99332575]  "
       ]
      },
      "execution_count": 30,
@@ -4894,13 +4900,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2272fb61",
+   "id": "7346a1fb",
    "metadata": {
     "papermill": {
-     "duration": 0.007041,
-     "end_time": "2023-12-09T20:58:45.983035",
+     "duration": 0.007607,
+     "end_time": "2023-12-22T05:36:10.523397",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.975994",
+     "start_time": "2023-12-22T05:36:10.515790",
      "status": "completed"
     },
     "tags": []
@@ -4915,19 +4921,19 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "5ec3313b",
+   "id": "9cbd5fc1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:45.997717Z",
-     "iopub.status.busy": "2023-12-09T20:58:45.997617Z",
-     "iopub.status.idle": "2023-12-09T20:58:45.999322Z",
-     "shell.execute_reply": "2023-12-09T20:58:45.999066Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.539068Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.538938Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.541026Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.540613Z"
     },
     "papermill": {
-     "duration": 0.00961,
-     "end_time": "2023-12-09T20:58:45.999823",
+     "duration": 0.010601,
+     "end_time": "2023-12-22T05:36:10.541594",
      "exception": false,
-     "start_time": "2023-12-09T20:58:45.990213",
+     "start_time": "2023-12-22T05:36:10.530993",
      "status": "completed"
     },
     "tags": []
@@ -4940,19 +4946,19 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "58f19e04",
+   "id": "9e9c3503",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.014053Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.013960Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.059019Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.058780Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.568852Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.568655Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.601779Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.601381Z"
     },
     "papermill": {
-     "duration": 0.052628,
-     "end_time": "2023-12-09T20:58:46.059526",
+     "duration": 0.0532,
+     "end_time": "2023-12-22T05:36:10.602344",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.006898",
+     "start_time": "2023-12-22T05:36:10.549144",
      "status": "completed"
     },
     "tags": []
@@ -5036,234 +5042,234 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>[0.0070557499999999995, 0.007194000000000001, ...</td>\n",
-       "      <td>0.000160</td>\n",
-       "      <td>2.549456e-08</td>\n",
-       "      <td>0.034919</td>\n",
-       "      <td>0.027250</td>\n",
-       "      <td>0.026362</td>\n",
-       "      <td>0.014810</td>\n",
-       "      <td>[0.02477725, 0.026362499999999997, 0.0327565]</td>\n",
-       "      <td>0.005898</td>\n",
-       "      <td>0.000035</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>[4723135.0, 4792513.5, 4834294.25]</td>\n",
+       "      <td>6.919881e+04</td>\n",
+       "      <td>4.788476e+09</td>\n",
+       "      <td>0.004523</td>\n",
+       "      <td>0.002853</td>\n",
+       "      <td>0.003061</td>\n",
+       "      <td>0.001071</td>\n",
+       "      <td>[0.0018989999999999999, 0.0030615, 0.00361175]</td>\n",
+       "      <td>0.001096</td>\n",
+       "      <td>0.000001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>[0.00058425, 0.000598, 0.000603]</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>3.652100e-10</td>\n",
-       "      <td>0.078947</td>\n",
-       "      <td>0.019933</td>\n",
-       "      <td>0.012092</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>[533120.25, 538624.5, 545153.5]</td>\n",
+       "      <td>1.263294e+04</td>\n",
+       "      <td>1.595913e+08</td>\n",
+       "      <td>0.014205</td>\n",
+       "      <td>0.005249</td>\n",
+       "      <td>0.004810</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>[0.00871275, 0.012091999999999999, 0.0234505]</td>\n",
-       "      <td>0.021524</td>\n",
-       "      <td>0.000463</td>\n",
+       "      <td>[0.00244575, 0.00481, 0.006431]</td>\n",
+       "      <td>0.004187</td>\n",
+       "      <td>0.000018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>[0.00022325, 0.0002315, 0.000243]</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>1.560100e-10</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.311366</td>\n",
-       "      <td>0.027067</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>[0.0, 0.027067, 0.764881]</td>\n",
-       "      <td>0.451230</td>\n",
-       "      <td>0.203608</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>[1.29070175, 1.2940295000000002, 1.30458350000...</td>\n",
-       "      <td>0.017678</td>\n",
-       "      <td>3.125018e-04</td>\n",
-       "      <td>0.998832</td>\n",
-       "      <td>0.996608</td>\n",
-       "      <td>0.996824</td>\n",
-       "      <td>0.993177</td>\n",
-       "      <td>[0.99538975, 0.9968239999999999, 0.99820775]</td>\n",
-       "      <td>0.001797</td>\n",
-       "      <td>0.000003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>[0.734351, 0.7368325, 0.74190975]</td>\n",
-       "      <td>0.008339</td>\n",
-       "      <td>6.953802e-05</td>\n",
-       "      <td>0.978771</td>\n",
-       "      <td>0.944047</td>\n",
-       "      <td>0.949658</td>\n",
-       "      <td>0.905950</td>\n",
-       "      <td>[0.9266765, 0.9496575, 0.9571795]</td>\n",
-       "      <td>0.022419</td>\n",
-       "      <td>0.000503</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>[1988997772.0, 2001817999.5, 2004877745.25]</td>\n",
+       "      <td>9.110282e+06</td>\n",
+       "      <td>8.299723e+13</td>\n",
+       "      <td>0.992988</td>\n",
+       "      <td>0.978926</td>\n",
+       "      <td>0.984356</td>\n",
+       "      <td>0.956393</td>\n",
+       "      <td>[0.968337, 0.984356, 0.9885772500000001]</td>\n",
+       "      <td>0.012040</td>\n",
+       "      <td>0.000145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>[2014797196.0, 2018832431.0, 2027420716.75]</td>\n",
+       "      <td>8.974735e+06</td>\n",
+       "      <td>8.054587e+13</td>\n",
+       "      <td>0.997550</td>\n",
+       "      <td>0.990934</td>\n",
+       "      <td>0.991464</td>\n",
+       "      <td>0.980433</td>\n",
+       "      <td>[0.98897875, 0.991464, 0.9943077499999999]</td>\n",
+       "      <td>0.005125</td>\n",
+       "      <td>0.000026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>[448020898.75, 449819291.0, 450391779.5]</td>\n",
+       "      <td>1.482635e+06</td>\n",
+       "      <td>2.198205e+12</td>\n",
+       "      <td>0.994624</td>\n",
+       "      <td>0.922818</td>\n",
+       "      <td>0.925987</td>\n",
+       "      <td>0.770469</td>\n",
+       "      <td>[0.8846215, 0.9259875, 0.987635]</td>\n",
+       "      <td>0.068656</td>\n",
+       "      <td>0.004714</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                              \\\n",
-       "                                                                        name   \n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                          \\\n",
+       "                                                                    name   \n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                            Clang  \\\n",
        "                                                   time (exc)_max   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.511030e+08   \n",
        "\n",
        "                                                                    \\\n",
        "                                                   time (exc)_mean   \n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.492754e+08   \n",
        "\n",
        "                                                                      \\\n",
        "                                                   time (exc)_median   \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                    0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                   0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...          1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...          0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}               5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...      4.498193e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_min   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.722036   \n",
-       "\n",
-       "                                                                                                       \\\n",
-       "                                                                               time (exc)_percentiles   \n",
-       "node                                                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}            [0.0070557499999999995, 0.007194000000000001, ...   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                            [0.00058425, 0.000598, 0.000603]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                    [0.00022325, 0.0002315, 0.000243]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  [1.29070175, 1.2940295000000002, 1.30458350000...   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                  [0.734351, 0.7368325, 0.74190975]   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.467119e+08   \n",
+       "\n",
+       "                                                                                                 \\\n",
+       "                                                                         time (exc)_percentiles   \n",
+       "node                                                                                              \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     [4723135.0, 4792513.5, 4834294.25]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       [533120.25, 538624.5, 545153.5]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}    [1988997772.0, 2001817999.5, 2004877745.25]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}    [2014797196.0, 2018832431.0, 2027420716.75]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     [448020898.75, 449819291.0, 450391779.5]   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_std   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.000160   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000019   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       0.017678   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.008339   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             6.919881e+04   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            1.263294e+04   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     9.110282e+06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     8.974735e+06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   1.482635e+06   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_var   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}             2.549456e-08   \n",
-       "{'name': 'Algorithm', 'type': 'function'}            3.652100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.560100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...   3.125018e-04   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...   6.953802e-05   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.788476e+09   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            1.595913e+08   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     8.299723e+13   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     8.054587e+13   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   2.198205e+12   \n",
        "\n",
        "                                                                  GCC  \\\n",
        "                                                   Machine clears_max   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.034919   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.078947   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.998832   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.978771   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.004523   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.014205   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.992988   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.997550   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.994624   \n",
        "\n",
        "                                                                        \\\n",
        "                                                   Machine clears_mean   \n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.027250   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.019933   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.311366   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.996608   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.944047   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.002853   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.005249   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.978926   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.990934   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.922818   \n",
        "\n",
        "                                                                          \\\n",
        "                                                   Machine clears_median   \n",
        "node                                                                       \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                        0.026362   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                       0.012092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.027067   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...              0.996824   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...              0.949658   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        0.003061   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       0.004810   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.984356   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                0.991464   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...              0.925987   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_min   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.014810   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001071   \n",
        "{'name': 'Algorithm', 'type': 'function'}                    0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.993177   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.905950   \n",
-       "\n",
-       "                                                                                                   \\\n",
-       "                                                                       Machine clears_percentiles   \n",
-       "node                                                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}            [0.02477725, 0.026362499999999997, 0.0327565]   \n",
-       "{'name': 'Algorithm', 'type': 'function'}           [0.00871275, 0.012091999999999999, 0.0234505]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                        [0.0, 0.027067, 0.764881]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...   [0.99538975, 0.9968239999999999, 0.99820775]   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...              [0.9266765, 0.9496575, 0.9571795]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.956393   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.980433   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.770469   \n",
+       "\n",
+       "                                                                                                    \\\n",
+       "                                                                        Machine clears_percentiles   \n",
+       "node                                                                                                 \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}            [0.0018989999999999999, 0.0030615, 0.00361175]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                          [0.00244575, 0.00481, 0.006431]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          [0.968337, 0.984356, 0.9885772500000001]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        [0.98897875, 0.991464, 0.9943077499999999]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                [0.8846215, 0.9259875, 0.987635]   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_std   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.005898   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.021524   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.451230   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.001797   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.022419   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001096   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.004187   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.012040   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.005125   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.068656   \n",
        "\n",
        "                                                                       \n",
        "                                                   Machine clears_var  \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.000035  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000463  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.203608  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.000003  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.000503  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.000001  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.000018  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000145  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.000026  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.004714  "
       ]
      },
      "execution_count": 32,
@@ -5278,13 +5284,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae1892f3",
+   "id": "4946ca49",
    "metadata": {
     "papermill": {
-     "duration": 0.007559,
-     "end_time": "2023-12-09T20:58:46.074434",
+     "duration": 0.007763,
+     "end_time": "2023-12-22T05:36:10.618412",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.066875",
+     "start_time": "2023-12-22T05:36:10.610649",
      "status": "completed"
     },
     "tags": []
@@ -5299,13 +5305,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9f42d8a",
+   "id": "bd7aa653",
    "metadata": {
     "papermill": {
-     "duration": 0.007179,
-     "end_time": "2023-12-09T20:58:46.088782",
+     "duration": 0.007567,
+     "end_time": "2023-12-22T05:36:10.633609",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.081603",
+     "start_time": "2023-12-22T05:36:10.626042",
      "status": "completed"
     },
     "tags": []
@@ -5317,19 +5323,19 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "2ee905e1",
+   "id": "8a655166",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.103577Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.103482Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.105084Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.104880Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.649115Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.648975Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.650808Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.650553Z"
     },
     "papermill": {
-     "duration": 0.009721,
-     "end_time": "2023-12-09T20:58:46.105591",
+     "duration": 0.010417,
+     "end_time": "2023-12-22T05:36:10.651374",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.095870",
+     "start_time": "2023-12-22T05:36:10.640957",
      "status": "completed"
     },
     "tags": []
@@ -5342,19 +5348,19 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "6e249802",
+   "id": "af7857af",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.120397Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.120297Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.162116Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.161854Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.667404Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.667264Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.692701Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.692368Z"
     },
     "papermill": {
-     "duration": 0.04974,
-     "end_time": "2023-12-09T20:58:46.162671",
+     "duration": 0.034296,
+     "end_time": "2023-12-22T05:36:10.693353",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.112931",
+     "start_time": "2023-12-22T05:36:10.659057",
      "status": "completed"
     },
     "tags": []
@@ -5432,245 +5438,245 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.044086</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.015849</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.027724</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.027126</td>\n",
-       "      <td>2.549456e-08</td>\n",
-       "      <td>0.000072</td>\n",
-       "      <td>0.000160</td>\n",
-       "      <td>0.008508</td>\n",
-       "      <td>[0.0070557499999999995, 0.007194000000000001, ...</td>\n",
-       "      <td>[0.02044975, 0.027724, 0.032329]</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>0.003932</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>0.001099</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>0.002209</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>0.002266</td>\n",
+       "      <td>4.788476e+09</td>\n",
+       "      <td>8.279246e-07</td>\n",
+       "      <td>6.919881e+04</td>\n",
+       "      <td>0.000910</td>\n",
+       "      <td>[4723135.0, 4792513.5, 4834294.25]</td>\n",
+       "      <td>[0.00157425, 0.002209, 0.002657]</td>\n",
        "      <td>True</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.045977</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.004092</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.012778</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.017969</td>\n",
-       "      <td>3.652100e-10</td>\n",
-       "      <td>0.000220</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>0.014843</td>\n",
-       "      <td>[0.00058425, 0.000598, 0.000603]</td>\n",
-       "      <td>[0.00833475, 0.0127785, 0.019187000000000003]</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>0.019851</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>0.004722</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>0.007061</td>\n",
+       "      <td>1.595913e+08</td>\n",
+       "      <td>3.895210e-05</td>\n",
+       "      <td>1.263294e+04</td>\n",
+       "      <td>0.006241</td>\n",
+       "      <td>[533120.25, 538624.5, 545153.5]</td>\n",
+       "      <td>[0.0025005, 0.0047220000000000005, 0.00878175]</td>\n",
+       "      <td>True</td>\n",
        "      <td>True</td>\n",
-       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.230749</td>\n",
-       "      <td>1.560100e-10</td>\n",
-       "      <td>0.169282</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.411439</td>\n",
-       "      <td>[0.00022325, 0.0002315, 0.000243]</td>\n",
-       "      <td>[nan, nan, nan]</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>0.997907</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>0.990401</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>0.996143</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>0.995425</td>\n",
+       "      <td>8.299723e+13</td>\n",
+       "      <td>4.977776e-06</td>\n",
+       "      <td>9.110282e+06</td>\n",
+       "      <td>0.002231</td>\n",
+       "      <td>[1988997772.0, 2001817999.5, 2004877745.25]</td>\n",
+       "      <td>[0.99408025, 0.9961425, 0.9970844999999999]</td>\n",
        "      <td>True</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>0.998909</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.987818</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>0.998147</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>0.996256</td>\n",
-       "      <td>3.125018e-04</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.017678</td>\n",
-       "      <td>0.003459</td>\n",
-       "      <td>[1.29070175, 1.2940295000000002, 1.30458350000...</td>\n",
-       "      <td>[0.99564675, 0.9981475, 0.998563]</td>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>0.997923</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>0.988505</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>0.993871</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>0.993798</td>\n",
+       "      <td>8.054587e+13</td>\n",
+       "      <td>8.699840e-06</td>\n",
+       "      <td>8.974735e+06</td>\n",
+       "      <td>0.002950</td>\n",
+       "      <td>[2014797196.0, 2018832431.0, 2027420716.75]</td>\n",
+       "      <td>[0.99133675, 0.993871, 0.9959795]</td>\n",
+       "      <td>True</td>\n",
        "      <td>True</td>\n",
-       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.973024</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.906853</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.953007</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.948031</td>\n",
-       "      <td>6.953802e-05</td>\n",
-       "      <td>0.000392</td>\n",
-       "      <td>0.008339</td>\n",
-       "      <td>0.019788</td>\n",
-       "      <td>[0.734351, 0.7368325, 0.74190975]</td>\n",
-       "      <td>[0.940767, 0.9530069999999999, 0.9630295]</td>\n",
-       "      <td>True</td>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>0.996756</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>0.954967</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>0.990938</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>0.981222</td>\n",
+       "      <td>2.198205e+12</td>\n",
+       "      <td>2.352473e-04</td>\n",
+       "      <td>1.482635e+06</td>\n",
+       "      <td>0.015338</td>\n",
+       "      <td>[448020898.75, 449819291.0, 450391779.5]</td>\n",
+       "      <td>[0.9703815, 0.990938, 0.99332575]</td>\n",
        "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                        name  \\\n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                    name  \\\n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                    time (exc)_max  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.511030e+08   \n",
        "\n",
        "                                                    Machine clears_max  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.044086   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.045977   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.998909   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.973024   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.003932   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.019851   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.997907   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.997923   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.996756   \n",
        "\n",
        "                                                    time (exc)_min  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.467119e+08   \n",
        "\n",
        "                                                    Machine clears_min  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.015849   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.004092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.987818   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.906853   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.001099   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.000000   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.990401   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.988505   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.954967   \n",
        "\n",
        "                                                    time (exc)_median  \\\n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                 4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}         2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...       4.498193e+08   \n",
        "\n",
        "                                                    Machine clears_median  \\\n",
        "node                                                                        \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                         0.027724   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                        0.012778   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...               0.998147   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...               0.953007   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                         0.002209   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                        0.004722   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                 0.996143   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                 0.993871   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...               0.990938   \n",
        "\n",
        "                                                    time (exc)_mean  \\\n",
        "node                                                                  \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                   0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                  0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}           0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...         1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...         0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}               4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}              5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}       1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}       2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     4.492754e+08   \n",
        "\n",
        "                                                    Machine clears_mean  \\\n",
        "node                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                       0.027126   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                      0.017969   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.230749   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...             0.996256   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...             0.948031   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                       0.002266   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      0.007061   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.995425   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}               0.993798   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...             0.981222   \n",
        "\n",
        "                                                    time (exc)_var  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}              2.549456e-08   \n",
-       "{'name': 'Algorithm', 'type': 'function'}             3.652100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.560100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...    3.125018e-04   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...    6.953802e-05   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788476e+09   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             1.595913e+08   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      8.299723e+13   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      8.054587e+13   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    2.198205e+12   \n",
        "\n",
        "                                                    Machine clears_var  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.000072   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.000220   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.169282   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.000392   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                  8.279246e-07   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                 3.895210e-05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          4.977776e-06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}          8.699840e-06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...        2.352473e-04   \n",
        "\n",
        "                                                    time (exc)_std  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.000160   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000019   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        0.017678   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.008339   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              6.919881e+04   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             1.263294e+04   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      9.110282e+06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      8.974735e+06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    1.482635e+06   \n",
        "\n",
        "                                                    Machine clears_std  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.008508   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.014843   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.411439   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.003459   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.019788   \n",
-       "\n",
-       "                                                                               time (exc)_percentiles  \\\n",
-       "node                                                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}            [0.0070557499999999995, 0.007194000000000001, ...   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                            [0.00058425, 0.000598, 0.000603]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                    [0.00022325, 0.0002315, 0.000243]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  [1.29070175, 1.2940295000000002, 1.30458350000...   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                  [0.734351, 0.7368325, 0.74190975]   \n",
-       "\n",
-       "                                                                       Machine clears_percentiles  \\\n",
-       "node                                                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                         [0.02044975, 0.027724, 0.032329]   \n",
-       "{'name': 'Algorithm', 'type': 'function'}           [0.00833475, 0.0127785, 0.019187000000000003]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                  [nan, nan, nan]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...              [0.99564675, 0.9981475, 0.998563]   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...      [0.940767, 0.9530069999999999, 0.9630295]   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.000910   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.006241   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.002231   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.002950   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.015338   \n",
+       "\n",
+       "                                                                         time (exc)_percentiles  \\\n",
+       "node                                                                                              \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     [4723135.0, 4792513.5, 4834294.25]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       [533120.25, 538624.5, 545153.5]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}    [1988997772.0, 2001817999.5, 2004877745.25]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}    [2014797196.0, 2018832431.0, 2027420716.75]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     [448020898.75, 449819291.0, 450391779.5]   \n",
+       "\n",
+       "                                                                        Machine clears_percentiles  \\\n",
+       "node                                                                                                 \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                          [0.00157425, 0.002209, 0.002657]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}           [0.0025005, 0.0047220000000000005, 0.00878175]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}       [0.99408025, 0.9961425, 0.9970844999999999]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                 [0.99133675, 0.993871, 0.9959795]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...               [0.9703815, 0.990938, 0.99332575]   \n",
        "\n",
        "                                                   time (exc)_normality  \\\n",
        "node                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                           True   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                           True   \n",
        "{'name': 'Algorithm', 'type': 'function'}                          True   \n",
        "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                   True   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                 True   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                 True   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                   True   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                 True   \n",
        "\n",
        "                                                   Machine clears_normality  \n",
        "node                                                                         \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                               True  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                             False  \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                               True  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                              True  \n",
        "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                       True  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                    False  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                     True  "
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                       True  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                    False  "
       ]
      },
      "execution_count": 34,
@@ -5685,13 +5691,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7237503c",
+   "id": "683ae976",
    "metadata": {
     "papermill": {
-     "duration": 0.007423,
-     "end_time": "2023-12-09T20:58:46.177555",
+     "duration": 0.009533,
+     "end_time": "2023-12-22T05:36:10.710716",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.170132",
+     "start_time": "2023-12-22T05:36:10.701183",
      "status": "completed"
     },
     "tags": []
@@ -5706,19 +5712,19 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "f9a54fba",
+   "id": "014dc38c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.192669Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.192569Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.194184Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.193991Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.727914Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.727749Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.729735Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.729360Z"
     },
     "papermill": {
-     "duration": 0.009741,
-     "end_time": "2023-12-09T20:58:46.194701",
+     "duration": 0.011346,
+     "end_time": "2023-12-22T05:36:10.730433",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.184960",
+     "start_time": "2023-12-22T05:36:10.719087",
      "status": "completed"
     },
     "tags": []
@@ -5731,19 +5737,19 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "13e526d2",
+   "id": "c2d5dee1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.209813Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.209712Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.252942Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.252700Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.747514Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.747358Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.777539Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.777203Z"
     },
     "papermill": {
-     "duration": 0.051206,
-     "end_time": "2023-12-09T20:58:46.253457",
+     "duration": 0.039429,
+     "end_time": "2023-12-22T05:36:10.778163",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.202251",
+     "start_time": "2023-12-22T05:36:10.738734",
      "status": "completed"
     },
     "tags": []
@@ -5754,13 +5760,7 @@
      "output_type": "stream",
      "text": [
       "/opt/conda/lib/python3.9/site-packages/thicket/stats/check_normality.py:57: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
-      "  for node in pd.unique(thicket.dataframe.reset_index()[\"node\"].tolist()):\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "  for node in pd.unique(thicket.dataframe.reset_index()[\"node\"].tolist()):\n",
       "/opt/conda/lib/python3.9/site-packages/thicket/stats/check_normality.py:57: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
       "  for node in pd.unique(thicket.dataframe.reset_index()[\"node\"].tolist()):\n"
      ]
@@ -5837,262 +5837,262 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.006875</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>4.696939e+06</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.0070557499999999995, 0.007194000000000001, ...</td>\n",
-       "      <td>0.000160</td>\n",
-       "      <td>2.549456e-08</td>\n",
-       "      <td>0.034919</td>\n",
-       "      <td>0.027250</td>\n",
-       "      <td>0.026362</td>\n",
-       "      <td>0.014810</td>\n",
+       "      <td>[4723135.0, 4792513.5, 4834294.25]</td>\n",
+       "      <td>6.919881e+04</td>\n",
+       "      <td>4.788476e+09</td>\n",
+       "      <td>0.004523</td>\n",
+       "      <td>0.002853</td>\n",
+       "      <td>0.003061</td>\n",
+       "      <td>0.001071</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.02477725, 0.026362499999999997, 0.0327565]</td>\n",
-       "      <td>0.005898</td>\n",
-       "      <td>0.000035</td>\n",
+       "      <td>[0.0018989999999999999, 0.0030615, 0.00361175]</td>\n",
+       "      <td>0.001096</td>\n",
+       "      <td>0.000001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.000564</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>5.176070e+05</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.00058425, 0.000598, 0.000603]</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>3.652100e-10</td>\n",
-       "      <td>0.078947</td>\n",
-       "      <td>0.019933</td>\n",
-       "      <td>0.012092</td>\n",
+       "      <td>[533120.25, 538624.5, 545153.5]</td>\n",
+       "      <td>1.263294e+04</td>\n",
+       "      <td>1.595913e+08</td>\n",
+       "      <td>0.014205</td>\n",
+       "      <td>0.005249</td>\n",
+       "      <td>0.004810</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>False</td>\n",
-       "      <td>[0.00871275, 0.012091999999999999, 0.0234505]</td>\n",
-       "      <td>0.021524</td>\n",
-       "      <td>0.000463</td>\n",
+       "      <td>True</td>\n",
+       "      <td>[0.00244575, 0.00481, 0.006431]</td>\n",
+       "      <td>0.004187</td>\n",
+       "      <td>0.000018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>0.000215</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>1.982438e+09</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.00022325, 0.0002315, 0.000243]</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>1.560100e-10</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.311366</td>\n",
-       "      <td>0.027067</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>False</td>\n",
-       "      <td>[0.0, 0.027067, 0.764881]</td>\n",
-       "      <td>0.451230</td>\n",
-       "      <td>0.203608</td>\n",
+       "      <td>[1988997772.0, 2001817999.5, 2004877745.25]</td>\n",
+       "      <td>9.110282e+06</td>\n",
+       "      <td>8.299723e+13</td>\n",
+       "      <td>0.992988</td>\n",
+       "      <td>0.978926</td>\n",
+       "      <td>0.984356</td>\n",
+       "      <td>0.956393</td>\n",
+       "      <td>True</td>\n",
+       "      <td>[0.968337, 0.984356, 0.9885772500000001]</td>\n",
+       "      <td>0.012040</td>\n",
+       "      <td>0.000145</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>1.275210</td>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>2.006252e+09</td>\n",
        "      <td>True</td>\n",
-       "      <td>[1.29070175, 1.2940295000000002, 1.30458350000...</td>\n",
-       "      <td>0.017678</td>\n",
-       "      <td>3.125018e-04</td>\n",
-       "      <td>0.998832</td>\n",
-       "      <td>0.996608</td>\n",
-       "      <td>0.996824</td>\n",
-       "      <td>0.993177</td>\n",
+       "      <td>[2014797196.0, 2018832431.0, 2027420716.75]</td>\n",
+       "      <td>8.974735e+06</td>\n",
+       "      <td>8.054587e+13</td>\n",
+       "      <td>0.997550</td>\n",
+       "      <td>0.990934</td>\n",
+       "      <td>0.991464</td>\n",
+       "      <td>0.980433</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.99538975, 0.9968239999999999, 0.99820775]</td>\n",
-       "      <td>0.001797</td>\n",
-       "      <td>0.000003</td>\n",
+       "      <td>[0.98897875, 0.991464, 0.9943077499999999]</td>\n",
+       "      <td>0.005125</td>\n",
+       "      <td>0.000026</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.722036</td>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>4.467119e+08</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.734351, 0.7368325, 0.74190975]</td>\n",
-       "      <td>0.008339</td>\n",
-       "      <td>6.953802e-05</td>\n",
-       "      <td>0.978771</td>\n",
-       "      <td>0.944047</td>\n",
-       "      <td>0.949658</td>\n",
-       "      <td>0.905950</td>\n",
+       "      <td>[448020898.75, 449819291.0, 450391779.5]</td>\n",
+       "      <td>1.482635e+06</td>\n",
+       "      <td>2.198205e+12</td>\n",
+       "      <td>0.994624</td>\n",
+       "      <td>0.922818</td>\n",
+       "      <td>0.925987</td>\n",
+       "      <td>0.770469</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.9266765, 0.9496575, 0.9571795]</td>\n",
-       "      <td>0.022419</td>\n",
-       "      <td>0.000503</td>\n",
+       "      <td>[0.8846215, 0.9259875, 0.987635]</td>\n",
+       "      <td>0.068656</td>\n",
+       "      <td>0.004714</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                              \\\n",
-       "                                                                        name   \n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                          \\\n",
+       "                                                                    name   \n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                            Clang  \\\n",
        "                                                   time (exc)_max   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.511030e+08   \n",
        "\n",
        "                                                                    \\\n",
        "                                                   time (exc)_mean   \n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.492754e+08   \n",
        "\n",
        "                                                                      \\\n",
        "                                                   time (exc)_median   \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                    0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                   0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...          1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...          0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}               5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...      4.498193e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_min   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.467119e+08   \n",
        "\n",
        "                                                                         \\\n",
        "                                                   time (exc)_normality   \n",
        "node                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                           True   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                           True   \n",
        "{'name': 'Algorithm', 'type': 'function'}                          True   \n",
        "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                   True   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                 True   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                 True   \n",
-       "\n",
-       "                                                                                                       \\\n",
-       "                                                                               time (exc)_percentiles   \n",
-       "node                                                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}            [0.0070557499999999995, 0.007194000000000001, ...   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                            [0.00058425, 0.000598, 0.000603]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                    [0.00022325, 0.0002315, 0.000243]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  [1.29070175, 1.2940295000000002, 1.30458350000...   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                  [0.734351, 0.7368325, 0.74190975]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                   True   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                 True   \n",
+       "\n",
+       "                                                                                                 \\\n",
+       "                                                                         time (exc)_percentiles   \n",
+       "node                                                                                              \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     [4723135.0, 4792513.5, 4834294.25]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       [533120.25, 538624.5, 545153.5]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}    [1988997772.0, 2001817999.5, 2004877745.25]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}    [2014797196.0, 2018832431.0, 2027420716.75]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     [448020898.75, 449819291.0, 450391779.5]   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_std   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.000160   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000019   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       0.017678   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.008339   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             6.919881e+04   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            1.263294e+04   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     9.110282e+06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     8.974735e+06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   1.482635e+06   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_var   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}             2.549456e-08   \n",
-       "{'name': 'Algorithm', 'type': 'function'}            3.652100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.560100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...   3.125018e-04   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...   6.953802e-05   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.788476e+09   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            1.595913e+08   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     8.299723e+13   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     8.054587e+13   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   2.198205e+12   \n",
        "\n",
        "                                                                  GCC  \\\n",
        "                                                   Machine clears_max   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.034919   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.078947   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.998832   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.978771   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.004523   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.014205   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.992988   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.997550   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.994624   \n",
        "\n",
        "                                                                        \\\n",
        "                                                   Machine clears_mean   \n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.027250   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.019933   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.311366   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.996608   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.944047   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.002853   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.005249   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.978926   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.990934   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.922818   \n",
        "\n",
        "                                                                          \\\n",
        "                                                   Machine clears_median   \n",
        "node                                                                       \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                        0.026362   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                       0.012092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.027067   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...              0.996824   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...              0.949658   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        0.003061   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       0.004810   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.984356   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                0.991464   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...              0.925987   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_min   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.014810   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001071   \n",
        "{'name': 'Algorithm', 'type': 'function'}                    0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.993177   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.905950   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.956393   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.980433   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.770469   \n",
        "\n",
        "                                                                             \\\n",
        "                                                   Machine clears_normality   \n",
        "node                                                                          \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                               True   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                             False   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      False   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                     True   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                     True   \n",
-       "\n",
-       "                                                                                                   \\\n",
-       "                                                                       Machine clears_percentiles   \n",
-       "node                                                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}            [0.02477725, 0.026362499999999997, 0.0327565]   \n",
-       "{'name': 'Algorithm', 'type': 'function'}           [0.00871275, 0.012091999999999999, 0.0234505]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                        [0.0, 0.027067, 0.764881]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...   [0.99538975, 0.9968239999999999, 0.99820775]   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...              [0.9266765, 0.9496575, 0.9571795]   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                               True   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                              True   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                       True   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                       True   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                     True   \n",
+       "\n",
+       "                                                                                                    \\\n",
+       "                                                                        Machine clears_percentiles   \n",
+       "node                                                                                                 \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}            [0.0018989999999999999, 0.0030615, 0.00361175]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                          [0.00244575, 0.00481, 0.006431]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          [0.968337, 0.984356, 0.9885772500000001]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        [0.98897875, 0.991464, 0.9943077499999999]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                [0.8846215, 0.9259875, 0.987635]   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_std   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.005898   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.021524   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.451230   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.001797   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.022419   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001096   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.004187   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.012040   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.005125   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.068656   \n",
        "\n",
        "                                                                       \n",
        "                                                   Machine clears_var  \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.000035  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000463  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.203608  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.000003  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.000503  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.000001  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.000018  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000145  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.000026  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.004714  "
       ]
      },
      "execution_count": 36,
@@ -6107,13 +6107,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fda94e9f",
+   "id": "d2cef71d",
    "metadata": {
     "papermill": {
-     "duration": 0.007629,
-     "end_time": "2023-12-09T20:58:46.268739",
+     "duration": 0.008122,
+     "end_time": "2023-12-22T05:36:10.794792",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.261110",
+     "start_time": "2023-12-22T05:36:10.786670",
      "status": "completed"
     },
     "tags": []
@@ -6130,13 +6130,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a9c75fd",
+   "id": "980b00bf",
    "metadata": {
     "papermill": {
-     "duration": 0.007613,
-     "end_time": "2023-12-09T20:58:46.284020",
+     "duration": 0.007829,
+     "end_time": "2023-12-22T05:36:10.810794",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.276407",
+     "start_time": "2023-12-22T05:36:10.802965",
      "status": "completed"
     },
     "tags": []
@@ -6148,19 +6148,19 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "b1082bb5",
+   "id": "284215d4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.299578Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.299481Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.337488Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.337217Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.827761Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.827616Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.851674Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.851385Z"
     },
     "papermill": {
-     "duration": 0.046312,
-     "end_time": "2023-12-09T20:58:46.338000",
+     "duration": 0.033527,
+     "end_time": "2023-12-22T05:36:10.852465",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.291688",
+     "start_time": "2023-12-22T05:36:10.818938",
      "status": "completed"
     },
     "tags": []
@@ -6173,19 +6173,19 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "2a93747d",
+   "id": "7b17c43c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.353545Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.353451Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.359717Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.359497Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.870037Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.869880Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.877518Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.877248Z"
     },
     "papermill": {
-     "duration": 0.014593,
-     "end_time": "2023-12-09T20:58:46.360221",
+     "duration": 0.016982,
+     "end_time": "2023-12-22T05:36:10.878045",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.345628",
+     "start_time": "2023-12-22T05:36:10.861063",
      "status": "completed"
     },
     "tags": []
@@ -6255,258 +6255,258 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.044086</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.015849</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.027724</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.027126</td>\n",
-       "      <td>2.549456e-08</td>\n",
-       "      <td>0.000072</td>\n",
-       "      <td>0.000160</td>\n",
-       "      <td>0.008508</td>\n",
-       "      <td>[0.0070557499999999995, 0.007194000000000001, ...</td>\n",
-       "      <td>[0.02044975, 0.027724, 0.032329]</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>0.003932</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>0.001099</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>0.002209</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>0.002266</td>\n",
+       "      <td>4.788476e+09</td>\n",
+       "      <td>8.279246e-07</td>\n",
+       "      <td>6.919881e+04</td>\n",
+       "      <td>0.000910</td>\n",
+       "      <td>[4723135.0, 4792513.5, 4834294.25]</td>\n",
+       "      <td>[0.00157425, 0.002209, 0.002657]</td>\n",
        "      <td>True</td>\n",
        "      <td>True</td>\n",
-       "      <td>-0.248485</td>\n",
+       "      <td>0.503030</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.045977</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.004092</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.012778</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.017969</td>\n",
-       "      <td>3.652100e-10</td>\n",
-       "      <td>0.000220</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>0.014843</td>\n",
-       "      <td>[0.00058425, 0.000598, 0.000603]</td>\n",
-       "      <td>[0.00833475, 0.0127785, 0.019187000000000003]</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>0.019851</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>0.004722</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>0.007061</td>\n",
+       "      <td>1.595913e+08</td>\n",
+       "      <td>3.895210e-05</td>\n",
+       "      <td>1.263294e+04</td>\n",
+       "      <td>0.006241</td>\n",
+       "      <td>[533120.25, 538624.5, 545153.5]</td>\n",
+       "      <td>[0.0025005, 0.0047220000000000005, 0.00878175]</td>\n",
        "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>-0.182372</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-0.660606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.230749</td>\n",
-       "      <td>1.560100e-10</td>\n",
-       "      <td>0.169282</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.411439</td>\n",
-       "      <td>[0.00022325, 0.0002315, 0.000243]</td>\n",
-       "      <td>[nan, nan, nan]</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>0.997907</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>0.990401</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>0.996143</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>0.995425</td>\n",
+       "      <td>8.299723e+13</td>\n",
+       "      <td>4.977776e-06</td>\n",
+       "      <td>9.110282e+06</td>\n",
+       "      <td>0.002231</td>\n",
+       "      <td>[1988997772.0, 2001817999.5, 2004877745.25]</td>\n",
+       "      <td>[0.99408025, 0.9961425, 0.9970844999999999]</td>\n",
        "      <td>True</td>\n",
        "      <td>True</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>0.998909</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.987818</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>0.998147</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>0.996256</td>\n",
-       "      <td>3.125018e-04</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.017678</td>\n",
-       "      <td>0.003459</td>\n",
-       "      <td>[1.29070175, 1.2940295000000002, 1.30458350000...</td>\n",
-       "      <td>[0.99564675, 0.9981475, 0.998563]</td>\n",
+       "      <td>0.078788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>0.997923</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>0.988505</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>0.993871</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>0.993798</td>\n",
+       "      <td>8.054587e+13</td>\n",
+       "      <td>8.699840e-06</td>\n",
+       "      <td>8.974735e+06</td>\n",
+       "      <td>0.002950</td>\n",
+       "      <td>[2014797196.0, 2018832431.0, 2027420716.75]</td>\n",
+       "      <td>[0.99133675, 0.993871, 0.9959795]</td>\n",
        "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>-0.066667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.973024</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.906853</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.953007</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.948031</td>\n",
-       "      <td>6.953802e-05</td>\n",
-       "      <td>0.000392</td>\n",
-       "      <td>0.008339</td>\n",
-       "      <td>0.019788</td>\n",
-       "      <td>[0.734351, 0.7368325, 0.74190975]</td>\n",
-       "      <td>[0.940767, 0.9530069999999999, 0.9630295]</td>\n",
        "      <td>True</td>\n",
+       "      <td>0.054545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>0.996756</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>0.954967</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>0.990938</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>0.981222</td>\n",
+       "      <td>2.198205e+12</td>\n",
+       "      <td>2.352473e-04</td>\n",
+       "      <td>1.482635e+06</td>\n",
+       "      <td>0.015338</td>\n",
+       "      <td>[448020898.75, 449819291.0, 450391779.5]</td>\n",
+       "      <td>[0.9703815, 0.990938, 0.99332575]</td>\n",
        "      <td>True</td>\n",
-       "      <td>-0.163636</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-0.345455</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                        name  \\\n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                    name  \\\n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                    time (exc)_max  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.511030e+08   \n",
        "\n",
        "                                                    Machine clears_max  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.044086   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.045977   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.998909   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.973024   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.003932   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.019851   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.997907   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.997923   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.996756   \n",
        "\n",
        "                                                    time (exc)_min  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.467119e+08   \n",
        "\n",
        "                                                    Machine clears_min  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.015849   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.004092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.987818   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.906853   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.001099   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.000000   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.990401   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.988505   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.954967   \n",
        "\n",
        "                                                    time (exc)_median  \\\n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                 4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}         2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...       4.498193e+08   \n",
        "\n",
        "                                                    Machine clears_median  \\\n",
        "node                                                                        \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                         0.027724   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                        0.012778   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...               0.998147   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...               0.953007   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                         0.002209   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                        0.004722   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                 0.996143   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                 0.993871   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...               0.990938   \n",
        "\n",
        "                                                    time (exc)_mean  \\\n",
        "node                                                                  \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                   0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                  0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}           0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...         1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...         0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}               4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}              5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}       1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}       2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     4.492754e+08   \n",
        "\n",
        "                                                    Machine clears_mean  \\\n",
        "node                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                       0.027126   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                      0.017969   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.230749   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...             0.996256   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...             0.948031   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                       0.002266   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      0.007061   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.995425   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}               0.993798   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...             0.981222   \n",
        "\n",
        "                                                    time (exc)_var  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}              2.549456e-08   \n",
-       "{'name': 'Algorithm', 'type': 'function'}             3.652100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.560100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...    3.125018e-04   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...    6.953802e-05   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788476e+09   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             1.595913e+08   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      8.299723e+13   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      8.054587e+13   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    2.198205e+12   \n",
        "\n",
        "                                                    Machine clears_var  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.000072   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.000220   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.169282   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.000392   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                  8.279246e-07   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                 3.895210e-05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          4.977776e-06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}          8.699840e-06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...        2.352473e-04   \n",
        "\n",
        "                                                    time (exc)_std  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.000160   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000019   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        0.017678   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.008339   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              6.919881e+04   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             1.263294e+04   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      9.110282e+06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      8.974735e+06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    1.482635e+06   \n",
        "\n",
        "                                                    Machine clears_std  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.008508   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.014843   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.411439   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.003459   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.019788   \n",
-       "\n",
-       "                                                                               time (exc)_percentiles  \\\n",
-       "node                                                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}            [0.0070557499999999995, 0.007194000000000001, ...   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                            [0.00058425, 0.000598, 0.000603]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                    [0.00022325, 0.0002315, 0.000243]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  [1.29070175, 1.2940295000000002, 1.30458350000...   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                  [0.734351, 0.7368325, 0.74190975]   \n",
-       "\n",
-       "                                                                       Machine clears_percentiles  \\\n",
-       "node                                                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                         [0.02044975, 0.027724, 0.032329]   \n",
-       "{'name': 'Algorithm', 'type': 'function'}           [0.00833475, 0.0127785, 0.019187000000000003]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                  [nan, nan, nan]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...              [0.99564675, 0.9981475, 0.998563]   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...      [0.940767, 0.9530069999999999, 0.9630295]   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.000910   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.006241   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.002231   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.002950   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.015338   \n",
+       "\n",
+       "                                                                         time (exc)_percentiles  \\\n",
+       "node                                                                                              \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     [4723135.0, 4792513.5, 4834294.25]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       [533120.25, 538624.5, 545153.5]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}    [1988997772.0, 2001817999.5, 2004877745.25]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}    [2014797196.0, 2018832431.0, 2027420716.75]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     [448020898.75, 449819291.0, 450391779.5]   \n",
+       "\n",
+       "                                                                        Machine clears_percentiles  \\\n",
+       "node                                                                                                 \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                          [0.00157425, 0.002209, 0.002657]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}           [0.0025005, 0.0047220000000000005, 0.00878175]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}       [0.99408025, 0.9961425, 0.9970844999999999]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                 [0.99133675, 0.993871, 0.9959795]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...               [0.9703815, 0.990938, 0.99332575]   \n",
        "\n",
        "                                                   time (exc)_normality  \\\n",
        "node                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                           True   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                           True   \n",
        "{'name': 'Algorithm', 'type': 'function'}                          True   \n",
        "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                   True   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                 True   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                 True   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                   True   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                 True   \n",
        "\n",
        "                                                   Machine clears_normality  \\\n",
        "node                                                                          \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                               True   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                             False   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                               True   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                              True   \n",
        "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                       True   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                    False   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                     True   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                       True   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                    False   \n",
        "\n",
        "                                                    time (exc)_vs_Machine clears spearman  \n",
        "node                                                                                       \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                        -0.248485  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                       -0.182372  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                      NaN  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                              -0.066667  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                              -0.163636  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                                         0.503030  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                       -0.660606  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                 0.078788  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                                 0.054545  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                              -0.345455  "
       ]
      },
      "execution_count": 38,
@@ -6520,13 +6520,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3d33c56",
+   "id": "2910a9ec",
    "metadata": {
     "papermill": {
-     "duration": 0.007596,
-     "end_time": "2023-12-09T20:58:46.375760",
+     "duration": 0.008336,
+     "end_time": "2023-12-22T05:36:10.894921",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.368164",
+     "start_time": "2023-12-22T05:36:10.886585",
      "status": "completed"
     },
     "tags": []
@@ -6542,19 +6542,19 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "51bf5121",
+   "id": "beb98598",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.391716Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.391621Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.430957Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.430698Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.912051Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.911911Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.942219Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.941823Z"
     },
     "papermill": {
-     "duration": 0.047993,
-     "end_time": "2023-12-09T20:58:46.431542",
+     "duration": 0.039956,
+     "end_time": "2023-12-22T05:36:10.943085",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.383549",
+     "start_time": "2023-12-22T05:36:10.903129",
      "status": "completed"
     },
     "tags": []
@@ -6567,19 +6567,19 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "b4a00a13",
+   "id": "7414440a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.447429Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.447320Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.454653Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.454397Z"
+     "iopub.execute_input": "2023-12-22T05:36:10.971098Z",
+     "iopub.status.busy": "2023-12-22T05:36:10.970968Z",
+     "iopub.status.idle": "2023-12-22T05:36:10.980229Z",
+     "shell.execute_reply": "2023-12-22T05:36:10.979922Z"
     },
     "papermill": {
-     "duration": 0.015835,
-     "end_time": "2023-12-09T20:58:46.455174",
+     "duration": 0.029417,
+     "end_time": "2023-12-22T05:36:10.980909",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.439339",
+     "start_time": "2023-12-22T05:36:10.951492",
      "status": "completed"
     },
     "tags": []
@@ -6660,276 +6660,276 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.006875</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>4.696939e+06</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.0070557499999999995, 0.007194000000000001, ...</td>\n",
-       "      <td>0.000160</td>\n",
-       "      <td>2.549456e-08</td>\n",
-       "      <td>0.034919</td>\n",
-       "      <td>0.027250</td>\n",
-       "      <td>0.026362</td>\n",
-       "      <td>0.014810</td>\n",
+       "      <td>[4723135.0, 4792513.5, 4834294.25]</td>\n",
+       "      <td>6.919881e+04</td>\n",
+       "      <td>4.788476e+09</td>\n",
+       "      <td>0.004523</td>\n",
+       "      <td>0.002853</td>\n",
+       "      <td>0.003061</td>\n",
+       "      <td>0.001071</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.02477725, 0.026362499999999997, 0.0327565]</td>\n",
-       "      <td>0.005898</td>\n",
-       "      <td>0.000035</td>\n",
-       "      <td>-0.139394</td>\n",
+       "      <td>[0.0018989999999999999, 0.0030615, 0.00361175]</td>\n",
+       "      <td>0.001096</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.115152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.000564</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>5.176070e+05</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.00058425, 0.000598, 0.000603]</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>3.652100e-10</td>\n",
-       "      <td>0.078947</td>\n",
-       "      <td>0.019933</td>\n",
-       "      <td>0.012092</td>\n",
+       "      <td>[533120.25, 538624.5, 545153.5]</td>\n",
+       "      <td>1.263294e+04</td>\n",
+       "      <td>1.595913e+08</td>\n",
+       "      <td>0.014205</td>\n",
+       "      <td>0.005249</td>\n",
+       "      <td>0.004810</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>False</td>\n",
-       "      <td>[0.00871275, 0.012091999999999999, 0.0234505]</td>\n",
-       "      <td>0.021524</td>\n",
-       "      <td>0.000463</td>\n",
-       "      <td>0.060791</td>\n",
+       "      <td>True</td>\n",
+       "      <td>[0.00244575, 0.00481, 0.006431]</td>\n",
+       "      <td>0.004187</td>\n",
+       "      <td>0.000018</td>\n",
+       "      <td>-0.182372</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>0.000215</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>1.982438e+09</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.00022325, 0.0002315, 0.000243]</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>1.560100e-10</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.311366</td>\n",
-       "      <td>0.027067</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>False</td>\n",
-       "      <td>[0.0, 0.027067, 0.764881]</td>\n",
-       "      <td>0.451230</td>\n",
-       "      <td>0.203608</td>\n",
-       "      <td>0.320908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>1.275210</td>\n",
+       "      <td>[1988997772.0, 2001817999.5, 2004877745.25]</td>\n",
+       "      <td>9.110282e+06</td>\n",
+       "      <td>8.299723e+13</td>\n",
+       "      <td>0.992988</td>\n",
+       "      <td>0.978926</td>\n",
+       "      <td>0.984356</td>\n",
+       "      <td>0.956393</td>\n",
+       "      <td>True</td>\n",
+       "      <td>[0.968337, 0.984356, 0.9885772500000001]</td>\n",
+       "      <td>0.012040</td>\n",
+       "      <td>0.000145</td>\n",
+       "      <td>-0.078788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>2.006252e+09</td>\n",
        "      <td>True</td>\n",
-       "      <td>[1.29070175, 1.2940295000000002, 1.30458350000...</td>\n",
-       "      <td>0.017678</td>\n",
-       "      <td>3.125018e-04</td>\n",
-       "      <td>0.998832</td>\n",
-       "      <td>0.996608</td>\n",
-       "      <td>0.996824</td>\n",
-       "      <td>0.993177</td>\n",
+       "      <td>[2014797196.0, 2018832431.0, 2027420716.75]</td>\n",
+       "      <td>8.974735e+06</td>\n",
+       "      <td>8.054587e+13</td>\n",
+       "      <td>0.997550</td>\n",
+       "      <td>0.990934</td>\n",
+       "      <td>0.991464</td>\n",
+       "      <td>0.980433</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.99538975, 0.9968239999999999, 0.99820775]</td>\n",
-       "      <td>0.001797</td>\n",
-       "      <td>0.000003</td>\n",
-       "      <td>-0.309091</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.722036</td>\n",
+       "      <td>[0.98897875, 0.991464, 0.9943077499999999]</td>\n",
+       "      <td>0.005125</td>\n",
+       "      <td>0.000026</td>\n",
+       "      <td>-0.090909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>4.467119e+08</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.734351, 0.7368325, 0.74190975]</td>\n",
-       "      <td>0.008339</td>\n",
-       "      <td>6.953802e-05</td>\n",
-       "      <td>0.978771</td>\n",
-       "      <td>0.944047</td>\n",
-       "      <td>0.949658</td>\n",
-       "      <td>0.905950</td>\n",
+       "      <td>[448020898.75, 449819291.0, 450391779.5]</td>\n",
+       "      <td>1.482635e+06</td>\n",
+       "      <td>2.198205e+12</td>\n",
+       "      <td>0.994624</td>\n",
+       "      <td>0.922818</td>\n",
+       "      <td>0.925987</td>\n",
+       "      <td>0.770469</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0.9266765, 0.9496575, 0.9571795]</td>\n",
-       "      <td>0.022419</td>\n",
-       "      <td>0.000503</td>\n",
-       "      <td>-0.042424</td>\n",
+       "      <td>[0.8846215, 0.9259875, 0.987635]</td>\n",
+       "      <td>0.068656</td>\n",
+       "      <td>0.004714</td>\n",
+       "      <td>0.575758</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                              \\\n",
-       "                                                                        name   \n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                          \\\n",
+       "                                                                    name   \n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                            Clang  \\\n",
        "                                                   time (exc)_max   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.511030e+08   \n",
        "\n",
        "                                                                    \\\n",
        "                                                   time (exc)_mean   \n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.492754e+08   \n",
        "\n",
        "                                                                      \\\n",
        "                                                   time (exc)_median   \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                    0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                   0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...          1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...          0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}               5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...      4.498193e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_min   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.467119e+08   \n",
        "\n",
        "                                                                         \\\n",
        "                                                   time (exc)_normality   \n",
        "node                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                           True   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                           True   \n",
        "{'name': 'Algorithm', 'type': 'function'}                          True   \n",
        "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                   True   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                 True   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                 True   \n",
-       "\n",
-       "                                                                                                       \\\n",
-       "                                                                               time (exc)_percentiles   \n",
-       "node                                                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}            [0.0070557499999999995, 0.007194000000000001, ...   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                            [0.00058425, 0.000598, 0.000603]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                    [0.00022325, 0.0002315, 0.000243]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  [1.29070175, 1.2940295000000002, 1.30458350000...   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                  [0.734351, 0.7368325, 0.74190975]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                   True   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                 True   \n",
+       "\n",
+       "                                                                                                 \\\n",
+       "                                                                         time (exc)_percentiles   \n",
+       "node                                                                                              \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     [4723135.0, 4792513.5, 4834294.25]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       [533120.25, 538624.5, 545153.5]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}    [1988997772.0, 2001817999.5, 2004877745.25]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}    [2014797196.0, 2018832431.0, 2027420716.75]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     [448020898.75, 449819291.0, 450391779.5]   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_std   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.000160   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000019   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       0.017678   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.008339   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             6.919881e+04   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            1.263294e+04   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     9.110282e+06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     8.974735e+06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   1.482635e+06   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_var   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}             2.549456e-08   \n",
-       "{'name': 'Algorithm', 'type': 'function'}            3.652100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.560100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...   3.125018e-04   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...   6.953802e-05   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.788476e+09   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            1.595913e+08   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     8.299723e+13   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     8.054587e+13   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   2.198205e+12   \n",
        "\n",
        "                                                                  GCC  \\\n",
        "                                                   Machine clears_max   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.034919   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.078947   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.998832   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.978771   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.004523   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.014205   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.992988   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.997550   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.994624   \n",
        "\n",
        "                                                                        \\\n",
        "                                                   Machine clears_mean   \n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.027250   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.019933   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.311366   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.996608   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.944047   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.002853   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.005249   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.978926   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.990934   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.922818   \n",
        "\n",
        "                                                                          \\\n",
        "                                                   Machine clears_median   \n",
        "node                                                                       \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                        0.026362   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                       0.012092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.027067   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...              0.996824   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...              0.949658   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        0.003061   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       0.004810   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.984356   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                0.991464   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...              0.925987   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_min   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.014810   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001071   \n",
        "{'name': 'Algorithm', 'type': 'function'}                    0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.993177   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.905950   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.956393   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.980433   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.770469   \n",
        "\n",
        "                                                                             \\\n",
        "                                                   Machine clears_normality   \n",
        "node                                                                          \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                               True   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                             False   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      False   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                     True   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                     True   \n",
-       "\n",
-       "                                                                                                   \\\n",
-       "                                                                       Machine clears_percentiles   \n",
-       "node                                                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}            [0.02477725, 0.026362499999999997, 0.0327565]   \n",
-       "{'name': 'Algorithm', 'type': 'function'}           [0.00871275, 0.012091999999999999, 0.0234505]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                        [0.0, 0.027067, 0.764881]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...   [0.99538975, 0.9968239999999999, 0.99820775]   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...              [0.9266765, 0.9496575, 0.9571795]   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                               True   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                              True   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                       True   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                       True   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                     True   \n",
+       "\n",
+       "                                                                                                    \\\n",
+       "                                                                        Machine clears_percentiles   \n",
+       "node                                                                                                 \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}            [0.0018989999999999999, 0.0030615, 0.00361175]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                          [0.00244575, 0.00481, 0.006431]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          [0.968337, 0.984356, 0.9885772500000001]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        [0.98897875, 0.991464, 0.9943077499999999]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                [0.8846215, 0.9259875, 0.987635]   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_std   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.005898   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.021524   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.451230   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.001797   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.022419   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001096   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.004187   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.012040   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.005125   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.068656   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_var   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.000035   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000463   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.203608   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.000003   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.000503   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.000001   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.000018   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000145   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.000026   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.004714   \n",
        "\n",
        "                                                                        Union statistics  \n",
        "                                                   time (exc)_vs_Machine clears spearman  \n",
        "node                                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                       -0.139394  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                       0.060791  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                0.320908  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                             -0.309091  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                             -0.042424  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                                        0.115152  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                      -0.182372  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                               -0.078788  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                               -0.090909  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                              0.575758  "
       ]
      },
      "execution_count": 40,
@@ -6943,13 +6943,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce188658",
+   "id": "100bba9a",
    "metadata": {
     "papermill": {
-     "duration": 0.007785,
-     "end_time": "2023-12-09T20:58:46.471046",
+     "duration": 0.008694,
+     "end_time": "2023-12-22T05:36:10.998704",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.463261",
+     "start_time": "2023-12-22T05:36:10.990010",
      "status": "completed"
     },
     "tags": []
@@ -6966,13 +6966,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32ee2881",
+   "id": "cbbc7da4",
    "metadata": {
     "papermill": {
-     "duration": 0.007758,
-     "end_time": "2023-12-09T20:58:46.486785",
+     "duration": 0.00878,
+     "end_time": "2023-12-22T05:36:11.016137",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.479027",
+     "start_time": "2023-12-22T05:36:11.007357",
      "status": "completed"
     },
     "tags": []
@@ -6984,19 +6984,19 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "6c2b8157",
+   "id": "27583071",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.503461Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.503335Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.504970Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.504642Z"
+     "iopub.execute_input": "2023-12-22T05:36:11.033947Z",
+     "iopub.status.busy": "2023-12-22T05:36:11.033800Z",
+     "iopub.status.idle": "2023-12-22T05:36:11.036001Z",
+     "shell.execute_reply": "2023-12-22T05:36:11.035457Z"
     },
     "papermill": {
-     "duration": 0.010679,
-     "end_time": "2023-12-09T20:58:46.505597",
+     "duration": 0.012035,
+     "end_time": "2023-12-22T05:36:11.036758",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.494918",
+     "start_time": "2023-12-22T05:36:11.024723",
      "status": "completed"
     },
     "tags": []
@@ -7009,19 +7009,19 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "79e5b37d",
+   "id": "ac4d4f9b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.522029Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.521915Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.601583Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.601295Z"
+     "iopub.execute_input": "2023-12-22T05:36:11.055639Z",
+     "iopub.status.busy": "2023-12-22T05:36:11.055517Z",
+     "iopub.status.idle": "2023-12-22T05:36:11.133784Z",
+     "shell.execute_reply": "2023-12-22T05:36:11.133421Z"
     },
     "papermill": {
-     "duration": 0.088303,
-     "end_time": "2023-12-09T20:58:46.602096",
+     "duration": 0.088458,
+     "end_time": "2023-12-22T05:36:11.134751",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.513793",
+     "start_time": "2023-12-22T05:36:11.046293",
      "status": "completed"
     },
     "tags": []
@@ -7129,440 +7129,440 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.044086</td>\n",
-       "      <td>0.006875</td>\n",
-       "      <td>0.015849</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.027724</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.027126</td>\n",
-       "      <td>2.549456e-08</td>\n",
-       "      <td>0.000072</td>\n",
-       "      <td>0.000160</td>\n",
-       "      <td>0.008508</td>\n",
-       "      <td>[0.0070557499999999995, 0.007194000000000001, ...</td>\n",
-       "      <td>[0.02044975, 0.027724, 0.032329]</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>0.003932</td>\n",
+       "      <td>4.696939e+06</td>\n",
+       "      <td>0.001099</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>0.002209</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>0.002266</td>\n",
+       "      <td>4.788476e+09</td>\n",
+       "      <td>8.279246e-07</td>\n",
+       "      <td>6.919881e+04</td>\n",
+       "      <td>0.000910</td>\n",
+       "      <td>[4723135.0, 4792513.5, 4834294.25]</td>\n",
+       "      <td>[0.00157425, 0.002209, 0.002657]</td>\n",
        "      <td>True</td>\n",
        "      <td>True</td>\n",
-       "      <td>-0.248485</td>\n",
-       "      <td>0.007056</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.007235</td>\n",
-       "      <td>0.000180</td>\n",
-       "      <td>0.006786</td>\n",
-       "      <td>0.007505</td>\n",
+       "      <td>0.503030</td>\n",
+       "      <td>4.723135e+06</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>4.834294e+06</td>\n",
+       "      <td>111159.25</td>\n",
+       "      <td>4.556396e+06</td>\n",
+       "      <td>5.001033e+06</td>\n",
        "      <td>[]</td>\n",
-       "      <td>0.020450</td>\n",
-       "      <td>0.027724</td>\n",
-       "      <td>0.032329</td>\n",
-       "      <td>0.011879</td>\n",
-       "      <td>0.002631</td>\n",
-       "      <td>0.050148</td>\n",
+       "      <td>0.001574</td>\n",
+       "      <td>0.002209</td>\n",
+       "      <td>0.002657</td>\n",
+       "      <td>0.001083</td>\n",
+       "      <td>-0.000050</td>\n",
+       "      <td>0.004281</td>\n",
        "      <td>[]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.045977</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.004092</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.012778</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.017969</td>\n",
-       "      <td>3.652100e-10</td>\n",
-       "      <td>0.000220</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>0.014843</td>\n",
-       "      <td>[0.00058425, 0.000598, 0.000603]</td>\n",
-       "      <td>[0.00833475, 0.0127785, 0.019187000000000003]</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>0.019851</td>\n",
+       "      <td>5.176070e+05</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>0.004722</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>0.007061</td>\n",
+       "      <td>1.595913e+08</td>\n",
+       "      <td>3.895210e-05</td>\n",
+       "      <td>1.263294e+04</td>\n",
+       "      <td>0.006241</td>\n",
+       "      <td>[533120.25, 538624.5, 545153.5]</td>\n",
+       "      <td>[0.0025005, 0.0047220000000000005, 0.00878175]</td>\n",
        "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>-0.182372</td>\n",
-       "      <td>0.000584</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.000603</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>0.000556</td>\n",
-       "      <td>0.000631</td>\n",
-       "      <td>[-8296401458019794746]</td>\n",
-       "      <td>0.008335</td>\n",
-       "      <td>0.012778</td>\n",
-       "      <td>0.019187</td>\n",
-       "      <td>0.010852</td>\n",
-       "      <td>-0.007944</td>\n",
-       "      <td>0.035465</td>\n",
-       "      <td>[-2692892625006755850, -2098688516509049969]</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-0.660606</td>\n",
+       "      <td>5.331202e+05</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>5.451535e+05</td>\n",
+       "      <td>12033.25</td>\n",
+       "      <td>5.150704e+05</td>\n",
+       "      <td>5.632034e+05</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0.002501</td>\n",
+       "      <td>0.004722</td>\n",
+       "      <td>0.008782</td>\n",
+       "      <td>0.006281</td>\n",
+       "      <td>-0.006921</td>\n",
+       "      <td>0.018204</td>\n",
+       "      <td>[8439404752619262298]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000215</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.230749</td>\n",
-       "      <td>1.560100e-10</td>\n",
-       "      <td>0.169282</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.411439</td>\n",
-       "      <td>[0.00022325, 0.0002315, 0.000243]</td>\n",
-       "      <td>[nan, nan, nan]</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>0.997907</td>\n",
+       "      <td>1.982438e+09</td>\n",
+       "      <td>0.990401</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>0.996143</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>0.995425</td>\n",
+       "      <td>8.299723e+13</td>\n",
+       "      <td>4.977776e-06</td>\n",
+       "      <td>9.110282e+06</td>\n",
+       "      <td>0.002231</td>\n",
+       "      <td>[1988997772.0, 2001817999.5, 2004877745.25]</td>\n",
+       "      <td>[0.99408025, 0.9961425, 0.9970844999999999]</td>\n",
        "      <td>True</td>\n",
        "      <td>True</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000223</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>0.000243</td>\n",
-       "      <td>0.000020</td>\n",
-       "      <td>0.000194</td>\n",
-       "      <td>0.000273</td>\n",
+       "      <td>0.078788</td>\n",
+       "      <td>1.988998e+09</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>2.004878e+09</td>\n",
+       "      <td>15879973.25</td>\n",
+       "      <td>1.965178e+09</td>\n",
+       "      <td>2.028698e+09</td>\n",
        "      <td>[]</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.994080</td>\n",
+       "      <td>0.996143</td>\n",
+       "      <td>0.997084</td>\n",
+       "      <td>0.003004</td>\n",
+       "      <td>0.989574</td>\n",
+       "      <td>1.001591</td>\n",
        "      <td>[]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>0.998909</td>\n",
-       "      <td>1.275210</td>\n",
-       "      <td>0.987818</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>0.998147</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>0.996256</td>\n",
-       "      <td>3.125018e-04</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.017678</td>\n",
-       "      <td>0.003459</td>\n",
-       "      <td>[1.29070175, 1.2940295000000002, 1.30458350000...</td>\n",
-       "      <td>[0.99564675, 0.9981475, 0.998563]</td>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>0.997923</td>\n",
+       "      <td>2.006252e+09</td>\n",
+       "      <td>0.988505</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>0.993871</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>0.993798</td>\n",
+       "      <td>8.054587e+13</td>\n",
+       "      <td>8.699840e-06</td>\n",
+       "      <td>8.974735e+06</td>\n",
+       "      <td>0.002950</td>\n",
+       "      <td>[2014797196.0, 2018832431.0, 2027420716.75]</td>\n",
+       "      <td>[0.99133675, 0.993871, 0.9959795]</td>\n",
        "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>-0.066667</td>\n",
-       "      <td>1.290702</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>1.304584</td>\n",
-       "      <td>0.013882</td>\n",
-       "      <td>1.269879</td>\n",
-       "      <td>1.325406</td>\n",
-       "      <td>[-2628646056497023044]</td>\n",
-       "      <td>0.995647</td>\n",
-       "      <td>0.998147</td>\n",
-       "      <td>0.998563</td>\n",
-       "      <td>0.002916</td>\n",
-       "      <td>0.991272</td>\n",
-       "      <td>1.002937</td>\n",
-       "      <td>[-5455622963296541348]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.973024</td>\n",
-       "      <td>0.722036</td>\n",
-       "      <td>0.906853</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.953007</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.948031</td>\n",
-       "      <td>6.953802e-05</td>\n",
-       "      <td>0.000392</td>\n",
-       "      <td>0.008339</td>\n",
-       "      <td>0.019788</td>\n",
-       "      <td>[0.734351, 0.7368325, 0.74190975]</td>\n",
-       "      <td>[0.940767, 0.9530069999999999, 0.9630295]</td>\n",
        "      <td>True</td>\n",
+       "      <td>0.054545</td>\n",
+       "      <td>2.014797e+09</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>2.027421e+09</td>\n",
+       "      <td>12623520.75</td>\n",
+       "      <td>1.995862e+09</td>\n",
+       "      <td>2.046356e+09</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0.991337</td>\n",
+       "      <td>0.993871</td>\n",
+       "      <td>0.995980</td>\n",
+       "      <td>0.004643</td>\n",
+       "      <td>0.984373</td>\n",
+       "      <td>1.002944</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>0.996756</td>\n",
+       "      <td>4.467119e+08</td>\n",
+       "      <td>0.954967</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>0.990938</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>0.981222</td>\n",
+       "      <td>2.198205e+12</td>\n",
+       "      <td>2.352473e-04</td>\n",
+       "      <td>1.482635e+06</td>\n",
+       "      <td>0.015338</td>\n",
+       "      <td>[448020898.75, 449819291.0, 450391779.5]</td>\n",
+       "      <td>[0.9703815, 0.990938, 0.99332575]</td>\n",
        "      <td>True</td>\n",
-       "      <td>-0.163636</td>\n",
-       "      <td>0.734351</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.741910</td>\n",
-       "      <td>0.007559</td>\n",
-       "      <td>0.723013</td>\n",
-       "      <td>0.753248</td>\n",
-       "      <td>[-8296401458019794746, 8522253423718309334]</td>\n",
-       "      <td>0.940767</td>\n",
-       "      <td>0.953007</td>\n",
-       "      <td>0.963029</td>\n",
-       "      <td>0.022262</td>\n",
-       "      <td>0.907373</td>\n",
-       "      <td>0.996423</td>\n",
-       "      <td>[622673755545357412]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-0.345455</td>\n",
+       "      <td>4.480209e+08</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>4.503918e+08</td>\n",
+       "      <td>2370880.75</td>\n",
+       "      <td>4.444646e+08</td>\n",
+       "      <td>4.539481e+08</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0.970382</td>\n",
+       "      <td>0.990938</td>\n",
+       "      <td>0.993326</td>\n",
+       "      <td>0.022944</td>\n",
+       "      <td>0.935965</td>\n",
+       "      <td>1.027742</td>\n",
+       "      <td>[]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                        name  \\\n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                    name  \\\n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                    time (exc)_max  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.511030e+08   \n",
        "\n",
        "                                                    Machine clears_max  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.044086   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.045977   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.998909   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.973024   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.003932   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.019851   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.997907   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.997923   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.996756   \n",
        "\n",
        "                                                    time (exc)_min  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.467119e+08   \n",
        "\n",
        "                                                    Machine clears_min  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.015849   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.004092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.987818   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.906853   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.001099   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.000000   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.990401   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.988505   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.954967   \n",
        "\n",
        "                                                    time (exc)_median  \\\n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                 4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}         2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...       4.498193e+08   \n",
        "\n",
        "                                                    Machine clears_median  \\\n",
        "node                                                                        \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                         0.027724   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                        0.012778   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...               0.998147   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...               0.953007   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                         0.002209   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                        0.004722   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                 0.996143   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                 0.993871   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...               0.990938   \n",
        "\n",
        "                                                    time (exc)_mean  \\\n",
        "node                                                                  \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                   0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                  0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}           0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...         1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...         0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}               4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}              5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}       1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}       2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     4.492754e+08   \n",
        "\n",
        "                                                    Machine clears_mean  \\\n",
        "node                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                       0.027126   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                      0.017969   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.230749   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...             0.996256   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...             0.948031   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                       0.002266   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      0.007061   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}               0.995425   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}               0.993798   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...             0.981222   \n",
        "\n",
        "                                                    time (exc)_var  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}              2.549456e-08   \n",
-       "{'name': 'Algorithm', 'type': 'function'}             3.652100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.560100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...    3.125018e-04   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...    6.953802e-05   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788476e+09   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             1.595913e+08   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      8.299723e+13   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      8.054587e+13   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    2.198205e+12   \n",
        "\n",
        "                                                    Machine clears_var  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.000072   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.000220   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.169282   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.000392   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                  8.279246e-07   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                 3.895210e-05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          4.977776e-06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}          8.699840e-06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...        2.352473e-04   \n",
        "\n",
        "                                                    time (exc)_std  \\\n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.000160   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000019   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        0.017678   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.008339   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              6.919881e+04   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             1.263294e+04   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      9.110282e+06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      8.974735e+06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    1.482635e+06   \n",
        "\n",
        "                                                    Machine clears_std  \\\n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.008508   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.014843   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.411439   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.003459   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.019788   \n",
-       "\n",
-       "                                                                               time (exc)_percentiles  \\\n",
-       "node                                                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}            [0.0070557499999999995, 0.007194000000000001, ...   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                            [0.00058425, 0.000598, 0.000603]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                    [0.00022325, 0.0002315, 0.000243]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  [1.29070175, 1.2940295000000002, 1.30458350000...   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                  [0.734351, 0.7368325, 0.74190975]   \n",
-       "\n",
-       "                                                                       Machine clears_percentiles  \\\n",
-       "node                                                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                         [0.02044975, 0.027724, 0.032329]   \n",
-       "{'name': 'Algorithm', 'type': 'function'}           [0.00833475, 0.0127785, 0.019187000000000003]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                  [nan, nan, nan]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...              [0.99564675, 0.9981475, 0.998563]   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...      [0.940767, 0.9530069999999999, 0.9630295]   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.000910   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.006241   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.002231   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.002950   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.015338   \n",
+       "\n",
+       "                                                                         time (exc)_percentiles  \\\n",
+       "node                                                                                              \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     [4723135.0, 4792513.5, 4834294.25]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       [533120.25, 538624.5, 545153.5]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}    [1988997772.0, 2001817999.5, 2004877745.25]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}    [2014797196.0, 2018832431.0, 2027420716.75]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     [448020898.75, 449819291.0, 450391779.5]   \n",
+       "\n",
+       "                                                                        Machine clears_percentiles  \\\n",
+       "node                                                                                                 \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                          [0.00157425, 0.002209, 0.002657]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}           [0.0025005, 0.0047220000000000005, 0.00878175]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}       [0.99408025, 0.9961425, 0.9970844999999999]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                 [0.99133675, 0.993871, 0.9959795]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...               [0.9703815, 0.990938, 0.99332575]   \n",
        "\n",
        "                                                   time (exc)_normality  \\\n",
        "node                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                           True   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                           True   \n",
        "{'name': 'Algorithm', 'type': 'function'}                          True   \n",
        "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                   True   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                 True   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                 True   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                   True   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                 True   \n",
        "\n",
        "                                                   Machine clears_normality  \\\n",
        "node                                                                          \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                               True   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                             False   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                               True   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                              True   \n",
        "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                       True   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                    False   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                     True   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                       True   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                    False   \n",
        "\n",
        "                                                    time (exc)_vs_Machine clears spearman  \\\n",
        "node                                                                                        \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                        -0.248485   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                       -0.182372   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                      NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                              -0.066667   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                              -0.163636   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                         0.503030   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                       -0.660606   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                 0.078788   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                                 0.054545   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                              -0.345455   \n",
        "\n",
        "                                                    time (exc)_q1(0.25, 0.5, 0.75)  \\\n",
        "node                                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                  0.007056   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                 0.000584   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                          0.000223   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                        1.290702   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                        0.734351   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                              4.723135e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                             5.331202e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      1.988998e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                      2.014797e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                    4.480209e+08   \n",
        "\n",
        "                                                    time (exc)_q2(0.25, 0.5, 0.75)  \\\n",
        "node                                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                  0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                 0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                          0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                        1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                        0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                              4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                             5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                      2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                    4.498193e+08   \n",
        "\n",
        "                                                    time (exc)_q3(0.25, 0.5, 0.75)  \\\n",
        "node                                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                  0.007235   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                 0.000603   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                          0.000243   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                        1.304584   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                        0.741910   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                              4.834294e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                             5.451535e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      2.004878e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                      2.027421e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                    4.503918e+08   \n",
        "\n",
        "                                                    time (exc)_iqr(0.25, 0.5, 0.75)  \\\n",
        "node                                                                                  \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                   0.000180   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                  0.000019   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                           0.000020   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                         0.013882   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                         0.007559   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                  111159.25   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                  12033.25   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                        15879973.25   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                        12623520.75   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                       2370880.75   \n",
        "\n",
        "                                                    time (exc)_lowerfence(0.25, 0.5, 0.75)  \\\n",
        "node                                                                                         \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                          0.006786   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                         0.000556   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                  0.000194   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                                1.269879   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                                0.723013   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                      4.556396e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                     5.150704e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                              1.965178e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                              1.995862e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                            4.444646e+08   \n",
        "\n",
        "                                                    time (exc)_upperfence(0.25, 0.5, 0.75)  \\\n",
        "node                                                                                         \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                          0.007505   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                         0.000631   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                  0.000273   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                                1.325406   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                                0.753248   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                      5.001033e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                     5.632034e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                              2.028698e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                              2.046356e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                            4.539481e+08   \n",
        "\n",
-       "                                                           time (exc)_outliers(0.25, 0.5, 0.75)  \\\n",
-       "node                                                                                              \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                                     []   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                [-8296401458019794746]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                             []   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                       [-2628646056497023044]   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  [-8296401458019794746, 8522253423718309334]   \n",
+       "                                                   time (exc)_outliers(0.25, 0.5, 0.75)  \\\n",
+       "node                                                                                      \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                             []   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                            []   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                     []   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                                     []   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                                   []   \n",
        "\n",
        "                                                    Machine clears_q1(0.25, 0.5, 0.75)  \\\n",
        "node                                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                      0.020450   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                     0.008335   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                   NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                            0.995647   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                            0.940767   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                      0.001574   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                     0.002501   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                              0.994080   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                              0.991337   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                            0.970382   \n",
        "\n",
        "                                                    Machine clears_q2(0.25, 0.5, 0.75)  \\\n",
        "node                                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                      0.027724   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                     0.012778   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                   NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                            0.998147   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                            0.953007   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                      0.002209   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                     0.004722   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                              0.996143   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                              0.993871   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                            0.990938   \n",
        "\n",
        "                                                    Machine clears_q3(0.25, 0.5, 0.75)  \\\n",
        "node                                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                      0.032329   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                     0.019187   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                   NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                            0.998563   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                            0.963029   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                      0.002657   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                     0.008782   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                              0.997084   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                              0.995980   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                            0.993326   \n",
        "\n",
        "                                                    Machine clears_iqr(0.25, 0.5, 0.75)  \\\n",
        "node                                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                       0.011879   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                      0.010852   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                    NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                             0.002916   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                             0.022262   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                       0.001083   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                      0.006281   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                               0.003004   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                               0.004643   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                             0.022944   \n",
        "\n",
        "                                                    Machine clears_lowerfence(0.25, 0.5, 0.75)  \\\n",
        "node                                                                                             \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                              0.002631   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                            -0.007944   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                           NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                                    0.991272   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                                    0.907373   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                             -0.000050   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                            -0.006921   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                      0.989574   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                                      0.984373   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                                    0.935965   \n",
        "\n",
        "                                                    Machine clears_upperfence(0.25, 0.5, 0.75)  \\\n",
        "node                                                                                             \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                              0.050148   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                             0.035465   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                           NaN   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                                    1.002937   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                                    0.996423   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                              0.004281   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                             0.018204   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                      1.001591   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                                      1.002944   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                                    1.027742   \n",
        "\n",
-       "                                                        Machine clears_outliers(0.25, 0.5, 0.75)  \n",
-       "node                                                                                              \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                                      []  \n",
-       "{'name': 'Algorithm', 'type': 'function'}           [-2692892625006755850, -2098688516509049969]  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                              []  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                        [-5455622963296541348]  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                          [622673755545357412]  "
+       "                                                   Machine clears_outliers(0.25, 0.5, 0.75)  \n",
+       "node                                                                                         \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                                 []  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                             [8439404752619262298]  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                         []  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                                         []  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                                       []  "
       ]
      },
      "execution_count": 42,
@@ -7577,13 +7577,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "694d4691",
+   "id": "d0c5d19f",
    "metadata": {
     "papermill": {
-     "duration": 0.00818,
-     "end_time": "2023-12-09T20:58:46.618840",
+     "duration": 0.011933,
+     "end_time": "2023-12-22T05:36:11.156434",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.610660",
+     "start_time": "2023-12-22T05:36:11.144501",
      "status": "completed"
     },
     "tags": []
@@ -7599,19 +7599,19 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "c35dce24",
+   "id": "cdabd6ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.636088Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.635993Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.637686Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.637466Z"
+     "iopub.execute_input": "2023-12-22T05:36:11.175659Z",
+     "iopub.status.busy": "2023-12-22T05:36:11.175511Z",
+     "iopub.status.idle": "2023-12-22T05:36:11.177615Z",
+     "shell.execute_reply": "2023-12-22T05:36:11.177267Z"
     },
     "papermill": {
-     "duration": 0.010782,
-     "end_time": "2023-12-09T20:58:46.638195",
+     "duration": 0.012675,
+     "end_time": "2023-12-22T05:36:11.178387",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.627413",
+     "start_time": "2023-12-22T05:36:11.165712",
      "status": "completed"
     },
     "tags": []
@@ -7624,19 +7624,19 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "204e56c8",
+   "id": "4b04dc0f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.655304Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.655209Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.740969Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.740648Z"
+     "iopub.execute_input": "2023-12-22T05:36:11.197227Z",
+     "iopub.status.busy": "2023-12-22T05:36:11.197109Z",
+     "iopub.status.idle": "2023-12-22T05:36:11.256388Z",
+     "shell.execute_reply": "2023-12-22T05:36:11.255987Z"
     },
     "papermill": {
-     "duration": 0.094725,
-     "end_time": "2023-12-09T20:58:46.741473",
+     "duration": 0.069417,
+     "end_time": "2023-12-22T05:36:11.257039",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.646748",
+     "start_time": "2023-12-22T05:36:11.187622",
      "status": "completed"
     },
     "tags": []
@@ -7755,472 +7755,472 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>{'name': 'Base_Seq', 'type': 'function'}</th>\n",
-       "      <td>Base_Seq</td>\n",
-       "      <td>0.000180</td>\n",
-       "      <td>0.006786</td>\n",
-       "      <td>0.007418</td>\n",
-       "      <td>0.007147</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.006875</td>\n",
+       "      <th>{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
+       "      <td>RAJAPerf</td>\n",
+       "      <td>111159.25</td>\n",
+       "      <td>4.556396e+06</td>\n",
+       "      <td>4.922397e+06</td>\n",
+       "      <td>4.788171e+06</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>4.696939e+06</td>\n",
        "      <td>True</td>\n",
        "      <td>[]</td>\n",
-       "      <td>[0.0070557499999999995, 0.007194000000000001, ...</td>\n",
-       "      <td>0.007056</td>\n",
-       "      <td>0.007194</td>\n",
-       "      <td>0.007235</td>\n",
-       "      <td>0.000160</td>\n",
-       "      <td>0.007505</td>\n",
-       "      <td>2.549456e-08</td>\n",
-       "      <td>0.007979</td>\n",
-       "      <td>0.012808</td>\n",
-       "      <td>0.034919</td>\n",
-       "      <td>0.027250</td>\n",
-       "      <td>0.026362</td>\n",
-       "      <td>0.014810</td>\n",
+       "      <td>[4723135.0, 4792513.5, 4834294.25]</td>\n",
+       "      <td>4.723135e+06</td>\n",
+       "      <td>4.792514e+06</td>\n",
+       "      <td>4.834294e+06</td>\n",
+       "      <td>6.919881e+04</td>\n",
+       "      <td>5.001033e+06</td>\n",
+       "      <td>4.788476e+09</td>\n",
+       "      <td>0.001713</td>\n",
+       "      <td>-0.000670</td>\n",
+       "      <td>0.004523</td>\n",
+       "      <td>0.002853</td>\n",
+       "      <td>0.003061</td>\n",
+       "      <td>0.001071</td>\n",
        "      <td>True</td>\n",
        "      <td>[]</td>\n",
-       "      <td>[0.02477725, 0.026362499999999997, 0.0327565]</td>\n",
-       "      <td>0.024777</td>\n",
-       "      <td>0.026362</td>\n",
-       "      <td>0.032757</td>\n",
-       "      <td>0.005898</td>\n",
-       "      <td>0.044725</td>\n",
-       "      <td>0.000035</td>\n",
-       "      <td>-0.139394</td>\n",
+       "      <td>[0.0018989999999999999, 0.0030615, 0.00361175]</td>\n",
+       "      <td>0.001899</td>\n",
+       "      <td>0.003061</td>\n",
+       "      <td>0.003612</td>\n",
+       "      <td>0.001096</td>\n",
+       "      <td>0.006181</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.115152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm', 'type': 'function'}</th>\n",
        "      <td>Algorithm</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>0.000556</td>\n",
-       "      <td>0.000637</td>\n",
-       "      <td>0.000597</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.000564</td>\n",
+       "      <td>12033.25</td>\n",
+       "      <td>5.150704e+05</td>\n",
+       "      <td>5.576280e+05</td>\n",
+       "      <td>5.383793e+05</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>5.176070e+05</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0]</td>\n",
-       "      <td>[0.00058425, 0.000598, 0.000603]</td>\n",
-       "      <td>0.000584</td>\n",
-       "      <td>0.000598</td>\n",
-       "      <td>0.000603</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>0.000631</td>\n",
-       "      <td>3.652100e-10</td>\n",
-       "      <td>0.014738</td>\n",
-       "      <td>-0.013394</td>\n",
-       "      <td>0.078947</td>\n",
-       "      <td>0.019933</td>\n",
-       "      <td>0.012092</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[533120.25, 538624.5, 545153.5]</td>\n",
+       "      <td>5.331202e+05</td>\n",
+       "      <td>5.386245e+05</td>\n",
+       "      <td>5.451535e+05</td>\n",
+       "      <td>1.263294e+04</td>\n",
+       "      <td>5.632034e+05</td>\n",
+       "      <td>1.595913e+08</td>\n",
+       "      <td>0.003985</td>\n",
+       "      <td>-0.003532</td>\n",
+       "      <td>0.014205</td>\n",
+       "      <td>0.005249</td>\n",
+       "      <td>0.004810</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>False</td>\n",
-       "      <td>[2]</td>\n",
-       "      <td>[0.00871275, 0.012091999999999999, 0.0234505]</td>\n",
-       "      <td>0.008713</td>\n",
-       "      <td>0.012092</td>\n",
-       "      <td>0.023450</td>\n",
-       "      <td>0.021524</td>\n",
-       "      <td>0.045557</td>\n",
-       "      <td>0.000463</td>\n",
-       "      <td>0.060791</td>\n",
+       "      <td>True</td>\n",
+       "      <td>[4]</td>\n",
+       "      <td>[0.00244575, 0.00481, 0.006431]</td>\n",
+       "      <td>0.002446</td>\n",
+       "      <td>0.004810</td>\n",
+       "      <td>0.006431</td>\n",
+       "      <td>0.004187</td>\n",
+       "      <td>0.012409</td>\n",
+       "      <td>0.000018</td>\n",
+       "      <td>-0.182372</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
-       "      <td>0.000020</td>\n",
-       "      <td>0.000194</td>\n",
-       "      <td>0.000252</td>\n",
-       "      <td>0.000233</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>0.000215</td>\n",
+       "      <td>15879973.25</td>\n",
+       "      <td>1.965178e+09</td>\n",
+       "      <td>2.008037e+09</td>\n",
+       "      <td>1.997560e+09</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>1.982438e+09</td>\n",
        "      <td>True</td>\n",
        "      <td>[]</td>\n",
-       "      <td>[0.00022325, 0.0002315, 0.000243]</td>\n",
-       "      <td>0.000223</td>\n",
-       "      <td>0.000231</td>\n",
-       "      <td>0.000243</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.000273</td>\n",
-       "      <td>1.560100e-10</td>\n",
-       "      <td>0.764881</td>\n",
-       "      <td>-1.147322</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.311366</td>\n",
-       "      <td>0.027067</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>False</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>[0.0, 0.027067, 0.764881]</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.027067</td>\n",
-       "      <td>0.764881</td>\n",
-       "      <td>0.451230</td>\n",
-       "      <td>1.912203</td>\n",
-       "      <td>0.203608</td>\n",
-       "      <td>0.320908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.default', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.default</td>\n",
-       "      <td>0.013882</td>\n",
-       "      <td>1.269879</td>\n",
-       "      <td>1.343627</td>\n",
-       "      <td>1.298458</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>1.275210</td>\n",
+       "      <td>[1988997772.0, 2001817999.5, 2004877745.25]</td>\n",
+       "      <td>1.988998e+09</td>\n",
+       "      <td>2.001818e+09</td>\n",
+       "      <td>2.004878e+09</td>\n",
+       "      <td>9.110282e+06</td>\n",
+       "      <td>2.028698e+09</td>\n",
+       "      <td>8.299723e+13</td>\n",
+       "      <td>0.020240</td>\n",
+       "      <td>0.937977</td>\n",
+       "      <td>0.992988</td>\n",
+       "      <td>0.978926</td>\n",
+       "      <td>0.984356</td>\n",
+       "      <td>0.956393</td>\n",
        "      <td>True</td>\n",
-       "      <td>[4]</td>\n",
-       "      <td>[1.29070175, 1.2940295000000002, 1.30458350000...</td>\n",
-       "      <td>1.290702</td>\n",
-       "      <td>1.294030</td>\n",
-       "      <td>1.304584</td>\n",
-       "      <td>0.017678</td>\n",
-       "      <td>1.325406</td>\n",
-       "      <td>3.125018e-04</td>\n",
-       "      <td>0.002818</td>\n",
-       "      <td>0.991163</td>\n",
-       "      <td>0.998832</td>\n",
-       "      <td>0.996608</td>\n",
-       "      <td>0.996824</td>\n",
-       "      <td>0.993177</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[0.968337, 0.984356, 0.9885772500000001]</td>\n",
+       "      <td>0.968337</td>\n",
+       "      <td>0.984356</td>\n",
+       "      <td>0.988577</td>\n",
+       "      <td>0.012040</td>\n",
+       "      <td>1.018938</td>\n",
+       "      <td>0.000145</td>\n",
+       "      <td>-0.078788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_MEMSET</td>\n",
+       "      <td>12623520.75</td>\n",
+       "      <td>1.995862e+09</td>\n",
+       "      <td>2.036565e+09</td>\n",
+       "      <td>2.020861e+09</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>2.006252e+09</td>\n",
        "      <td>True</td>\n",
        "      <td>[]</td>\n",
-       "      <td>[0.99538975, 0.9968239999999999, 0.99820775]</td>\n",
-       "      <td>0.995390</td>\n",
-       "      <td>0.996824</td>\n",
-       "      <td>0.998208</td>\n",
-       "      <td>0.001797</td>\n",
-       "      <td>1.002435</td>\n",
-       "      <td>0.000003</td>\n",
-       "      <td>-0.309091</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>{'name': 'Algorithm_MEMCPY.library', 'type': 'function'}</th>\n",
-       "      <td>Algorithm_MEMCPY.library</td>\n",
-       "      <td>0.007559</td>\n",
-       "      <td>0.723013</td>\n",
-       "      <td>0.756431</td>\n",
-       "      <td>0.738028</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.722036</td>\n",
+       "      <td>[2014797196.0, 2018832431.0, 2027420716.75]</td>\n",
+       "      <td>2.014797e+09</td>\n",
+       "      <td>2.018832e+09</td>\n",
+       "      <td>2.027421e+09</td>\n",
+       "      <td>8.974735e+06</td>\n",
+       "      <td>2.046356e+09</td>\n",
+       "      <td>8.054587e+13</td>\n",
+       "      <td>0.005329</td>\n",
+       "      <td>0.980985</td>\n",
+       "      <td>0.997550</td>\n",
+       "      <td>0.990934</td>\n",
+       "      <td>0.991464</td>\n",
+       "      <td>0.980433</td>\n",
+       "      <td>True</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>[0.98897875, 0.991464, 0.9943077499999999]</td>\n",
+       "      <td>0.988979</td>\n",
+       "      <td>0.991464</td>\n",
+       "      <td>0.994308</td>\n",
+       "      <td>0.005125</td>\n",
+       "      <td>1.002301</td>\n",
+       "      <td>0.000026</td>\n",
+       "      <td>-0.090909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "      <td>2370880.75</td>\n",
+       "      <td>4.444646e+08</td>\n",
+       "      <td>4.511030e+08</td>\n",
+       "      <td>4.492754e+08</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>4.467119e+08</td>\n",
        "      <td>True</td>\n",
-       "      <td>[0, 8]</td>\n",
-       "      <td>[0.734351, 0.7368325, 0.74190975]</td>\n",
-       "      <td>0.734351</td>\n",
-       "      <td>0.736833</td>\n",
-       "      <td>0.741910</td>\n",
-       "      <td>0.008339</td>\n",
-       "      <td>0.753248</td>\n",
-       "      <td>6.953802e-05</td>\n",
-       "      <td>0.030503</td>\n",
-       "      <td>0.880922</td>\n",
-       "      <td>0.978771</td>\n",
-       "      <td>0.944047</td>\n",
-       "      <td>0.949658</td>\n",
-       "      <td>0.905950</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[448020898.75, 449819291.0, 450391779.5]</td>\n",
+       "      <td>4.480209e+08</td>\n",
+       "      <td>4.498193e+08</td>\n",
+       "      <td>4.503918e+08</td>\n",
+       "      <td>1.482635e+06</td>\n",
+       "      <td>4.539481e+08</td>\n",
+       "      <td>2.198205e+12</td>\n",
+       "      <td>0.103014</td>\n",
+       "      <td>0.730101</td>\n",
+       "      <td>0.994624</td>\n",
+       "      <td>0.922818</td>\n",
+       "      <td>0.925987</td>\n",
+       "      <td>0.770469</td>\n",
        "      <td>True</td>\n",
        "      <td>[]</td>\n",
-       "      <td>[0.9266765, 0.9496575, 0.9571795]</td>\n",
-       "      <td>0.926677</td>\n",
-       "      <td>0.949658</td>\n",
-       "      <td>0.957179</td>\n",
-       "      <td>0.022419</td>\n",
-       "      <td>1.002934</td>\n",
-       "      <td>0.000503</td>\n",
-       "      <td>-0.042424</td>\n",
+       "      <td>[0.8846215, 0.9259875, 0.987635]</td>\n",
+       "      <td>0.884621</td>\n",
+       "      <td>0.925987</td>\n",
+       "      <td>0.987635</td>\n",
+       "      <td>0.068656</td>\n",
+       "      <td>1.142155</td>\n",
+       "      <td>0.004714</td>\n",
+       "      <td>0.575758</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                                              \\\n",
-       "                                                                        name   \n",
-       "node                                                                           \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                            Base_Seq   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                          Algorithm   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            Algorithm_MEMCPY   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  Algorithm_MEMCPY.default   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...  Algorithm_MEMCPY.library   \n",
+       "                                                                          \\\n",
+       "                                                                    name   \n",
+       "node                                                                       \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        RAJAPerf   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                      Algorithm   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        Algorithm_MEMCPY   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        Algorithm_MEMSET   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...  Algorithm_REDUCE_SUM   \n",
        "\n",
        "                                                                             Clang  \\\n",
        "                                                   time (exc)_iqr(0.25, 0.5, 0.75)   \n",
        "node                                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                  0.000180   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                 0.000019   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                          0.000020   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                        0.013882   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                        0.007559   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                 111159.25   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                 12033.25   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                       15879973.25   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                       12623520.75   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                      2370880.75   \n",
        "\n",
        "                                                                                           \\\n",
        "                                                   time (exc)_lowerfence(0.25, 0.5, 0.75)   \n",
        "node                                                                                        \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                         0.006786   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                        0.000556   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                 0.000194   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                               1.269879   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                               0.723013   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                     4.556396e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                    5.150704e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                             1.965178e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                             1.995862e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                           4.444646e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_max   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.007418   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000637   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000252   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.343627   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.756431   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.922397e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.576280e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     2.008037e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.036565e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.511030e+08   \n",
        "\n",
        "                                                                    \\\n",
        "                                                   time (exc)_mean   \n",
        "node                                                                 \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                  0.007147   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                 0.000597   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          0.000233   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...        1.298458   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...        0.738028   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}              4.788171e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}             5.383793e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}      1.997560e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}      2.020861e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...    4.492754e+08   \n",
        "\n",
        "                                                                      \\\n",
        "                                                   time (exc)_median   \n",
        "node                                                                   \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                    0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                   0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}            0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...          1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...          0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}               5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}        2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...      4.498193e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_min   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.006875   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000564   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000215   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       1.275210   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.722036   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.696939e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            5.176070e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.982438e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     2.006252e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   4.467119e+08   \n",
        "\n",
        "                                                                         \\\n",
        "                                                   time (exc)_normality   \n",
        "node                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                           True   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                           True   \n",
        "{'name': 'Algorithm', 'type': 'function'}                          True   \n",
        "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                   True   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                 True   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                 True   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                   True   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                 True   \n",
        "\n",
        "                                                                                         \\\n",
        "                                                   time (exc)_outliers(0.25, 0.5, 0.75)   \n",
        "node                                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                             []   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                           [0]   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                             []   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                            []   \n",
        "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                     []   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                                  [4]   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                               [0, 8]   \n",
-       "\n",
-       "                                                                                                       \\\n",
-       "                                                                               time (exc)_percentiles   \n",
-       "node                                                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}            [0.0070557499999999995, 0.007194000000000001, ...   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                            [0.00058425, 0.000598, 0.000603]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                    [0.00022325, 0.0002315, 0.000243]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...  [1.29070175, 1.2940295000000002, 1.30458350000...   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                  [0.734351, 0.7368325, 0.74190975]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                                     []   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                                   []   \n",
+       "\n",
+       "                                                                                                 \\\n",
+       "                                                                         time (exc)_percentiles   \n",
+       "node                                                                                              \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     [4723135.0, 4792513.5, 4834294.25]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       [533120.25, 538624.5, 545153.5]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}    [1988997772.0, 2001817999.5, 2004877745.25]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}    [2014797196.0, 2018832431.0, 2027420716.75]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...     [448020898.75, 449819291.0, 450391779.5]   \n",
        "\n",
        "                                                                                   \\\n",
        "                                                   time (exc)_q1(0.25, 0.5, 0.75)   \n",
        "node                                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                 0.007056   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                0.000584   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                         0.000223   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                       1.290702   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                       0.734351   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                             4.723135e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                            5.331202e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                     1.988998e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                     2.014797e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                   4.480209e+08   \n",
        "\n",
        "                                                                                   \\\n",
        "                                                   time (exc)_q2(0.25, 0.5, 0.75)   \n",
        "node                                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                 0.007194   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                0.000598   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                         0.000231   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                       1.294030   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                       0.736833   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                             4.792514e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                            5.386245e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                     2.001818e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                     2.018832e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                   4.498193e+08   \n",
        "\n",
        "                                                                                   \\\n",
        "                                                   time (exc)_q3(0.25, 0.5, 0.75)   \n",
        "node                                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                 0.007235   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                0.000603   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                         0.000243   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                       1.304584   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                       0.741910   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                             4.834294e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                            5.451535e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                     2.004878e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                     2.027421e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                   4.503918e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_std   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                 0.000160   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                0.000019   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}         0.000012   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...       0.017678   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...       0.008339   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             6.919881e+04   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            1.263294e+04   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     9.110282e+06   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     8.974735e+06   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   1.482635e+06   \n",
        "\n",
        "                                                                                           \\\n",
        "                                                   time (exc)_upperfence(0.25, 0.5, 0.75)   \n",
        "node                                                                                        \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                         0.007505   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                        0.000631   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                 0.000273   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                               1.325406   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                               0.753248   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                     5.001033e+06   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                    5.632034e+05   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                             2.028698e+09   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                             2.046356e+09   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                           4.539481e+08   \n",
        "\n",
        "                                                                   \\\n",
        "                                                   time (exc)_var   \n",
        "node                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}             2.549456e-08   \n",
-       "{'name': 'Algorithm', 'type': 'function'}            3.652100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     1.560100e-10   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...   3.125018e-04   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...   6.953802e-05   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}             4.788476e+09   \n",
+       "{'name': 'Algorithm', 'type': 'function'}            1.595913e+08   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}     8.299723e+13   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}     8.054587e+13   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...   2.198205e+12   \n",
        "\n",
        "                                                                                   GCC  \\\n",
        "                                                   Machine clears_iqr(0.25, 0.5, 0.75)   \n",
        "node                                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                      0.007979   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                     0.014738   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                              0.764881   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                            0.002818   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                            0.030503   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                      0.001713   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                     0.003985   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                              0.020240   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                              0.005329   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                            0.103014   \n",
        "\n",
        "                                                                                               \\\n",
        "                                                   Machine clears_lowerfence(0.25, 0.5, 0.75)   \n",
        "node                                                                                            \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                             0.012808   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                           -0.013394   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                    -1.147322   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                                   0.991163   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                                   0.880922   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                            -0.000670   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                           -0.003532   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                     0.937977   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                                     0.980985   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                                   0.730101   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_max   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.034919   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.078947   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             1.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.998832   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.978771   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.004523   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.014205   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.992988   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.997550   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.994624   \n",
        "\n",
        "                                                                        \\\n",
        "                                                   Machine clears_mean   \n",
        "node                                                                     \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                      0.027250   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                     0.019933   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.311366   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...            0.996608   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...            0.944047   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                      0.002853   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                     0.005249   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}              0.978926   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}              0.990934   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...            0.922818   \n",
        "\n",
        "                                                                          \\\n",
        "                                                   Machine clears_median   \n",
        "node                                                                       \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                        0.026362   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                       0.012092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.027067   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...              0.996824   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...              0.949658   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                        0.003061   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                       0.004810   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                0.984356   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                0.991464   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...              0.925987   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_min   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.014810   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001071   \n",
        "{'name': 'Algorithm', 'type': 'function'}                    0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.993177   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.905950   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.956393   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.980433   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.770469   \n",
        "\n",
        "                                                                             \\\n",
        "                                                   Machine clears_normality   \n",
        "node                                                                          \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                               True   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                             False   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                      False   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                     True   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                     True   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                               True   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                              True   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                       True   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                       True   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                     True   \n",
        "\n",
        "                                                                                             \\\n",
        "                                                   Machine clears_outliers(0.25, 0.5, 0.75)   \n",
        "node                                                                                          \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                                 []   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                               [2]   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                                 []   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                               [4]   \n",
        "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                         []   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                                       []   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                                       []   \n",
-       "\n",
-       "                                                                                                   \\\n",
-       "                                                                       Machine clears_percentiles   \n",
-       "node                                                                                                \n",
-       "{'name': 'Base_Seq', 'type': 'function'}            [0.02477725, 0.026362499999999997, 0.0327565]   \n",
-       "{'name': 'Algorithm', 'type': 'function'}           [0.00871275, 0.012091999999999999, 0.0234505]   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                        [0.0, 0.027067, 0.764881]   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...   [0.99538975, 0.9968239999999999, 0.99820775]   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...              [0.9266765, 0.9496575, 0.9571795]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                                        [1]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                                       []   \n",
+       "\n",
+       "                                                                                                    \\\n",
+       "                                                                        Machine clears_percentiles   \n",
+       "node                                                                                                 \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}            [0.0018989999999999999, 0.0030615, 0.00361175]   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                          [0.00244575, 0.00481, 0.006431]   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}          [0.968337, 0.984356, 0.9885772500000001]   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}        [0.98897875, 0.991464, 0.9943077499999999]   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                [0.8846215, 0.9259875, 0.987635]   \n",
        "\n",
        "                                                                                       \\\n",
        "                                                   Machine clears_q1(0.25, 0.5, 0.75)   \n",
        "node                                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                     0.024777   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                    0.008713   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                             0.000000   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                           0.995390   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                           0.926677   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                     0.001899   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                    0.002446   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                             0.968337   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                             0.988979   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                           0.884621   \n",
        "\n",
        "                                                                                       \\\n",
        "                                                   Machine clears_q2(0.25, 0.5, 0.75)   \n",
        "node                                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                     0.026362   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                    0.012092   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                             0.027067   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                           0.996824   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                           0.949658   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                     0.003061   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                    0.004810   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                             0.984356   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                             0.991464   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                           0.925987   \n",
        "\n",
        "                                                                                       \\\n",
        "                                                   Machine clears_q3(0.25, 0.5, 0.75)   \n",
        "node                                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                     0.032757   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                    0.023450   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                             0.764881   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                           0.998208   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                           0.957179   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                     0.003612   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                    0.006431   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                             0.988577   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                             0.994308   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                           0.987635   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_std   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.005898   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.021524   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.451230   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.001797   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.022419   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.001096   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.004187   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.012040   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.005125   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.068656   \n",
        "\n",
        "                                                                                               \\\n",
        "                                                   Machine clears_upperfence(0.25, 0.5, 0.75)   \n",
        "node                                                                                            \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                             0.044725   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                            0.045557   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                     1.912203   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                                   1.002435   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                                   1.002934   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                                             0.006181   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                            0.012409   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                     1.018938   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                                     1.002301   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                                   1.142155   \n",
        "\n",
        "                                                                       \\\n",
        "                                                   Machine clears_var   \n",
        "node                                                                    \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                     0.000035   \n",
-       "{'name': 'Algorithm', 'type': 'function'}                    0.000463   \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.203608   \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...           0.000003   \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...           0.000503   \n",
+       "{'name': 'RAJAPerf', 'type': 'function'}                     0.000001   \n",
+       "{'name': 'Algorithm', 'type': 'function'}                    0.000018   \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}             0.000145   \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}             0.000026   \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...           0.004714   \n",
        "\n",
        "                                                                        Union statistics  \n",
        "                                                   time (exc)_vs_Machine clears spearman  \n",
        "node                                                                                      \n",
-       "{'name': 'Base_Seq', 'type': 'function'}                                       -0.139394  \n",
-       "{'name': 'Algorithm', 'type': 'function'}                                       0.060791  \n",
-       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                                0.320908  \n",
-       "{'name': 'Algorithm_MEMCPY.default', 'type': 'f...                             -0.309091  \n",
-       "{'name': 'Algorithm_MEMCPY.library', 'type': 'f...                             -0.042424  "
+       "{'name': 'RAJAPerf', 'type': 'function'}                                        0.115152  \n",
+       "{'name': 'Algorithm', 'type': 'function'}                                      -0.182372  \n",
+       "{'name': 'Algorithm_MEMCPY', 'type': 'function'}                               -0.078788  \n",
+       "{'name': 'Algorithm_MEMSET', 'type': 'function'}                               -0.090909  \n",
+       "{'name': 'Algorithm_REDUCE_SUM', 'type': 'funct...                              0.575758  "
       ]
      },
      "execution_count": 44,
@@ -8235,13 +8235,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35a1db0f",
+   "id": "c6ad438c",
    "metadata": {
     "papermill": {
-     "duration": 0.008588,
-     "end_time": "2023-12-09T20:58:46.758872",
+     "duration": 0.009397,
+     "end_time": "2023-12-22T05:36:11.275908",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.750284",
+     "start_time": "2023-12-22T05:36:11.266511",
      "status": "completed"
     },
     "tags": []
@@ -8252,13 +8252,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d16fe53",
+   "id": "2533496c",
    "metadata": {
     "papermill": {
-     "duration": 0.008513,
-     "end_time": "2023-12-09T20:58:46.776332",
+     "duration": 0.009225,
+     "end_time": "2023-12-22T05:36:11.294500",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.767819",
+     "start_time": "2023-12-22T05:36:11.285275",
      "status": "completed"
     },
     "tags": []
@@ -8273,13 +8273,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ff10454",
+   "id": "0ee1dc66",
    "metadata": {
     "papermill": {
-     "duration": 0.008436,
-     "end_time": "2023-12-09T20:58:46.793517",
+     "duration": 0.009296,
+     "end_time": "2023-12-22T05:36:11.313095",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.785081",
+     "start_time": "2023-12-22T05:36:11.303799",
      "status": "completed"
     },
     "tags": []
@@ -8291,19 +8291,19 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "754241bf",
+   "id": "20fef965",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:46.811492Z",
-     "iopub.status.busy": "2023-12-09T20:58:46.811386Z",
-     "iopub.status.idle": "2023-12-09T20:58:46.998563Z",
-     "shell.execute_reply": "2023-12-09T20:58:46.998275Z"
+     "iopub.execute_input": "2023-12-22T05:36:11.332604Z",
+     "iopub.status.busy": "2023-12-22T05:36:11.332473Z",
+     "iopub.status.idle": "2023-12-22T05:36:11.405181Z",
+     "shell.execute_reply": "2023-12-22T05:36:11.404674Z"
     },
     "papermill": {
-     "duration": 0.196843,
-     "end_time": "2023-12-09T20:58:46.999142",
+     "duration": 0.083565,
+     "end_time": "2023-12-22T05:36:11.406048",
      "exception": false,
-     "start_time": "2023-12-09T20:58:46.802299",
+     "start_time": "2023-12-22T05:36:11.322483",
      "status": "completed"
     },
     "tags": []
@@ -8322,19 +8322,19 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "f8861779",
+   "id": "b538469a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:47.016883Z",
-     "iopub.status.busy": "2023-12-09T20:58:47.016772Z",
-     "iopub.status.idle": "2023-12-09T20:58:47.153718Z",
-     "shell.execute_reply": "2023-12-09T20:58:47.153518Z"
+     "iopub.execute_input": "2023-12-22T05:36:11.425835Z",
+     "iopub.status.busy": "2023-12-22T05:36:11.425723Z",
+     "iopub.status.idle": "2023-12-22T05:36:11.562756Z",
+     "shell.execute_reply": "2023-12-22T05:36:11.562212Z"
     },
     "papermill": {
-     "duration": 0.146317,
-     "end_time": "2023-12-09T20:58:47.154217",
+     "duration": 0.147698,
+     "end_time": "2023-12-22T05:36:11.563624",
      "exception": false,
-     "start_time": "2023-12-09T20:58:47.007900",
+     "start_time": "2023-12-22T05:36:11.415926",
      "status": "completed"
     },
     "tags": []
@@ -8352,7 +8352,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 2160x2160 with 2 Axes>"
       ]
@@ -8371,13 +8371,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d2d0e735",
+   "id": "77ea8fb0",
    "metadata": {
     "papermill": {
-     "duration": 0.009045,
-     "end_time": "2023-12-09T20:58:47.173041",
+     "duration": 0.009483,
+     "end_time": "2023-12-22T05:36:11.583093",
      "exception": false,
-     "start_time": "2023-12-09T20:58:47.163996",
+     "start_time": "2023-12-22T05:36:11.573610",
      "status": "completed"
     },
     "tags": []
@@ -8393,19 +8393,19 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "d62e59d1",
+   "id": "eaceeef2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:47.191932Z",
-     "iopub.status.busy": "2023-12-09T20:58:47.191823Z",
-     "iopub.status.idle": "2023-12-09T20:58:47.835838Z",
-     "shell.execute_reply": "2023-12-09T20:58:47.835529Z"
+     "iopub.execute_input": "2023-12-22T05:36:11.602413Z",
+     "iopub.status.busy": "2023-12-22T05:36:11.602273Z",
+     "iopub.status.idle": "2023-12-22T05:36:12.012880Z",
+     "shell.execute_reply": "2023-12-22T05:36:12.012547Z"
     },
     "papermill": {
-     "duration": 0.655234,
-     "end_time": "2023-12-09T20:58:47.837638",
+     "duration": 0.421561,
+     "end_time": "2023-12-22T05:36:12.014001",
      "exception": false,
-     "start_time": "2023-12-09T20:58:47.182404",
+     "start_time": "2023-12-22T05:36:11.592440",
      "status": "completed"
     },
     "tags": []
@@ -8423,7 +8423,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 2160x2160 with 2 Axes>"
       ]
@@ -8442,13 +8442,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "114c90cc",
+   "id": "18fcdd1e",
    "metadata": {
     "papermill": {
-     "duration": 0.013373,
-     "end_time": "2023-12-09T20:58:47.864471",
+     "duration": 0.012299,
+     "end_time": "2023-12-22T05:36:12.039372",
      "exception": false,
-     "start_time": "2023-12-09T20:58:47.851098",
+     "start_time": "2023-12-22T05:36:12.027073",
      "status": "completed"
     },
     "tags": []
@@ -8460,19 +8460,19 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "88f20c78",
+   "id": "abe77c7c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:47.891725Z",
-     "iopub.status.busy": "2023-12-09T20:58:47.891601Z",
-     "iopub.status.idle": "2023-12-09T20:58:47.893253Z",
-     "shell.execute_reply": "2023-12-09T20:58:47.892980Z"
+     "iopub.execute_input": "2023-12-22T05:36:12.064236Z",
+     "iopub.status.busy": "2023-12-22T05:36:12.064093Z",
+     "iopub.status.idle": "2023-12-22T05:36:12.065764Z",
+     "shell.execute_reply": "2023-12-22T05:36:12.065469Z"
     },
     "papermill": {
-     "duration": 0.016178,
-     "end_time": "2023-12-09T20:58:47.893766",
+     "duration": 0.014419,
+     "end_time": "2023-12-22T05:36:12.066299",
      "exception": false,
-     "start_time": "2023-12-09T20:58:47.877588",
+     "start_time": "2023-12-22T05:36:12.051880",
      "status": "completed"
     },
     "tags": []
@@ -8485,13 +8485,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac224f16",
+   "id": "aec8f21d",
    "metadata": {
     "papermill": {
-     "duration": 0.013555,
-     "end_time": "2023-12-09T20:58:47.920749",
+     "duration": 0.011511,
+     "end_time": "2023-12-22T05:36:12.089268",
      "exception": false,
-     "start_time": "2023-12-09T20:58:47.907194",
+     "start_time": "2023-12-22T05:36:12.077757",
      "status": "completed"
     },
     "tags": []
@@ -8507,19 +8507,19 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "17dbc49d",
+   "id": "f9e044dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:47.948443Z",
-     "iopub.status.busy": "2023-12-09T20:58:47.948299Z",
-     "iopub.status.idle": "2023-12-09T20:58:47.951754Z",
-     "shell.execute_reply": "2023-12-09T20:58:47.951506Z"
+     "iopub.execute_input": "2023-12-22T05:36:12.113958Z",
+     "iopub.status.busy": "2023-12-22T05:36:12.113787Z",
+     "iopub.status.idle": "2023-12-22T05:36:12.117359Z",
+     "shell.execute_reply": "2023-12-22T05:36:12.117119Z"
     },
     "papermill": {
-     "duration": 0.018183,
-     "end_time": "2023-12-09T20:58:47.952269",
+     "duration": 0.016884,
+     "end_time": "2023-12-22T05:36:12.117972",
      "exception": false,
-     "start_time": "2023-12-09T20:58:47.934086",
+     "start_time": "2023-12-22T05:36:12.101088",
      "status": "completed"
     },
     "tags": []
@@ -8532,13 +8532,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0ad2def",
+   "id": "4aaae61e",
    "metadata": {
     "papermill": {
-     "duration": 0.013149,
-     "end_time": "2023-12-09T20:58:47.979042",
+     "duration": 0.011661,
+     "end_time": "2023-12-22T05:36:12.141819",
      "exception": false,
-     "start_time": "2023-12-09T20:58:47.965893",
+     "start_time": "2023-12-22T05:36:12.130158",
      "status": "completed"
     },
     "tags": []
@@ -8550,19 +8550,19 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "b9b9f70d",
+   "id": "71e78f16",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:48.006138Z",
-     "iopub.status.busy": "2023-12-09T20:58:48.006025Z",
-     "iopub.status.idle": "2023-12-09T20:58:48.112927Z",
-     "shell.execute_reply": "2023-12-09T20:58:48.112565Z"
+     "iopub.execute_input": "2023-12-22T05:36:12.165722Z",
+     "iopub.status.busy": "2023-12-22T05:36:12.165561Z",
+     "iopub.status.idle": "2023-12-22T05:36:12.250234Z",
+     "shell.execute_reply": "2023-12-22T05:36:12.249898Z"
     },
     "papermill": {
-     "duration": 0.121084,
-     "end_time": "2023-12-09T20:58:48.113496",
+     "duration": 0.097573,
+     "end_time": "2023-12-22T05:36:12.250873",
      "exception": false,
-     "start_time": "2023-12-09T20:58:47.992412",
+     "start_time": "2023-12-22T05:36:12.153300",
      "status": "completed"
     },
     "tags": []
@@ -8571,7 +8571,7 @@
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0xffff2af2cd60>"
+       "<seaborn.axisgrid.FacetGrid at 0xffff435948b0>"
       ]
      },
      "execution_count": 50,
@@ -8589,7 +8589,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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X7trkQwT4NLr7tiQn3lJ9U5JXdvd7q+rnq+pJq4d9fZL3VdX7kzwgq9/i3X17Nv/K9fqquiFJJfmdXf4WzqqdbI8kWZ2Gd3GSN+3m3EvZ4fa4IptnDdyQzWOg13f3q3Zz/rNph9vi/CRvqaobs/k25WeuXm9P81ZkgCH2gAGGCDDAEAEGGCLAAEMEGGCIAAMMEWCAIQIMMESAAYYIMMAQAQYYIsAAQwQYYIgAAwwRYIAhAgwwRIABhggwwBABBhgiwABDBBhgyH8BkvTgpkQiIvYAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -8607,13 +8607,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9abf4b7a",
+   "id": "36515d5e",
    "metadata": {
     "papermill": {
-     "duration": 0.013292,
-     "end_time": "2023-12-09T20:58:48.140387",
+     "duration": 0.011556,
+     "end_time": "2023-12-22T05:36:12.274382",
      "exception": false,
-     "start_time": "2023-12-09T20:58:48.127095",
+     "start_time": "2023-12-22T05:36:12.262826",
      "status": "completed"
     },
     "tags": []
@@ -8629,19 +8629,19 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "65c63f79",
+   "id": "87b0e2cf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:48.168579Z",
-     "iopub.status.busy": "2023-12-09T20:58:48.168462Z",
-     "iopub.status.idle": "2023-12-09T20:58:48.241852Z",
-     "shell.execute_reply": "2023-12-09T20:58:48.241581Z"
+     "iopub.execute_input": "2023-12-22T05:36:12.298756Z",
+     "iopub.status.busy": "2023-12-22T05:36:12.298623Z",
+     "iopub.status.idle": "2023-12-22T05:36:12.367095Z",
+     "shell.execute_reply": "2023-12-22T05:36:12.366730Z"
     },
     "papermill": {
-     "duration": 0.088219,
-     "end_time": "2023-12-09T20:58:48.242363",
+     "duration": 0.081366,
+     "end_time": "2023-12-22T05:36:12.367655",
      "exception": false,
-     "start_time": "2023-12-09T20:58:48.154144",
+     "start_time": "2023-12-22T05:36:12.286289",
      "status": "completed"
     },
     "tags": []
@@ -8650,7 +8650,7 @@
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0xffff2ae82bb0>"
+       "<seaborn.axisgrid.FacetGrid at 0xffff42569d00>"
       ]
      },
      "execution_count": 51,
@@ -8668,7 +8668,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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X7trkQwT4NLr7tiQn3lJ9U5JXdvd7q+rnq+pJq4d9fZL3VdX7kzwgq9/i3X17Nv/K9fqquiFJJfmdXf4WzqqdbI8kWZ2Gd3GSN+3m3EvZ4fa4IptnDdyQzWOg13f3q3Zz/rNph9vi/CRvqaobs/k25WeuXm9P81ZkgCH2gAGGCDDAEAEGGCLAAEMEGGCIAAMMEWCAIQIMMESAAYYIMMAQAQYYIsAAQwQYYIgAAwwRYIAhAgwwRIABhggwwBABBhgiwABDBBhgyH8BkvTgpkQiIvYAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -8686,13 +8686,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0ad442a",
+   "id": "214ee1fd",
    "metadata": {
     "papermill": {
-     "duration": 0.014253,
-     "end_time": "2023-12-09T20:58:48.270208",
+     "duration": 0.012696,
+     "end_time": "2023-12-22T05:36:12.392828",
      "exception": false,
-     "start_time": "2023-12-09T20:58:48.255955",
+     "start_time": "2023-12-22T05:36:12.380132",
      "status": "completed"
     },
     "tags": []
@@ -8704,19 +8704,19 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "8356f510",
+   "id": "db4124ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:48.298323Z",
-     "iopub.status.busy": "2023-12-09T20:58:48.298211Z",
-     "iopub.status.idle": "2023-12-09T20:58:48.299833Z",
-     "shell.execute_reply": "2023-12-09T20:58:48.299574Z"
+     "iopub.execute_input": "2023-12-22T05:36:12.418324Z",
+     "iopub.status.busy": "2023-12-22T05:36:12.418173Z",
+     "iopub.status.idle": "2023-12-22T05:36:12.420035Z",
+     "shell.execute_reply": "2023-12-22T05:36:12.419752Z"
     },
     "papermill": {
-     "duration": 0.01634,
-     "end_time": "2023-12-09T20:58:48.300345",
+     "duration": 0.015637,
+     "end_time": "2023-12-22T05:36:12.420708",
      "exception": false,
-     "start_time": "2023-12-09T20:58:48.284005",
+     "start_time": "2023-12-22T05:36:12.405071",
      "status": "completed"
     },
     "tags": []
@@ -8729,13 +8729,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b8e2ad1",
+   "id": "0cdfbb55",
    "metadata": {
     "papermill": {
-     "duration": 0.013596,
-     "end_time": "2023-12-09T20:58:48.327833",
+     "duration": 0.011809,
+     "end_time": "2023-12-22T05:36:12.444547",
      "exception": false,
-     "start_time": "2023-12-09T20:58:48.314237",
+     "start_time": "2023-12-22T05:36:12.432738",
      "status": "completed"
     },
     "tags": []
@@ -8749,19 +8749,19 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "b66b041a",
+   "id": "fe1cac95",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:48.355477Z",
-     "iopub.status.busy": "2023-12-09T20:58:48.355348Z",
-     "iopub.status.idle": "2023-12-09T20:58:48.358450Z",
-     "shell.execute_reply": "2023-12-09T20:58:48.358220Z"
+     "iopub.execute_input": "2023-12-22T05:36:12.510090Z",
+     "iopub.status.busy": "2023-12-22T05:36:12.509717Z",
+     "iopub.status.idle": "2023-12-22T05:36:12.514984Z",
+     "shell.execute_reply": "2023-12-22T05:36:12.514072Z"
     },
     "papermill": {
-     "duration": 0.017445,
-     "end_time": "2023-12-09T20:58:48.358965",
+     "duration": 0.059505,
+     "end_time": "2023-12-22T05:36:12.516091",
      "exception": false,
-     "start_time": "2023-12-09T20:58:48.341520",
+     "start_time": "2023-12-22T05:36:12.456586",
      "status": "completed"
     },
     "tags": []
@@ -8774,13 +8774,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35c7f523",
+   "id": "faea6496",
    "metadata": {
     "papermill": {
-     "duration": 0.01337,
-     "end_time": "2023-12-09T20:58:48.385950",
+     "duration": 0.012049,
+     "end_time": "2023-12-22T05:36:12.540784",
      "exception": false,
-     "start_time": "2023-12-09T20:58:48.372580",
+     "start_time": "2023-12-22T05:36:12.528735",
      "status": "completed"
     },
     "tags": []
@@ -8792,19 +8792,19 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "1879c01b",
+   "id": "5260c27d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:48.413561Z",
-     "iopub.status.busy": "2023-12-09T20:58:48.413445Z",
-     "iopub.status.idle": "2023-12-09T20:58:48.519934Z",
-     "shell.execute_reply": "2023-12-09T20:58:48.519634Z"
+     "iopub.execute_input": "2023-12-22T05:36:12.566970Z",
+     "iopub.status.busy": "2023-12-22T05:36:12.566789Z",
+     "iopub.status.idle": "2023-12-22T05:36:12.679384Z",
+     "shell.execute_reply": "2023-12-22T05:36:12.679068Z"
     },
     "papermill": {
-     "duration": 0.121208,
-     "end_time": "2023-12-09T20:58:48.520550",
+     "duration": 0.126324,
+     "end_time": "2023-12-22T05:36:12.679953",
      "exception": false,
-     "start_time": "2023-12-09T20:58:48.399342",
+     "start_time": "2023-12-22T05:36:12.553629",
      "status": "completed"
     },
     "tags": []
@@ -8822,7 +8822,7 @@
     },
     {
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       "text/plain": [
        "<Figure size 2160x2160 with 1 Axes>"
       ]
@@ -8840,13 +8840,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb12e879",
+   "id": "5b4d170d",
    "metadata": {
     "papermill": {
-     "duration": 0.01435,
-     "end_time": "2023-12-09T20:58:48.548765",
+     "duration": 0.012752,
+     "end_time": "2023-12-22T05:36:12.704982",
      "exception": false,
-     "start_time": "2023-12-09T20:58:48.534415",
+     "start_time": "2023-12-22T05:36:12.692230",
      "status": "completed"
     },
     "tags": []
@@ -8862,19 +8862,19 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "599ef144",
+   "id": "9c0a0eaa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:48.576870Z",
-     "iopub.status.busy": "2023-12-09T20:58:48.576736Z",
-     "iopub.status.idle": "2023-12-09T20:58:48.678313Z",
-     "shell.execute_reply": "2023-12-09T20:58:48.678003Z"
+     "iopub.execute_input": "2023-12-22T05:36:12.730610Z",
+     "iopub.status.busy": "2023-12-22T05:36:12.730467Z",
+     "iopub.status.idle": "2023-12-22T05:36:12.838067Z",
+     "shell.execute_reply": "2023-12-22T05:36:12.837744Z"
     },
     "papermill": {
-     "duration": 0.116253,
-     "end_time": "2023-12-09T20:58:48.678938",
+     "duration": 0.121313,
+     "end_time": "2023-12-22T05:36:12.838718",
      "exception": false,
-     "start_time": "2023-12-09T20:58:48.562685",
+     "start_time": "2023-12-22T05:36:12.717405",
      "status": "completed"
     },
     "tags": []
@@ -8892,7 +8892,7 @@
     },
     {
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       "text/plain": [
        "<Figure size 2160x2160 with 1 Axes>"
       ]
@@ -8910,13 +8910,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91248b3f",
+   "id": "6e3f3a9e",
    "metadata": {
     "papermill": {
-     "duration": 0.014454,
-     "end_time": "2023-12-09T20:58:48.707929",
+     "duration": 0.013701,
+     "end_time": "2023-12-22T05:36:12.865957",
      "exception": false,
-     "start_time": "2023-12-09T20:58:48.693475",
+     "start_time": "2023-12-22T05:36:12.852256",
      "status": "completed"
     },
     "tags": []
@@ -8928,19 +8928,19 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "a119934b",
+   "id": "7ac91412",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:48.736862Z",
-     "iopub.status.busy": "2023-12-09T20:58:48.736755Z",
-     "iopub.status.idle": "2023-12-09T20:58:48.738422Z",
-     "shell.execute_reply": "2023-12-09T20:58:48.738173Z"
+     "iopub.execute_input": "2023-12-22T05:36:12.892565Z",
+     "iopub.status.busy": "2023-12-22T05:36:12.892450Z",
+     "iopub.status.idle": "2023-12-22T05:36:12.894251Z",
+     "shell.execute_reply": "2023-12-22T05:36:12.893911Z"
     },
     "papermill": {
-     "duration": 0.016942,
-     "end_time": "2023-12-09T20:58:48.738957",
+     "duration": 0.01599,
+     "end_time": "2023-12-22T05:36:12.894758",
      "exception": false,
-     "start_time": "2023-12-09T20:58:48.722015",
+     "start_time": "2023-12-22T05:36:12.878768",
      "status": "completed"
     },
     "tags": []
@@ -8971,14 +8971,14 @@
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 6.077203,
-   "end_time": "2023-12-09T20:58:49.064043",
+   "duration": 5.581439,
+   "end_time": "2023-12-22T05:36:13.216880",
    "environment_variables": {},
    "exception": null,
    "input_path": "04_stats-functions.ipynb",
    "output_path": "04_stats-functions.ipynb",
    "parameters": {},
-   "start_time": "2023-12-09T20:58:42.986840",
+   "start_time": "2023-12-22T05:36:07.635441",
    "version": "2.5.0"
   },
   "vscode": {
diff --git a/docs/thicket_rajaperf_clustering.ipynb b/docs/thicket_rajaperf_clustering.ipynb
index 621ea718..a86f3439 100644
--- a/docs/thicket_rajaperf_clustering.ipynb
+++ b/docs/thicket_rajaperf_clustering.ipynb
@@ -5,10 +5,10 @@
    "id": "b0c9ae32",
    "metadata": {
     "papermill": {
-     "duration": 0.01921,
-     "end_time": "2023-12-09T20:58:37.200853",
+     "duration": 0.004409,
+     "end_time": "2023-12-22T05:36:00.123183",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.181643",
+     "start_time": "2023-12-22T05:36:00.118774",
      "status": "completed"
     },
     "tags": []
@@ -33,16 +33,16 @@
    "id": "e76011bf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.217117Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.216863Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.768342Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.767994Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.129360Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.129230Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.726283Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.725859Z"
     },
     "papermill": {
-     "duration": 0.559695,
-     "end_time": "2023-12-09T20:58:37.769060",
+     "duration": 0.600918,
+     "end_time": "2023-12-22T05:36:00.727034",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.209365",
+     "start_time": "2023-12-22T05:36:00.126116",
      "status": "completed"
     },
     "tags": []
@@ -410,10 +410,10 @@
    "id": "9c198209",
    "metadata": {
     "papermill": {
-     "duration": 0.002712,
-     "end_time": "2023-12-09T20:58:37.774867",
+     "duration": 0.002708,
+     "end_time": "2023-12-22T05:36:00.732944",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.772155",
+     "start_time": "2023-12-22T05:36:00.730236",
      "status": "completed"
     },
     "tags": []
@@ -428,16 +428,16 @@
    "id": "6586fbfb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.780599Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.780454Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.782163Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.781906Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.738821Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.738659Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.740470Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.740220Z"
     },
     "papermill": {
-     "duration": 0.005229,
-     "end_time": "2023-12-09T20:58:37.782714",
+     "duration": 0.005559,
+     "end_time": "2023-12-22T05:36:00.741160",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.777485",
+     "start_time": "2023-12-22T05:36:00.735601",
      "status": "completed"
     },
     "tags": []
@@ -453,10 +453,10 @@
    "id": "ff9038d9",
    "metadata": {
     "papermill": {
-     "duration": 0.002571,
-     "end_time": "2023-12-09T20:58:37.787967",
+     "duration": 0.00275,
+     "end_time": "2023-12-22T05:36:00.746726",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.785396",
+     "start_time": "2023-12-22T05:36:00.743976",
      "status": "completed"
     },
     "tags": []
@@ -471,16 +471,16 @@
    "id": "1c86f0eb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.793559Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.793462Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.799531Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.799271Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.752586Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.752475Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.759106Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.758811Z"
     },
     "papermill": {
-     "duration": 0.009414,
-     "end_time": "2023-12-09T20:58:37.800020",
+     "duration": 0.010203,
+     "end_time": "2023-12-22T05:36:00.759633",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.790606",
+     "start_time": "2023-12-22T05:36:00.749430",
      "status": "completed"
     },
     "tags": []
@@ -589,16 +589,16 @@
    "id": "cc7affa3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.805638Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.805550Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.808413Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.808197Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.767250Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.767148Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.770217Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.769989Z"
     },
     "papermill": {
-     "duration": 0.00625,
-     "end_time": "2023-12-09T20:58:37.808929",
+     "duration": 0.006597,
+     "end_time": "2023-12-22T05:36:00.770792",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.802679",
+     "start_time": "2023-12-22T05:36:00.764195",
      "status": "completed"
     },
     "tags": []
@@ -606,13 +606,13 @@
    "outputs": [],
    "source": [
     "def check_for_optimization_level(val):\n",
-    "    match = re.match(r\"(?P<opt_level>-O[0-3]).*\", val)\n",
+    "    match = re.search(r\"(?P<opt_level>-O[0123]).*\", val)\n",
     "    if match is None:\n",
     "        raise ValueError(\"Could not find opt level in {}\".format(val))\n",
     "    return match.group(\"opt_level\")\n",
     "\n",
     "def check_for_optimization_level_int(val):\n",
-    "    match = re.match(r\"-O(?P<opt_level>[0-3]).*\", val)\n",
+    "    match = re.search(r\"-O(?P<opt_level>[0-3]).*\", val)\n",
     "    if match is None:\n",
     "        raise ValueError(\"Could not find opt level in {}\".format(val))\n",
     "    return match.group(\"opt_level\")\n",
@@ -641,16 +641,16 @@
    "id": "5d602c2d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.814659Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.814566Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.817838Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.817603Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.776759Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.776652Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.780110Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.779845Z"
     },
     "papermill": {
-     "duration": 0.006858,
-     "end_time": "2023-12-09T20:58:37.818365",
+     "duration": 0.007117,
+     "end_time": "2023-12-22T05:36:00.780681",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.811507",
+     "start_time": "2023-12-22T05:36:00.773564",
      "status": "completed"
     },
     "tags": []
@@ -692,16 +692,16 @@
    "id": "d1880c5e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.824020Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.823940Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.827016Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.826714Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.791332Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.791227Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.794280Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.794018Z"
     },
     "papermill": {
-     "duration": 0.006519,
-     "end_time": "2023-12-09T20:58:37.827571",
+     "duration": 0.00688,
+     "end_time": "2023-12-22T05:36:00.794826",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.821052",
+     "start_time": "2023-12-22T05:36:00.787946",
      "status": "completed"
     },
     "tags": []
@@ -743,10 +743,10 @@
    "id": "a811cb0a",
    "metadata": {
     "papermill": {
-     "duration": 0.002492,
-     "end_time": "2023-12-09T20:58:37.832720",
+     "duration": 0.002943,
+     "end_time": "2023-12-22T05:36:00.800326",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.830228",
+     "start_time": "2023-12-22T05:36:00.797383",
      "status": "completed"
     },
     "tags": []
@@ -761,16 +761,16 @@
    "id": "0534d891",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.838118Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.838025Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.840013Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.839775Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.809582Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.809473Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.811736Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.811449Z"
     },
     "papermill": {
-     "duration": 0.005237,
-     "end_time": "2023-12-09T20:58:37.840481",
+     "duration": 0.009229,
+     "end_time": "2023-12-22T05:36:00.812374",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.835244",
+     "start_time": "2023-12-22T05:36:00.803145",
      "status": "completed"
     },
     "tags": []
@@ -791,16 +791,16 @@
    "id": "cddd1f09",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.846192Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.846104Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.849581Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.849338Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.818318Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.818228Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.821836Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.821536Z"
     },
     "papermill": {
-     "duration": 0.006916,
-     "end_time": "2023-12-09T20:58:37.850081",
+     "duration": 0.007201,
+     "end_time": "2023-12-22T05:36:00.822343",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.843165",
+     "start_time": "2023-12-22T05:36:00.815142",
      "status": "completed"
     },
     "tags": []
@@ -848,16 +848,16 @@
    "id": "06bcf566",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.855614Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.855523Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.861788Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.861594Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.828211Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.828118Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.834751Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.834505Z"
     },
     "papermill": {
-     "duration": 0.009638,
-     "end_time": "2023-12-09T20:58:37.862228",
+     "duration": 0.01042,
+     "end_time": "2023-12-22T05:36:00.835313",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.852590",
+     "start_time": "2023-12-22T05:36:00.824893",
      "status": "completed"
     },
     "tags": []
@@ -987,16 +987,16 @@
    "id": "3b990cfa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.867840Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.867749Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.871480Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.871239Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.841693Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.841572Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.845711Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.845440Z"
     },
     "papermill": {
-     "duration": 0.007191,
-     "end_time": "2023-12-09T20:58:37.871972",
+     "duration": 0.00789,
+     "end_time": "2023-12-22T05:36:00.846261",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.864781",
+     "start_time": "2023-12-22T05:36:00.838371",
      "status": "completed"
     },
     "tags": []
@@ -1068,16 +1068,16 @@
    "id": "2c4c9966",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.877641Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.877551Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.879417Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.879172Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.854081Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.853980Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.856025Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.855729Z"
     },
     "papermill": {
-     "duration": 0.005308,
-     "end_time": "2023-12-09T20:58:37.879925",
+     "duration": 0.007526,
+     "end_time": "2023-12-22T05:36:00.856586",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.874617",
+     "start_time": "2023-12-22T05:36:00.849060",
      "status": "completed"
     },
     "tags": []
@@ -1094,10 +1094,10 @@
    "id": "c922e597",
    "metadata": {
     "papermill": {
-     "duration": 0.002535,
-     "end_time": "2023-12-09T20:58:37.885090",
+     "duration": 0.002803,
+     "end_time": "2023-12-22T05:36:00.862092",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.882555",
+     "start_time": "2023-12-22T05:36:00.859289",
      "status": "completed"
     },
     "tags": []
@@ -1114,16 +1114,16 @@
    "id": "a25f953f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.890412Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.890312Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.892106Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.891919Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.867872Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.867777Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.869595Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.869331Z"
     },
     "papermill": {
-     "duration": 0.004993,
-     "end_time": "2023-12-09T20:58:37.892608",
+     "duration": 0.005278,
+     "end_time": "2023-12-22T05:36:00.870089",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.887615",
+     "start_time": "2023-12-22T05:36:00.864811",
      "status": "completed"
     },
     "tags": []
@@ -1132,10 +1132,10 @@
    "source": [
     "root = \"../data/quartz\"\n",
     "dirs_ordered = [\n",
-    "    \"GCC_831_BaseSeq_O0_08388608/GCC_831_BaseSeq_O0_08388608_01.cali\",\n",
-    "    \"GCC_831_BaseSeq_O1_08388608/GCC_831_BaseSeq_O1_08388608_01.cali\",\n",
-    "    \"GCC_831_BaseSeq_O2_08388608/GCC_831_BaseSeq_O2_08388608_01.cali\",\n",
-    "    \"GCC_831_BaseSeq_O3_08388608/GCC_831_BaseSeq_O3_08388608_01.cali\"\n",
+    "    \"GCC_10.3.1_BaseSeq_08388608/O0/GCC_1031_BaseSeq_O0_8388608_01.cali\",\n",
+    "    \"GCC_10.3.1_BaseSeq_08388608/O1/GCC_1031_BaseSeq_O1_8388608_01.cali\",\n",
+    "    \"GCC_10.3.1_BaseSeq_08388608/O2/GCC_1031_BaseSeq_O2_8388608_01.cali\",\n",
+    "    \"GCC_10.3.1_BaseSeq_08388608/O3/GCC_1031_BaseSeq_O3_8388608_01.cali\"\n",
     "]\n",
     "directories = [\"{}/{}\".format(root, d) for d in dirs_ordered]"
    ]
@@ -1145,10 +1145,10 @@
    "id": "f345aff7",
    "metadata": {
     "papermill": {
-     "duration": 0.002663,
-     "end_time": "2023-12-09T20:58:37.897976",
+     "duration": 0.002995,
+     "end_time": "2023-12-22T05:36:00.876999",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.895313",
+     "start_time": "2023-12-22T05:36:00.874004",
      "status": "completed"
     },
     "tags": []
@@ -1163,16 +1163,16 @@
    "id": "9c32973c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.903412Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.903301Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.904864Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.904668Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.883049Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.882937Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.884784Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.884483Z"
     },
     "papermill": {
-     "duration": 0.004794,
-     "end_time": "2023-12-09T20:58:37.905323",
+     "duration": 0.00569,
+     "end_time": "2023-12-22T05:36:00.885368",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.900529",
+     "start_time": "2023-12-22T05:36:00.879678",
      "status": "completed"
     },
     "tags": []
@@ -1192,10 +1192,10 @@
    "id": "e479a3a6",
    "metadata": {
     "papermill": {
-     "duration": 0.002563,
-     "end_time": "2023-12-09T20:58:37.910652",
+     "duration": 0.002733,
+     "end_time": "2023-12-22T05:36:00.890907",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.908089",
+     "start_time": "2023-12-22T05:36:00.888174",
      "status": "completed"
     },
     "tags": []
@@ -1214,16 +1214,16 @@
    "id": "10cfa497",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.916187Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.916095Z",
-     "iopub.status.idle": "2023-12-09T20:58:37.917843Z",
-     "shell.execute_reply": "2023-12-09T20:58:37.917642Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.896776Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.896640Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.898636Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.898392Z"
     },
     "papermill": {
-     "duration": 0.005062,
-     "end_time": "2023-12-09T20:58:37.918282",
+     "duration": 0.005644,
+     "end_time": "2023-12-22T05:36:00.899264",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.913220",
+     "start_time": "2023-12-22T05:36:00.893620",
      "status": "completed"
     },
     "tags": []
@@ -1248,10 +1248,10 @@
    "id": "9315af2a",
    "metadata": {
     "papermill": {
-     "duration": 0.002597,
-     "end_time": "2023-12-09T20:58:37.923637",
+     "duration": 0.002813,
+     "end_time": "2023-12-22T05:36:00.904966",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.921040",
+     "start_time": "2023-12-22T05:36:00.902153",
      "status": "completed"
     },
     "tags": []
@@ -1266,16 +1266,16 @@
    "id": "e439c1a1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:37.929124Z",
-     "iopub.status.busy": "2023-12-09T20:58:37.929034Z",
-     "iopub.status.idle": "2023-12-09T20:58:38.049387Z",
-     "shell.execute_reply": "2023-12-09T20:58:38.049054Z"
+     "iopub.execute_input": "2023-12-22T05:36:00.913703Z",
+     "iopub.status.busy": "2023-12-22T05:36:00.913586Z",
+     "iopub.status.idle": "2023-12-22T05:36:00.996973Z",
+     "shell.execute_reply": "2023-12-22T05:36:00.996625Z"
     },
     "papermill": {
-     "duration": 0.12385,
-     "end_time": "2023-12-09T20:58:38.050111",
+     "duration": 0.089937,
+     "end_time": "2023-12-22T05:36:00.997749",
      "exception": false,
-     "start_time": "2023-12-09T20:58:37.926261",
+     "start_time": "2023-12-22T05:36:00.907812",
      "status": "completed"
     },
     "tags": []
@@ -1290,10 +1290,10 @@
    "id": "c4ea7b97",
    "metadata": {
     "papermill": {
-     "duration": 0.002756,
-     "end_time": "2023-12-09T20:58:38.055832",
+     "duration": 0.003117,
+     "end_time": "2023-12-22T05:36:01.003862",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.053076",
+     "start_time": "2023-12-22T05:36:01.000745",
      "status": "completed"
     },
     "tags": []
@@ -1314,16 +1314,16 @@
    "id": "aea168dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:38.061504Z",
-     "iopub.status.busy": "2023-12-09T20:58:38.061407Z",
-     "iopub.status.idle": "2023-12-09T20:58:38.093198Z",
-     "shell.execute_reply": "2023-12-09T20:58:38.092904Z"
+     "iopub.execute_input": "2023-12-22T05:36:01.009915Z",
+     "iopub.status.busy": "2023-12-22T05:36:01.009786Z",
+     "iopub.status.idle": "2023-12-22T05:36:01.031479Z",
+     "shell.execute_reply": "2023-12-22T05:36:01.031174Z"
     },
     "papermill": {
-     "duration": 0.035333,
-     "end_time": "2023-12-09T20:58:38.093832",
+     "duration": 0.025409,
+     "end_time": "2023-12-22T05:36:01.032100",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.058499",
+     "start_time": "2023-12-22T05:36:01.006691",
      "status": "completed"
     },
     "tags": []
@@ -1339,16 +1339,16 @@
    "id": "11be2ef5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:38.099930Z",
-     "iopub.status.busy": "2023-12-09T20:58:38.099824Z",
-     "iopub.status.idle": "2023-12-09T20:58:38.404108Z",
-     "shell.execute_reply": "2023-12-09T20:58:38.403777Z"
+     "iopub.execute_input": "2023-12-22T05:36:01.038346Z",
+     "iopub.status.busy": "2023-12-22T05:36:01.038240Z",
+     "iopub.status.idle": "2023-12-22T05:36:01.394236Z",
+     "shell.execute_reply": "2023-12-22T05:36:01.393817Z"
     },
     "papermill": {
-     "duration": 0.308283,
-     "end_time": "2023-12-09T20:58:38.404921",
+     "duration": 0.360156,
+     "end_time": "2023-12-22T05:36:01.395073",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.096638",
+     "start_time": "2023-12-22T05:36:01.034917",
      "status": "completed"
     },
     "tags": []
@@ -1364,16 +1364,16 @@
    "id": "cf9b54ef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:38.411383Z",
-     "iopub.status.busy": "2023-12-09T20:58:38.411273Z",
-     "iopub.status.idle": "2023-12-09T20:58:38.780492Z",
-     "shell.execute_reply": "2023-12-09T20:58:38.780069Z"
+     "iopub.execute_input": "2023-12-22T05:36:01.402297Z",
+     "iopub.status.busy": "2023-12-22T05:36:01.402183Z",
+     "iopub.status.idle": "2023-12-22T05:36:01.825979Z",
+     "shell.execute_reply": "2023-12-22T05:36:01.825574Z"
     },
     "papermill": {
-     "duration": 0.37314,
-     "end_time": "2023-12-09T20:58:38.781240",
+     "duration": 0.428222,
+     "end_time": "2023-12-22T05:36:01.826829",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.408100",
+     "start_time": "2023-12-22T05:36:01.398607",
      "status": "completed"
     },
     "tags": []
@@ -1388,10 +1388,10 @@
    "id": "20d3e2cb",
    "metadata": {
     "papermill": {
-     "duration": 0.002835,
-     "end_time": "2023-12-09T20:58:38.787428",
+     "duration": 0.003516,
+     "end_time": "2023-12-22T05:36:01.833486",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.784593",
+     "start_time": "2023-12-22T05:36:01.829970",
      "status": "completed"
     },
     "tags": []
@@ -1406,16 +1406,16 @@
    "id": "5fdffa2b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:38.793166Z",
-     "iopub.status.busy": "2023-12-09T20:58:38.793074Z",
-     "iopub.status.idle": "2023-12-09T20:58:38.796940Z",
-     "shell.execute_reply": "2023-12-09T20:58:38.796669Z"
+     "iopub.execute_input": "2023-12-22T05:36:01.840440Z",
+     "iopub.status.busy": "2023-12-22T05:36:01.840324Z",
+     "iopub.status.idle": "2023-12-22T05:36:01.844726Z",
+     "shell.execute_reply": "2023-12-22T05:36:01.844440Z"
     },
     "papermill": {
-     "duration": 0.007382,
-     "end_time": "2023-12-09T20:58:38.797493",
+     "duration": 0.008539,
+     "end_time": "2023-12-22T05:36:01.845315",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.790111",
+     "start_time": "2023-12-22T05:36:01.836776",
      "status": "completed"
     },
     "tags": []
@@ -1430,10 +1430,10 @@
    "id": "ba80ae38",
    "metadata": {
     "papermill": {
-     "duration": 0.002798,
-     "end_time": "2023-12-09T20:58:38.803271",
+     "duration": 0.00307,
+     "end_time": "2023-12-22T05:36:01.851390",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.800473",
+     "start_time": "2023-12-22T05:36:01.848320",
      "status": "completed"
     },
     "tags": []
@@ -1448,16 +1448,16 @@
    "id": "1703fcef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:38.809156Z",
-     "iopub.status.busy": "2023-12-09T20:58:38.809064Z",
-     "iopub.status.idle": "2023-12-09T20:58:38.812792Z",
-     "shell.execute_reply": "2023-12-09T20:58:38.812541Z"
+     "iopub.execute_input": "2023-12-22T05:36:01.858270Z",
+     "iopub.status.busy": "2023-12-22T05:36:01.858119Z",
+     "iopub.status.idle": "2023-12-22T05:36:01.862951Z",
+     "shell.execute_reply": "2023-12-22T05:36:01.862562Z"
     },
     "papermill": {
-     "duration": 0.007238,
-     "end_time": "2023-12-09T20:58:38.813289",
+     "duration": 0.009083,
+     "end_time": "2023-12-22T05:36:01.863595",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.806051",
+     "start_time": "2023-12-22T05:36:01.854512",
      "status": "completed"
     },
     "tags": []
@@ -1472,10 +1472,10 @@
    "id": "337a8cfa",
    "metadata": {
     "papermill": {
-     "duration": 0.002743,
-     "end_time": "2023-12-09T20:58:38.818830",
+     "duration": 0.003027,
+     "end_time": "2023-12-22T05:36:01.869793",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.816087",
+     "start_time": "2023-12-22T05:36:01.866766",
      "status": "completed"
     },
     "tags": []
@@ -1490,16 +1490,16 @@
    "id": "bc244ead",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:38.824709Z",
-     "iopub.status.busy": "2023-12-09T20:58:38.824614Z",
-     "iopub.status.idle": "2023-12-09T20:58:38.831221Z",
-     "shell.execute_reply": "2023-12-09T20:58:38.830987Z"
+     "iopub.execute_input": "2023-12-22T05:36:01.876539Z",
+     "iopub.status.busy": "2023-12-22T05:36:01.876403Z",
+     "iopub.status.idle": "2023-12-22T05:36:01.884226Z",
+     "shell.execute_reply": "2023-12-22T05:36:01.883937Z"
     },
     "papermill": {
-     "duration": 0.010213,
-     "end_time": "2023-12-09T20:58:38.831782",
+     "duration": 0.011931,
+     "end_time": "2023-12-22T05:36:01.884808",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.821569",
+     "start_time": "2023-12-22T05:36:01.872877",
      "status": "completed"
     },
     "tags": []
@@ -1542,290 +1542,290 @@
        "      <th>0</th>\n",
        "      <td>-O3</td>\n",
        "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.109995</td>\n",
-       "      <td>0.001164</td>\n",
-       "      <td>0.887694</td>\n",
-       "      <td>0.001146</td>\n",
-       "      <td>1.481885</td>\n",
-       "      <td>Stream_ADD.default</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.056902</td>\n",
+       "      <td>0.006809</td>\n",
+       "      <td>0.935997</td>\n",
+       "      <td>0.000292</td>\n",
+       "      <td>0.994071</td>\n",
+       "      <td>Stream_ADD</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>-O2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.056447</td>\n",
+       "      <td>0.006957</td>\n",
+       "      <td>0.936493</td>\n",
+       "      <td>0.000103</td>\n",
+       "      <td>1.015401</td>\n",
+       "      <td>Stream_ADD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.526971</td>\n",
-       "      <td>0.027250</td>\n",
-       "      <td>0.443978</td>\n",
-       "      <td>0.001801</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.057756</td>\n",
+       "      <td>0.007283</td>\n",
+       "      <td>0.934745</td>\n",
+       "      <td>0.000215</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_ADD.default</td>\n",
+       "      <td>Stream_ADD</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>3</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.182242</td>\n",
-       "      <td>0.012817</td>\n",
-       "      <td>0.804312</td>\n",
-       "      <td>0.000629</td>\n",
-       "      <td>1.480293</td>\n",
-       "      <td>Stream_ADD.default</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-O2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.188412</td>\n",
-       "      <td>0.012662</td>\n",
-       "      <td>0.798407</td>\n",
-       "      <td>0.000519</td>\n",
-       "      <td>1.505408</td>\n",
-       "      <td>Stream_ADD.default</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.056757</td>\n",
+       "      <td>0.007064</td>\n",
+       "      <td>0.936231</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.007056</td>\n",
+       "      <td>Stream_ADD</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-O3</td>\n",
        "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.102321</td>\n",
-       "      <td>0.000995</td>\n",
-       "      <td>0.895513</td>\n",
-       "      <td>0.001172</td>\n",
-       "      <td>1.660180</td>\n",
-       "      <td>Stream_COPY.default</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.050100</td>\n",
+       "      <td>0.000739</td>\n",
+       "      <td>0.949018</td>\n",
+       "      <td>0.000143</td>\n",
+       "      <td>0.998211</td>\n",
+       "      <td>Stream_COPY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
+       "      <td>-O2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.050511</td>\n",
+       "      <td>0.000783</td>\n",
+       "      <td>0.948439</td>\n",
+       "      <td>0.000267</td>\n",
+       "      <td>1.022479</td>\n",
+       "      <td>Stream_COPY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.456877</td>\n",
-       "      <td>0.032924</td>\n",
-       "      <td>0.507152</td>\n",
-       "      <td>0.003047</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.050230</td>\n",
+       "      <td>0.000932</td>\n",
+       "      <td>0.948421</td>\n",
+       "      <td>0.000417</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_COPY.default</td>\n",
+       "      <td>Stream_COPY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>7</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.201569</td>\n",
-       "      <td>0.000738</td>\n",
-       "      <td>0.797165</td>\n",
-       "      <td>0.000527</td>\n",
-       "      <td>1.658998</td>\n",
-       "      <td>Stream_COPY.default</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>-O2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.209207</td>\n",
-       "      <td>0.000611</td>\n",
-       "      <td>0.789802</td>\n",
-       "      <td>0.000380</td>\n",
-       "      <td>1.709656</td>\n",
-       "      <td>Stream_COPY.default</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.050061</td>\n",
+       "      <td>0.000932</td>\n",
+       "      <td>0.948870</td>\n",
+       "      <td>0.000137</td>\n",
+       "      <td>1.004292</td>\n",
+       "      <td>Stream_COPY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>-O3</td>\n",
        "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.241718</td>\n",
-       "      <td>0.001036</td>\n",
-       "      <td>0.756053</td>\n",
-       "      <td>0.001194</td>\n",
-       "      <td>2.208209</td>\n",
-       "      <td>Stream_DOT.default</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.084316</td>\n",
+       "      <td>0.009045</td>\n",
+       "      <td>0.906415</td>\n",
+       "      <td>0.000224</td>\n",
+       "      <td>0.991317</td>\n",
+       "      <td>Stream_DOT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
+       "      <td>-O2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.083024</td>\n",
+       "      <td>0.009174</td>\n",
+       "      <td>0.907857</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.005844</td>\n",
+       "      <td>Stream_DOT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.520236</td>\n",
-       "      <td>0.027751</td>\n",
-       "      <td>0.451740</td>\n",
-       "      <td>0.000272</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.085235</td>\n",
+       "      <td>0.009152</td>\n",
+       "      <td>0.905275</td>\n",
+       "      <td>0.000337</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_DOT.default</td>\n",
+       "      <td>Stream_DOT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>11</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.300882</td>\n",
-       "      <td>0.020785</td>\n",
-       "      <td>0.677828</td>\n",
-       "      <td>0.000504</td>\n",
-       "      <td>2.186819</td>\n",
-       "      <td>Stream_DOT.default</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>-O2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.366981</td>\n",
-       "      <td>0.000601</td>\n",
-       "      <td>0.631834</td>\n",
-       "      <td>0.000584</td>\n",
-       "      <td>2.275193</td>\n",
-       "      <td>Stream_DOT.default</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.083823</td>\n",
+       "      <td>0.009310</td>\n",
+       "      <td>0.906485</td>\n",
+       "      <td>0.000381</td>\n",
+       "      <td>1.004005</td>\n",
+       "      <td>Stream_DOT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>-O3</td>\n",
        "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.126252</td>\n",
-       "      <td>0.015701</td>\n",
-       "      <td>0.856954</td>\n",
-       "      <td>0.001093</td>\n",
-       "      <td>1.960274</td>\n",
-       "      <td>Stream_MUL.default</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.067028</td>\n",
+       "      <td>0.007162</td>\n",
+       "      <td>0.925704</td>\n",
+       "      <td>0.000105</td>\n",
+       "      <td>0.998667</td>\n",
+       "      <td>Stream_MUL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
+       "      <td>-O2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.066528</td>\n",
+       "      <td>0.007375</td>\n",
+       "      <td>0.926011</td>\n",
+       "      <td>0.000086</td>\n",
+       "      <td>1.023686</td>\n",
+       "      <td>Stream_MUL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.421374</td>\n",
-       "      <td>0.001907</td>\n",
-       "      <td>0.575349</td>\n",
-       "      <td>0.001371</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.067740</td>\n",
+       "      <td>0.007602</td>\n",
+       "      <td>0.924428</td>\n",
+       "      <td>0.000229</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_MUL.default</td>\n",
+       "      <td>Stream_MUL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>15</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.251204</td>\n",
-       "      <td>0.030122</td>\n",
-       "      <td>0.717387</td>\n",
-       "      <td>0.001287</td>\n",
-       "      <td>1.962381</td>\n",
-       "      <td>Stream_MUL.default</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>-O2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.262410</td>\n",
-       "      <td>0.017472</td>\n",
-       "      <td>0.719536</td>\n",
-       "      <td>0.000583</td>\n",
-       "      <td>2.031418</td>\n",
-       "      <td>Stream_MUL.default</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.066369</td>\n",
+       "      <td>0.007401</td>\n",
+       "      <td>0.926359</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.005575</td>\n",
+       "      <td>Stream_MUL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>-O3</td>\n",
        "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.127593</td>\n",
-       "      <td>0.001151</td>\n",
-       "      <td>0.869938</td>\n",
-       "      <td>0.001317</td>\n",
-       "      <td>1.349819</td>\n",
-       "      <td>Stream_TRIAD.default</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.057196</td>\n",
+       "      <td>0.006985</td>\n",
+       "      <td>0.935524</td>\n",
+       "      <td>0.000295</td>\n",
+       "      <td>1.000759</td>\n",
+       "      <td>Stream_TRIAD</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
+       "      <td>-O2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.057509</td>\n",
+       "      <td>0.006734</td>\n",
+       "      <td>0.935501</td>\n",
+       "      <td>0.000256</td>\n",
+       "      <td>1.029841</td>\n",
+       "      <td>Stream_TRIAD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.600742</td>\n",
-       "      <td>0.002234</td>\n",
-       "      <td>0.393250</td>\n",
-       "      <td>0.003773</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.057387</td>\n",
+       "      <td>0.007198</td>\n",
+       "      <td>0.935109</td>\n",
+       "      <td>0.000307</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_TRIAD.default</td>\n",
+       "      <td>Stream_TRIAD</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>19</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.219064</td>\n",
-       "      <td>0.014731</td>\n",
-       "      <td>0.765560</td>\n",
-       "      <td>0.000644</td>\n",
-       "      <td>1.351241</td>\n",
-       "      <td>Stream_TRIAD.default</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>-O2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.260140</td>\n",
-       "      <td>0.000624</td>\n",
-       "      <td>0.738776</td>\n",
-       "      <td>0.000460</td>\n",
-       "      <td>1.391553</td>\n",
-       "      <td>Stream_TRIAD.default</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.056760</td>\n",
+       "      <td>0.006755</td>\n",
+       "      <td>0.936456</td>\n",
+       "      <td>0.000030</td>\n",
+       "      <td>1.010570</td>\n",
+       "      <td>Stream_TRIAD</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   opt_level opt_level_int   compiler  Retiring  Frontend bound  \\\n",
-       "0        -O3             3  g++-8.3.1  0.109995        0.001164   \n",
-       "1        -O0             0  g++-8.3.1  0.526971        0.027250   \n",
-       "2        -O1             1  g++-8.3.1  0.182242        0.012817   \n",
-       "3        -O2             2  g++-8.3.1  0.188412        0.012662   \n",
-       "4        -O3             3  g++-8.3.1  0.102321        0.000995   \n",
-       "5        -O0             0  g++-8.3.1  0.456877        0.032924   \n",
-       "6        -O1             1  g++-8.3.1  0.201569        0.000738   \n",
-       "7        -O2             2  g++-8.3.1  0.209207        0.000611   \n",
-       "8        -O3             3  g++-8.3.1  0.241718        0.001036   \n",
-       "9        -O0             0  g++-8.3.1  0.520236        0.027751   \n",
-       "10       -O1             1  g++-8.3.1  0.300882        0.020785   \n",
-       "11       -O2             2  g++-8.3.1  0.366981        0.000601   \n",
-       "12       -O3             3  g++-8.3.1  0.126252        0.015701   \n",
-       "13       -O0             0  g++-8.3.1  0.421374        0.001907   \n",
-       "14       -O1             1  g++-8.3.1  0.251204        0.030122   \n",
-       "15       -O2             2  g++-8.3.1  0.262410        0.017472   \n",
-       "16       -O3             3  g++-8.3.1  0.127593        0.001151   \n",
-       "17       -O0             0  g++-8.3.1  0.600742        0.002234   \n",
-       "18       -O1             1  g++-8.3.1  0.219064        0.014731   \n",
-       "19       -O2             2  g++-8.3.1  0.260140        0.000624   \n",
-       "\n",
-       "    Backend bound  Bad speculation   speedup                  name  \n",
-       "0        0.887694         0.001146  1.481885    Stream_ADD.default  \n",
-       "1        0.443978         0.001801  1.000000    Stream_ADD.default  \n",
-       "2        0.804312         0.000629  1.480293    Stream_ADD.default  \n",
-       "3        0.798407         0.000519  1.505408    Stream_ADD.default  \n",
-       "4        0.895513         0.001172  1.660180   Stream_COPY.default  \n",
-       "5        0.507152         0.003047  1.000000   Stream_COPY.default  \n",
-       "6        0.797165         0.000527  1.658998   Stream_COPY.default  \n",
-       "7        0.789802         0.000380  1.709656   Stream_COPY.default  \n",
-       "8        0.756053         0.001194  2.208209    Stream_DOT.default  \n",
-       "9        0.451740         0.000272  1.000000    Stream_DOT.default  \n",
-       "10       0.677828         0.000504  2.186819    Stream_DOT.default  \n",
-       "11       0.631834         0.000584  2.275193    Stream_DOT.default  \n",
-       "12       0.856954         0.001093  1.960274    Stream_MUL.default  \n",
-       "13       0.575349         0.001371  1.000000    Stream_MUL.default  \n",
-       "14       0.717387         0.001287  1.962381    Stream_MUL.default  \n",
-       "15       0.719536         0.000583  2.031418    Stream_MUL.default  \n",
-       "16       0.869938         0.001317  1.349819  Stream_TRIAD.default  \n",
-       "17       0.393250         0.003773  1.000000  Stream_TRIAD.default  \n",
-       "18       0.765560         0.000644  1.351241  Stream_TRIAD.default  \n",
-       "19       0.738776         0.000460  1.391553  Stream_TRIAD.default  "
+       "   opt_level opt_level_int    compiler  Retiring  Frontend bound  \\\n",
+       "0        -O3             3  g++-10.3.1  0.056902        0.006809   \n",
+       "1        -O2             2  g++-10.3.1  0.056447        0.006957   \n",
+       "2        -O0             0  g++-10.3.1  0.057756        0.007283   \n",
+       "3        -O1             1  g++-10.3.1  0.056757        0.007064   \n",
+       "4        -O3             3  g++-10.3.1  0.050100        0.000739   \n",
+       "5        -O2             2  g++-10.3.1  0.050511        0.000783   \n",
+       "6        -O0             0  g++-10.3.1  0.050230        0.000932   \n",
+       "7        -O1             1  g++-10.3.1  0.050061        0.000932   \n",
+       "8        -O3             3  g++-10.3.1  0.084316        0.009045   \n",
+       "9        -O2             2  g++-10.3.1  0.083024        0.009174   \n",
+       "10       -O0             0  g++-10.3.1  0.085235        0.009152   \n",
+       "11       -O1             1  g++-10.3.1  0.083823        0.009310   \n",
+       "12       -O3             3  g++-10.3.1  0.067028        0.007162   \n",
+       "13       -O2             2  g++-10.3.1  0.066528        0.007375   \n",
+       "14       -O0             0  g++-10.3.1  0.067740        0.007602   \n",
+       "15       -O1             1  g++-10.3.1  0.066369        0.007401   \n",
+       "16       -O3             3  g++-10.3.1  0.057196        0.006985   \n",
+       "17       -O2             2  g++-10.3.1  0.057509        0.006734   \n",
+       "18       -O0             0  g++-10.3.1  0.057387        0.007198   \n",
+       "19       -O1             1  g++-10.3.1  0.056760        0.006755   \n",
+       "\n",
+       "    Backend bound  Bad speculation   speedup          name  \n",
+       "0        0.935997         0.000292  0.994071    Stream_ADD  \n",
+       "1        0.936493         0.000103  1.015401    Stream_ADD  \n",
+       "2        0.934745         0.000215  1.000000    Stream_ADD  \n",
+       "3        0.936231         0.000000  1.007056    Stream_ADD  \n",
+       "4        0.949018         0.000143  0.998211   Stream_COPY  \n",
+       "5        0.948439         0.000267  1.022479   Stream_COPY  \n",
+       "6        0.948421         0.000417  1.000000   Stream_COPY  \n",
+       "7        0.948870         0.000137  1.004292   Stream_COPY  \n",
+       "8        0.906415         0.000224  0.991317    Stream_DOT  \n",
+       "9        0.907857         0.000000  1.005844    Stream_DOT  \n",
+       "10       0.905275         0.000337  1.000000    Stream_DOT  \n",
+       "11       0.906485         0.000381  1.004005    Stream_DOT  \n",
+       "12       0.925704         0.000105  0.998667    Stream_MUL  \n",
+       "13       0.926011         0.000086  1.023686    Stream_MUL  \n",
+       "14       0.924428         0.000229  1.000000    Stream_MUL  \n",
+       "15       0.926359         0.000000  1.005575    Stream_MUL  \n",
+       "16       0.935524         0.000295  1.000759  Stream_TRIAD  \n",
+       "17       0.935501         0.000256  1.029841  Stream_TRIAD  \n",
+       "18       0.935109         0.000307  1.000000  Stream_TRIAD  \n",
+       "19       0.936456         0.000030  1.010570  Stream_TRIAD  "
       ]
      },
      "execution_count": 21,
@@ -1843,10 +1843,10 @@
    "id": "f91e0e6d",
    "metadata": {
     "papermill": {
-     "duration": 0.003084,
-     "end_time": "2023-12-09T20:58:38.837828",
+     "duration": 0.003247,
+     "end_time": "2023-12-22T05:36:01.891249",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.834744",
+     "start_time": "2023-12-22T05:36:01.888002",
      "status": "completed"
     },
     "tags": []
@@ -1863,16 +1863,16 @@
    "id": "1ea23bf3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:38.844075Z",
-     "iopub.status.busy": "2023-12-09T20:58:38.843974Z",
-     "iopub.status.idle": "2023-12-09T20:58:38.851406Z",
-     "shell.execute_reply": "2023-12-09T20:58:38.851138Z"
+     "iopub.execute_input": "2023-12-22T05:36:01.897970Z",
+     "iopub.status.busy": "2023-12-22T05:36:01.897848Z",
+     "iopub.status.idle": "2023-12-22T05:36:01.905536Z",
+     "shell.execute_reply": "2023-12-22T05:36:01.905268Z"
     },
     "papermill": {
-     "duration": 0.011171,
-     "end_time": "2023-12-09T20:58:38.851940",
+     "duration": 0.011705,
+     "end_time": "2023-12-22T05:36:01.906195",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.840769",
+     "start_time": "2023-12-22T05:36:01.894490",
      "status": "completed"
     },
     "tags": []
@@ -1920,434 +1920,434 @@
        "      <th>0</th>\n",
        "      <td>-O3</td>\n",
        "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.109995</td>\n",
-       "      <td>0.001164</td>\n",
-       "      <td>0.887694</td>\n",
-       "      <td>0.001146</td>\n",
-       "      <td>1.481885</td>\n",
-       "      <td>Stream_ADD.default</td>\n",
-       "      <td>-1.194151</td>\n",
-       "      <td>-0.885276</td>\n",
-       "      <td>1.220477</td>\n",
-       "      <td>0.035396</td>\n",
-       "      <td>-0.185414</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.056902</td>\n",
+       "      <td>0.006809</td>\n",
+       "      <td>0.935997</td>\n",
+       "      <td>0.000292</td>\n",
+       "      <td>0.994071</td>\n",
+       "      <td>Stream_ADD</td>\n",
+       "      <td>-0.524400</td>\n",
+       "      <td>0.190302</td>\n",
+       "      <td>0.393491</td>\n",
+       "      <td>0.793402</td>\n",
+       "      <td>-1.167452</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>-O2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.056447</td>\n",
+       "      <td>0.006957</td>\n",
+       "      <td>0.936493</td>\n",
+       "      <td>0.000103</td>\n",
+       "      <td>1.015401</td>\n",
+       "      <td>Stream_ADD</td>\n",
+       "      <td>-0.562996</td>\n",
+       "      <td>0.242517</td>\n",
+       "      <td>0.428783</td>\n",
+       "      <td>-0.694227</td>\n",
+       "      <td>0.994632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.526971</td>\n",
-       "      <td>0.027250</td>\n",
-       "      <td>0.443978</td>\n",
-       "      <td>0.001801</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.057756</td>\n",
+       "      <td>0.007283</td>\n",
+       "      <td>0.934745</td>\n",
+       "      <td>0.000215</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_ADD.default</td>\n",
-       "      <td>1.670582</td>\n",
-       "      <td>1.424102</td>\n",
-       "      <td>-1.726185</td>\n",
-       "      <td>0.786925</td>\n",
-       "      <td>-1.319538</td>\n",
+       "      <td>Stream_ADD</td>\n",
+       "      <td>-0.451957</td>\n",
+       "      <td>0.357531</td>\n",
+       "      <td>0.304406</td>\n",
+       "      <td>0.187331</td>\n",
+       "      <td>-0.566466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>3</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.182242</td>\n",
-       "      <td>0.012817</td>\n",
-       "      <td>0.804312</td>\n",
-       "      <td>0.000629</td>\n",
-       "      <td>1.480293</td>\n",
-       "      <td>Stream_ADD.default</td>\n",
-       "      <td>-0.697795</td>\n",
-       "      <td>0.146357</td>\n",
-       "      <td>0.666748</td>\n",
-       "      <td>-0.557795</td>\n",
-       "      <td>-0.189160</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-O2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.188412</td>\n",
-       "      <td>0.012662</td>\n",
-       "      <td>0.798407</td>\n",
-       "      <td>0.000519</td>\n",
-       "      <td>1.505408</td>\n",
-       "      <td>Stream_ADD.default</td>\n",
-       "      <td>-0.655406</td>\n",
-       "      <td>0.132635</td>\n",
-       "      <td>0.627534</td>\n",
-       "      <td>-0.684005</td>\n",
-       "      <td>-0.130052</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.056757</td>\n",
+       "      <td>0.007064</td>\n",
+       "      <td>0.936231</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.007056</td>\n",
+       "      <td>Stream_ADD</td>\n",
+       "      <td>-0.536700</td>\n",
+       "      <td>0.280267</td>\n",
+       "      <td>0.410141</td>\n",
+       "      <td>-1.504945</td>\n",
+       "      <td>0.148736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-O3</td>\n",
        "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.102321</td>\n",
-       "      <td>0.000995</td>\n",
-       "      <td>0.895513</td>\n",
-       "      <td>0.001172</td>\n",
-       "      <td>1.660180</td>\n",
-       "      <td>Stream_COPY.default</td>\n",
-       "      <td>-1.246873</td>\n",
-       "      <td>-0.900238</td>\n",
-       "      <td>1.272402</td>\n",
-       "      <td>0.065228</td>\n",
-       "      <td>0.234208</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.050100</td>\n",
+       "      <td>0.000739</td>\n",
+       "      <td>0.949018</td>\n",
+       "      <td>0.000143</td>\n",
+       "      <td>0.998211</td>\n",
+       "      <td>Stream_COPY</td>\n",
+       "      <td>-1.101397</td>\n",
+       "      <td>-1.951214</td>\n",
+       "      <td>1.319992</td>\n",
+       "      <td>-0.379385</td>\n",
+       "      <td>-0.747844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
+       "      <td>-O2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.050511</td>\n",
+       "      <td>0.000783</td>\n",
+       "      <td>0.948439</td>\n",
+       "      <td>0.000267</td>\n",
+       "      <td>1.022479</td>\n",
+       "      <td>Stream_COPY</td>\n",
+       "      <td>-1.066533</td>\n",
+       "      <td>-1.935691</td>\n",
+       "      <td>1.278794</td>\n",
+       "      <td>0.596626</td>\n",
+       "      <td>1.711995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.456877</td>\n",
-       "      <td>0.032924</td>\n",
-       "      <td>0.507152</td>\n",
-       "      <td>0.003047</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.050230</td>\n",
+       "      <td>0.000932</td>\n",
+       "      <td>0.948421</td>\n",
+       "      <td>0.000417</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_COPY.default</td>\n",
-       "      <td>1.189019</td>\n",
-       "      <td>1.926418</td>\n",
-       "      <td>-1.306655</td>\n",
-       "      <td>2.216549</td>\n",
-       "      <td>-1.319538</td>\n",
+       "      <td>Stream_COPY</td>\n",
+       "      <td>-1.090369</td>\n",
+       "      <td>-1.883123</td>\n",
+       "      <td>1.277513</td>\n",
+       "      <td>1.777283</td>\n",
+       "      <td>-0.566466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>7</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.201569</td>\n",
-       "      <td>0.000738</td>\n",
-       "      <td>0.797165</td>\n",
-       "      <td>0.000527</td>\n",
-       "      <td>1.658998</td>\n",
-       "      <td>Stream_COPY.default</td>\n",
-       "      <td>-0.565014</td>\n",
-       "      <td>-0.922990</td>\n",
-       "      <td>0.619286</td>\n",
-       "      <td>-0.674826</td>\n",
-       "      <td>0.231424</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>-O2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.209207</td>\n",
-       "      <td>0.000611</td>\n",
-       "      <td>0.789802</td>\n",
-       "      <td>0.000380</td>\n",
-       "      <td>1.709656</td>\n",
-       "      <td>Stream_COPY.default</td>\n",
-       "      <td>-0.512538</td>\n",
-       "      <td>-0.934233</td>\n",
-       "      <td>0.570389</td>\n",
-       "      <td>-0.843490</td>\n",
-       "      <td>0.350649</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.050061</td>\n",
+       "      <td>0.000932</td>\n",
+       "      <td>0.948870</td>\n",
+       "      <td>0.000137</td>\n",
+       "      <td>1.004292</td>\n",
+       "      <td>Stream_COPY</td>\n",
+       "      <td>-1.104705</td>\n",
+       "      <td>-1.883123</td>\n",
+       "      <td>1.309461</td>\n",
+       "      <td>-0.426611</td>\n",
+       "      <td>-0.131445</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>-O3</td>\n",
        "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.241718</td>\n",
-       "      <td>0.001036</td>\n",
-       "      <td>0.756053</td>\n",
-       "      <td>0.001194</td>\n",
-       "      <td>2.208209</td>\n",
-       "      <td>Stream_DOT.default</td>\n",
-       "      <td>-0.289180</td>\n",
-       "      <td>-0.896608</td>\n",
-       "      <td>0.346266</td>\n",
-       "      <td>0.090470</td>\n",
-       "      <td>1.524002</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.084316</td>\n",
+       "      <td>0.009045</td>\n",
+       "      <td>0.906415</td>\n",
+       "      <td>0.000224</td>\n",
+       "      <td>0.991317</td>\n",
+       "      <td>Stream_DOT</td>\n",
+       "      <td>1.801063</td>\n",
+       "      <td>0.979170</td>\n",
+       "      <td>-1.711398</td>\n",
+       "      <td>0.258170</td>\n",
+       "      <td>-1.446604</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
+       "      <td>-O2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.083024</td>\n",
+       "      <td>0.009174</td>\n",
+       "      <td>0.907857</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.005844</td>\n",
+       "      <td>Stream_DOT</td>\n",
+       "      <td>1.691466</td>\n",
+       "      <td>1.024682</td>\n",
+       "      <td>-1.608793</td>\n",
+       "      <td>-1.504945</td>\n",
+       "      <td>0.025889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.520236</td>\n",
-       "      <td>0.027751</td>\n",
-       "      <td>0.451740</td>\n",
-       "      <td>0.000272</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.085235</td>\n",
+       "      <td>0.009152</td>\n",
+       "      <td>0.905275</td>\n",
+       "      <td>0.000337</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_DOT.default</td>\n",
-       "      <td>1.624311</td>\n",
-       "      <td>1.468455</td>\n",
-       "      <td>-1.674639</td>\n",
-       "      <td>-0.967406</td>\n",
-       "      <td>-1.319538</td>\n",
+       "      <td>Stream_DOT</td>\n",
+       "      <td>1.879020</td>\n",
+       "      <td>1.016920</td>\n",
+       "      <td>-1.792514</td>\n",
+       "      <td>1.147599</td>\n",
+       "      <td>-0.566466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>11</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.300882</td>\n",
-       "      <td>0.020785</td>\n",
-       "      <td>0.677828</td>\n",
-       "      <td>0.000504</td>\n",
-       "      <td>2.186819</td>\n",
-       "      <td>Stream_DOT.default</td>\n",
-       "      <td>0.117292</td>\n",
-       "      <td>0.851759</td>\n",
-       "      <td>-0.173217</td>\n",
-       "      <td>-0.701216</td>\n",
-       "      <td>1.473661</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>-O2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.366981</td>\n",
-       "      <td>0.000601</td>\n",
-       "      <td>0.631834</td>\n",
-       "      <td>0.000584</td>\n",
-       "      <td>2.275193</td>\n",
-       "      <td>Stream_DOT.default</td>\n",
-       "      <td>0.571410</td>\n",
-       "      <td>-0.935119</td>\n",
-       "      <td>-0.478657</td>\n",
-       "      <td>-0.609426</td>\n",
-       "      <td>1.681651</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.083823</td>\n",
+       "      <td>0.009310</td>\n",
+       "      <td>0.906485</td>\n",
+       "      <td>0.000381</td>\n",
+       "      <td>1.004005</td>\n",
+       "      <td>Stream_DOT</td>\n",
+       "      <td>1.759243</td>\n",
+       "      <td>1.072663</td>\n",
+       "      <td>-1.706417</td>\n",
+       "      <td>1.493925</td>\n",
+       "      <td>-0.160496</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>-O3</td>\n",
        "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.126252</td>\n",
-       "      <td>0.015701</td>\n",
-       "      <td>0.856954</td>\n",
-       "      <td>0.001093</td>\n",
-       "      <td>1.960274</td>\n",
-       "      <td>Stream_MUL.default</td>\n",
-       "      <td>-1.082461</td>\n",
-       "      <td>0.401676</td>\n",
-       "      <td>1.016337</td>\n",
-       "      <td>-0.025414</td>\n",
-       "      <td>0.940483</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.067028</td>\n",
+       "      <td>0.007162</td>\n",
+       "      <td>0.925704</td>\n",
+       "      <td>0.000105</td>\n",
+       "      <td>0.998667</td>\n",
+       "      <td>Stream_MUL</td>\n",
+       "      <td>0.334564</td>\n",
+       "      <td>0.314842</td>\n",
+       "      <td>-0.338901</td>\n",
+       "      <td>-0.678484</td>\n",
+       "      <td>-0.701590</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
+       "      <td>-O2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.066528</td>\n",
+       "      <td>0.007375</td>\n",
+       "      <td>0.926011</td>\n",
+       "      <td>0.000086</td>\n",
+       "      <td>1.023686</td>\n",
+       "      <td>Stream_MUL</td>\n",
+       "      <td>0.292150</td>\n",
+       "      <td>0.389989</td>\n",
+       "      <td>-0.317057</td>\n",
+       "      <td>-0.828034</td>\n",
+       "      <td>1.834334</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.421374</td>\n",
-       "      <td>0.001907</td>\n",
-       "      <td>0.575349</td>\n",
-       "      <td>0.001371</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.067740</td>\n",
+       "      <td>0.007602</td>\n",
+       "      <td>0.924428</td>\n",
+       "      <td>0.000229</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_MUL.default</td>\n",
-       "      <td>0.945104</td>\n",
-       "      <td>-0.819499</td>\n",
-       "      <td>-0.853767</td>\n",
-       "      <td>0.293555</td>\n",
-       "      <td>-1.319538</td>\n",
+       "      <td>Stream_MUL</td>\n",
+       "      <td>0.394961</td>\n",
+       "      <td>0.470075</td>\n",
+       "      <td>-0.429694</td>\n",
+       "      <td>0.297526</td>\n",
+       "      <td>-0.566466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>15</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.251204</td>\n",
-       "      <td>0.030122</td>\n",
-       "      <td>0.717387</td>\n",
-       "      <td>0.001287</td>\n",
-       "      <td>1.962381</td>\n",
-       "      <td>Stream_MUL.default</td>\n",
-       "      <td>-0.224008</td>\n",
-       "      <td>1.678358</td>\n",
-       "      <td>0.089490</td>\n",
-       "      <td>0.197176</td>\n",
-       "      <td>0.945442</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>-O2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.262410</td>\n",
-       "      <td>0.017472</td>\n",
-       "      <td>0.719536</td>\n",
-       "      <td>0.000583</td>\n",
-       "      <td>2.031418</td>\n",
-       "      <td>Stream_MUL.default</td>\n",
-       "      <td>-0.147020</td>\n",
-       "      <td>0.558461</td>\n",
-       "      <td>0.103761</td>\n",
-       "      <td>-0.610574</td>\n",
-       "      <td>1.107923</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.066369</td>\n",
+       "      <td>0.007401</td>\n",
+       "      <td>0.926359</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.005575</td>\n",
+       "      <td>Stream_MUL</td>\n",
+       "      <td>0.278663</td>\n",
+       "      <td>0.399162</td>\n",
+       "      <td>-0.292295</td>\n",
+       "      <td>-1.504945</td>\n",
+       "      <td>-0.001373</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>-O3</td>\n",
        "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.127593</td>\n",
-       "      <td>0.001151</td>\n",
-       "      <td>0.869938</td>\n",
-       "      <td>0.001317</td>\n",
-       "      <td>1.349819</td>\n",
-       "      <td>Stream_TRIAD.default</td>\n",
-       "      <td>-1.073248</td>\n",
-       "      <td>-0.886427</td>\n",
-       "      <td>1.102562</td>\n",
-       "      <td>0.231597</td>\n",
-       "      <td>-0.496233</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.057196</td>\n",
+       "      <td>0.006985</td>\n",
+       "      <td>0.935524</td>\n",
+       "      <td>0.000295</td>\n",
+       "      <td>1.000759</td>\n",
+       "      <td>Stream_TRIAD</td>\n",
+       "      <td>-0.499460</td>\n",
+       "      <td>0.252395</td>\n",
+       "      <td>0.359835</td>\n",
+       "      <td>0.817015</td>\n",
+       "      <td>-0.489583</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
+       "      <td>-O2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.057509</td>\n",
+       "      <td>0.006734</td>\n",
+       "      <td>0.935501</td>\n",
+       "      <td>0.000256</td>\n",
+       "      <td>1.029841</td>\n",
+       "      <td>Stream_TRIAD</td>\n",
+       "      <td>-0.472909</td>\n",
+       "      <td>0.163842</td>\n",
+       "      <td>0.358198</td>\n",
+       "      <td>0.510044</td>\n",
+       "      <td>2.458264</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.600742</td>\n",
-       "      <td>0.002234</td>\n",
-       "      <td>0.393250</td>\n",
-       "      <td>0.003773</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.057387</td>\n",
+       "      <td>0.007198</td>\n",
+       "      <td>0.935109</td>\n",
+       "      <td>0.000307</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_TRIAD.default</td>\n",
-       "      <td>2.177408</td>\n",
-       "      <td>-0.790550</td>\n",
-       "      <td>-2.063064</td>\n",
-       "      <td>3.049541</td>\n",
-       "      <td>-1.319538</td>\n",
+       "      <td>Stream_TRIAD</td>\n",
+       "      <td>-0.483258</td>\n",
+       "      <td>0.327543</td>\n",
+       "      <td>0.330306</td>\n",
+       "      <td>0.911468</td>\n",
+       "      <td>-0.566466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>19</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.219064</td>\n",
-       "      <td>0.014731</td>\n",
-       "      <td>0.765560</td>\n",
-       "      <td>0.000644</td>\n",
-       "      <td>1.351241</td>\n",
-       "      <td>Stream_TRIAD.default</td>\n",
-       "      <td>-0.444818</td>\n",
-       "      <td>0.315802</td>\n",
-       "      <td>0.409401</td>\n",
-       "      <td>-0.540584</td>\n",
-       "      <td>-0.492885</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>-O2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.260140</td>\n",
-       "      <td>0.000624</td>\n",
-       "      <td>0.738776</td>\n",
-       "      <td>0.000460</td>\n",
-       "      <td>1.391553</td>\n",
-       "      <td>Stream_TRIAD.default</td>\n",
-       "      <td>-0.162616</td>\n",
-       "      <td>-0.933082</td>\n",
-       "      <td>0.231531</td>\n",
-       "      <td>-0.751700</td>\n",
-       "      <td>-0.398010</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.056760</td>\n",
+       "      <td>0.006755</td>\n",
+       "      <td>0.936456</td>\n",
+       "      <td>0.000030</td>\n",
+       "      <td>1.010570</td>\n",
+       "      <td>Stream_TRIAD</td>\n",
+       "      <td>-0.536445</td>\n",
+       "      <td>0.171251</td>\n",
+       "      <td>0.426151</td>\n",
+       "      <td>-1.268813</td>\n",
+       "      <td>0.504867</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   opt_level opt_level_int   compiler  Retiring  Frontend bound  \\\n",
-       "0        -O3             3  g++-8.3.1  0.109995        0.001164   \n",
-       "1        -O0             0  g++-8.3.1  0.526971        0.027250   \n",
-       "2        -O1             1  g++-8.3.1  0.182242        0.012817   \n",
-       "3        -O2             2  g++-8.3.1  0.188412        0.012662   \n",
-       "4        -O3             3  g++-8.3.1  0.102321        0.000995   \n",
-       "5        -O0             0  g++-8.3.1  0.456877        0.032924   \n",
-       "6        -O1             1  g++-8.3.1  0.201569        0.000738   \n",
-       "7        -O2             2  g++-8.3.1  0.209207        0.000611   \n",
-       "8        -O3             3  g++-8.3.1  0.241718        0.001036   \n",
-       "9        -O0             0  g++-8.3.1  0.520236        0.027751   \n",
-       "10       -O1             1  g++-8.3.1  0.300882        0.020785   \n",
-       "11       -O2             2  g++-8.3.1  0.366981        0.000601   \n",
-       "12       -O3             3  g++-8.3.1  0.126252        0.015701   \n",
-       "13       -O0             0  g++-8.3.1  0.421374        0.001907   \n",
-       "14       -O1             1  g++-8.3.1  0.251204        0.030122   \n",
-       "15       -O2             2  g++-8.3.1  0.262410        0.017472   \n",
-       "16       -O3             3  g++-8.3.1  0.127593        0.001151   \n",
-       "17       -O0             0  g++-8.3.1  0.600742        0.002234   \n",
-       "18       -O1             1  g++-8.3.1  0.219064        0.014731   \n",
-       "19       -O2             2  g++-8.3.1  0.260140        0.000624   \n",
-       "\n",
-       "    Backend bound  Bad speculation   speedup                  name  \\\n",
-       "0        0.887694         0.001146  1.481885    Stream_ADD.default   \n",
-       "1        0.443978         0.001801  1.000000    Stream_ADD.default   \n",
-       "2        0.804312         0.000629  1.480293    Stream_ADD.default   \n",
-       "3        0.798407         0.000519  1.505408    Stream_ADD.default   \n",
-       "4        0.895513         0.001172  1.660180   Stream_COPY.default   \n",
-       "5        0.507152         0.003047  1.000000   Stream_COPY.default   \n",
-       "6        0.797165         0.000527  1.658998   Stream_COPY.default   \n",
-       "7        0.789802         0.000380  1.709656   Stream_COPY.default   \n",
-       "8        0.756053         0.001194  2.208209    Stream_DOT.default   \n",
-       "9        0.451740         0.000272  1.000000    Stream_DOT.default   \n",
-       "10       0.677828         0.000504  2.186819    Stream_DOT.default   \n",
-       "11       0.631834         0.000584  2.275193    Stream_DOT.default   \n",
-       "12       0.856954         0.001093  1.960274    Stream_MUL.default   \n",
-       "13       0.575349         0.001371  1.000000    Stream_MUL.default   \n",
-       "14       0.717387         0.001287  1.962381    Stream_MUL.default   \n",
-       "15       0.719536         0.000583  2.031418    Stream_MUL.default   \n",
-       "16       0.869938         0.001317  1.349819  Stream_TRIAD.default   \n",
-       "17       0.393250         0.003773  1.000000  Stream_TRIAD.default   \n",
-       "18       0.765560         0.000644  1.351241  Stream_TRIAD.default   \n",
-       "19       0.738776         0.000460  1.391553  Stream_TRIAD.default   \n",
-       "\n",
-       "    Retiring_norm  Frontend bound_norm  Backend bound_norm  \\\n",
-       "0       -1.194151            -0.885276            1.220477   \n",
-       "1        1.670582             1.424102           -1.726185   \n",
-       "2       -0.697795             0.146357            0.666748   \n",
-       "3       -0.655406             0.132635            0.627534   \n",
-       "4       -1.246873            -0.900238            1.272402   \n",
-       "5        1.189019             1.926418           -1.306655   \n",
-       "6       -0.565014            -0.922990            0.619286   \n",
-       "7       -0.512538            -0.934233            0.570389   \n",
-       "8       -0.289180            -0.896608            0.346266   \n",
-       "9        1.624311             1.468455           -1.674639   \n",
-       "10       0.117292             0.851759           -0.173217   \n",
-       "11       0.571410            -0.935119           -0.478657   \n",
-       "12      -1.082461             0.401676            1.016337   \n",
-       "13       0.945104            -0.819499           -0.853767   \n",
-       "14      -0.224008             1.678358            0.089490   \n",
-       "15      -0.147020             0.558461            0.103761   \n",
-       "16      -1.073248            -0.886427            1.102562   \n",
-       "17       2.177408            -0.790550           -2.063064   \n",
-       "18      -0.444818             0.315802            0.409401   \n",
-       "19      -0.162616            -0.933082            0.231531   \n",
-       "\n",
-       "    Bad speculation_norm  speedup_norm  \n",
-       "0               0.035396     -0.185414  \n",
-       "1               0.786925     -1.319538  \n",
-       "2              -0.557795     -0.189160  \n",
-       "3              -0.684005     -0.130052  \n",
-       "4               0.065228      0.234208  \n",
-       "5               2.216549     -1.319538  \n",
-       "6              -0.674826      0.231424  \n",
-       "7              -0.843490      0.350649  \n",
-       "8               0.090470      1.524002  \n",
-       "9              -0.967406     -1.319538  \n",
-       "10             -0.701216      1.473661  \n",
-       "11             -0.609426      1.681651  \n",
-       "12             -0.025414      0.940483  \n",
-       "13              0.293555     -1.319538  \n",
-       "14              0.197176      0.945442  \n",
-       "15             -0.610574      1.107923  \n",
-       "16              0.231597     -0.496233  \n",
-       "17              3.049541     -1.319538  \n",
-       "18             -0.540584     -0.492885  \n",
-       "19             -0.751700     -0.398010  "
+       "   opt_level opt_level_int    compiler  Retiring  Frontend bound  \\\n",
+       "0        -O3             3  g++-10.3.1  0.056902        0.006809   \n",
+       "1        -O2             2  g++-10.3.1  0.056447        0.006957   \n",
+       "2        -O0             0  g++-10.3.1  0.057756        0.007283   \n",
+       "3        -O1             1  g++-10.3.1  0.056757        0.007064   \n",
+       "4        -O3             3  g++-10.3.1  0.050100        0.000739   \n",
+       "5        -O2             2  g++-10.3.1  0.050511        0.000783   \n",
+       "6        -O0             0  g++-10.3.1  0.050230        0.000932   \n",
+       "7        -O1             1  g++-10.3.1  0.050061        0.000932   \n",
+       "8        -O3             3  g++-10.3.1  0.084316        0.009045   \n",
+       "9        -O2             2  g++-10.3.1  0.083024        0.009174   \n",
+       "10       -O0             0  g++-10.3.1  0.085235        0.009152   \n",
+       "11       -O1             1  g++-10.3.1  0.083823        0.009310   \n",
+       "12       -O3             3  g++-10.3.1  0.067028        0.007162   \n",
+       "13       -O2             2  g++-10.3.1  0.066528        0.007375   \n",
+       "14       -O0             0  g++-10.3.1  0.067740        0.007602   \n",
+       "15       -O1             1  g++-10.3.1  0.066369        0.007401   \n",
+       "16       -O3             3  g++-10.3.1  0.057196        0.006985   \n",
+       "17       -O2             2  g++-10.3.1  0.057509        0.006734   \n",
+       "18       -O0             0  g++-10.3.1  0.057387        0.007198   \n",
+       "19       -O1             1  g++-10.3.1  0.056760        0.006755   \n",
+       "\n",
+       "    Backend bound  Bad speculation   speedup          name  Retiring_norm  \\\n",
+       "0        0.935997         0.000292  0.994071    Stream_ADD      -0.524400   \n",
+       "1        0.936493         0.000103  1.015401    Stream_ADD      -0.562996   \n",
+       "2        0.934745         0.000215  1.000000    Stream_ADD      -0.451957   \n",
+       "3        0.936231         0.000000  1.007056    Stream_ADD      -0.536700   \n",
+       "4        0.949018         0.000143  0.998211   Stream_COPY      -1.101397   \n",
+       "5        0.948439         0.000267  1.022479   Stream_COPY      -1.066533   \n",
+       "6        0.948421         0.000417  1.000000   Stream_COPY      -1.090369   \n",
+       "7        0.948870         0.000137  1.004292   Stream_COPY      -1.104705   \n",
+       "8        0.906415         0.000224  0.991317    Stream_DOT       1.801063   \n",
+       "9        0.907857         0.000000  1.005844    Stream_DOT       1.691466   \n",
+       "10       0.905275         0.000337  1.000000    Stream_DOT       1.879020   \n",
+       "11       0.906485         0.000381  1.004005    Stream_DOT       1.759243   \n",
+       "12       0.925704         0.000105  0.998667    Stream_MUL       0.334564   \n",
+       "13       0.926011         0.000086  1.023686    Stream_MUL       0.292150   \n",
+       "14       0.924428         0.000229  1.000000    Stream_MUL       0.394961   \n",
+       "15       0.926359         0.000000  1.005575    Stream_MUL       0.278663   \n",
+       "16       0.935524         0.000295  1.000759  Stream_TRIAD      -0.499460   \n",
+       "17       0.935501         0.000256  1.029841  Stream_TRIAD      -0.472909   \n",
+       "18       0.935109         0.000307  1.000000  Stream_TRIAD      -0.483258   \n",
+       "19       0.936456         0.000030  1.010570  Stream_TRIAD      -0.536445   \n",
+       "\n",
+       "    Frontend bound_norm  Backend bound_norm  Bad speculation_norm  \\\n",
+       "0              0.190302            0.393491              0.793402   \n",
+       "1              0.242517            0.428783             -0.694227   \n",
+       "2              0.357531            0.304406              0.187331   \n",
+       "3              0.280267            0.410141             -1.504945   \n",
+       "4             -1.951214            1.319992             -0.379385   \n",
+       "5             -1.935691            1.278794              0.596626   \n",
+       "6             -1.883123            1.277513              1.777283   \n",
+       "7             -1.883123            1.309461             -0.426611   \n",
+       "8              0.979170           -1.711398              0.258170   \n",
+       "9              1.024682           -1.608793             -1.504945   \n",
+       "10             1.016920           -1.792514              1.147599   \n",
+       "11             1.072663           -1.706417              1.493925   \n",
+       "12             0.314842           -0.338901             -0.678484   \n",
+       "13             0.389989           -0.317057             -0.828034   \n",
+       "14             0.470075           -0.429694              0.297526   \n",
+       "15             0.399162           -0.292295             -1.504945   \n",
+       "16             0.252395            0.359835              0.817015   \n",
+       "17             0.163842            0.358198              0.510044   \n",
+       "18             0.327543            0.330306              0.911468   \n",
+       "19             0.171251            0.426151             -1.268813   \n",
+       "\n",
+       "    speedup_norm  \n",
+       "0      -1.167452  \n",
+       "1       0.994632  \n",
+       "2      -0.566466  \n",
+       "3       0.148736  \n",
+       "4      -0.747844  \n",
+       "5       1.711995  \n",
+       "6      -0.566466  \n",
+       "7      -0.131445  \n",
+       "8      -1.446604  \n",
+       "9       0.025889  \n",
+       "10     -0.566466  \n",
+       "11     -0.160496  \n",
+       "12     -0.701590  \n",
+       "13      1.834334  \n",
+       "14     -0.566466  \n",
+       "15     -0.001373  \n",
+       "16     -0.489583  \n",
+       "17      2.458264  \n",
+       "18     -0.566466  \n",
+       "19      0.504867  "
       ]
      },
      "execution_count": 22,
@@ -2365,10 +2365,10 @@
    "id": "bc0832fc",
    "metadata": {
     "papermill": {
-     "duration": 0.003091,
-     "end_time": "2023-12-09T20:58:38.858288",
+     "duration": 0.00328,
+     "end_time": "2023-12-22T05:36:01.912894",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.855197",
+     "start_time": "2023-12-22T05:36:01.909614",
      "status": "completed"
     },
     "tags": []
@@ -2389,16 +2389,16 @@
    "id": "8e8f0f53",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:38.864888Z",
-     "iopub.status.busy": "2023-12-09T20:58:38.864795Z",
-     "iopub.status.idle": "2023-12-09T20:58:38.866352Z",
-     "shell.execute_reply": "2023-12-09T20:58:38.866129Z"
+     "iopub.execute_input": "2023-12-22T05:36:01.920414Z",
+     "iopub.status.busy": "2023-12-22T05:36:01.920317Z",
+     "iopub.status.idle": "2023-12-22T05:36:01.921916Z",
+     "shell.execute_reply": "2023-12-22T05:36:01.921679Z"
     },
     "papermill": {
-     "duration": 0.005639,
-     "end_time": "2023-12-09T20:58:38.867068",
+     "duration": 0.005766,
+     "end_time": "2023-12-22T05:36:01.922409",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.861429",
+     "start_time": "2023-12-22T05:36:01.916643",
      "status": "completed"
     },
     "tags": []
@@ -2414,16 +2414,16 @@
    "id": "9c1f94a1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:38.873786Z",
-     "iopub.status.busy": "2023-12-09T20:58:38.873700Z",
-     "iopub.status.idle": "2023-12-09T20:58:38.960909Z",
-     "shell.execute_reply": "2023-12-09T20:58:38.960491Z"
+     "iopub.execute_input": "2023-12-22T05:36:01.929275Z",
+     "iopub.status.busy": "2023-12-22T05:36:01.929167Z",
+     "iopub.status.idle": "2023-12-22T05:36:02.059389Z",
+     "shell.execute_reply": "2023-12-22T05:36:02.058846Z"
     },
     "papermill": {
-     "duration": 0.092095,
-     "end_time": "2023-12-09T20:58:38.962228",
+     "duration": 0.143885,
+     "end_time": "2023-12-22T05:36:02.069538",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.870133",
+     "start_time": "2023-12-22T05:36:01.925653",
      "status": "completed"
     },
     "scrolled": false,
@@ -2494,10 +2494,10 @@
    "id": "95d78248",
    "metadata": {
     "papermill": {
-     "duration": 0.003777,
-     "end_time": "2023-12-09T20:58:38.974949",
+     "duration": 0.00405,
+     "end_time": "2023-12-22T05:36:02.094055",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.971172",
+     "start_time": "2023-12-22T05:36:02.090005",
      "status": "completed"
     },
     "tags": []
@@ -2512,16 +2512,16 @@
    "id": "a062f169",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:38.989419Z",
-     "iopub.status.busy": "2023-12-09T20:58:38.989315Z",
-     "iopub.status.idle": "2023-12-09T20:58:38.999013Z",
-     "shell.execute_reply": "2023-12-09T20:58:38.998768Z"
+     "iopub.execute_input": "2023-12-22T05:36:02.103874Z",
+     "iopub.status.busy": "2023-12-22T05:36:02.103729Z",
+     "iopub.status.idle": "2023-12-22T05:36:02.114907Z",
+     "shell.execute_reply": "2023-12-22T05:36:02.114542Z"
     },
     "papermill": {
-     "duration": 0.018457,
-     "end_time": "2023-12-09T20:58:38.999608",
+     "duration": 0.024013,
+     "end_time": "2023-12-22T05:36:02.122288",
      "exception": false,
-     "start_time": "2023-12-09T20:58:38.981151",
+     "start_time": "2023-12-22T05:36:02.098275",
      "status": "completed"
     },
     "tags": []
@@ -2573,116 +2573,140 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-O0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.526971</td>\n",
-       "      <td>0.027250</td>\n",
-       "      <td>0.443978</td>\n",
-       "      <td>0.001801</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>Stream_ADD.default</td>\n",
-       "      <td>1.670582</td>\n",
+       "      <th>0</th>\n",
+       "      <td>-O3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.056902</td>\n",
+       "      <td>0.006809</td>\n",
+       "      <td>0.935997</td>\n",
+       "      <td>0.000292</td>\n",
+       "      <td>0.994071</td>\n",
+       "      <td>Stream_ADD</td>\n",
+       "      <td>-0.524400</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.786925</td>\n",
-       "      <td>-1.319538</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cluster 1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Cluster 2</td>\n",
+       "      <td>0.793402</td>\n",
+       "      <td>-1.167452</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Cluster 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-O1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.182242</td>\n",
-       "      <td>0.012817</td>\n",
-       "      <td>0.804312</td>\n",
-       "      <td>0.000629</td>\n",
-       "      <td>1.480293</td>\n",
-       "      <td>Stream_ADD.default</td>\n",
-       "      <td>-0.697795</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.557795</td>\n",
-       "      <td>-0.189160</td>\n",
+       "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>Cluster 0</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.057756</td>\n",
+       "      <td>0.007283</td>\n",
+       "      <td>0.934745</td>\n",
+       "      <td>0.000215</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>Stream_ADD</td>\n",
+       "      <td>-0.451957</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.187331</td>\n",
+       "      <td>-0.566466</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
        "      <td>3</td>\n",
        "      <td>Cluster 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-O3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.109995</td>\n",
-       "      <td>0.001164</td>\n",
-       "      <td>0.887694</td>\n",
-       "      <td>0.001146</td>\n",
-       "      <td>1.481885</td>\n",
-       "      <td>Stream_ADD.default</td>\n",
-       "      <td>-1.194151</td>\n",
+       "      <th>3</th>\n",
+       "      <td>-O1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.056757</td>\n",
+       "      <td>0.007064</td>\n",
+       "      <td>0.936231</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.007056</td>\n",
+       "      <td>Stream_ADD</td>\n",
+       "      <td>-0.536700</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.035396</td>\n",
-       "      <td>-0.185414</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Cluster 0</td>\n",
+       "      <td>-1.504945</td>\n",
+       "      <td>0.148736</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Cluster 3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>1</th>\n",
        "      <td>-O2</td>\n",
        "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.188412</td>\n",
-       "      <td>0.012662</td>\n",
-       "      <td>0.798407</td>\n",
-       "      <td>0.000519</td>\n",
-       "      <td>1.505408</td>\n",
-       "      <td>Stream_ADD.default</td>\n",
-       "      <td>-0.655406</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.056447</td>\n",
+       "      <td>0.006957</td>\n",
+       "      <td>0.936493</td>\n",
+       "      <td>0.000103</td>\n",
+       "      <td>1.015401</td>\n",
+       "      <td>Stream_ADD</td>\n",
+       "      <td>-0.562996</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.684005</td>\n",
-       "      <td>-0.130052</td>\n",
+       "      <td>-0.694227</td>\n",
+       "      <td>0.994632</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Cluster 0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Cluster 2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-O3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.050100</td>\n",
+       "      <td>0.000739</td>\n",
+       "      <td>0.949018</td>\n",
+       "      <td>0.000143</td>\n",
+       "      <td>0.998211</td>\n",
+       "      <td>Stream_COPY</td>\n",
+       "      <td>-1.101397</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.379385</td>\n",
+       "      <td>-0.747844</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Cluster 2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
        "      <td>3</td>\n",
        "      <td>Cluster 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>6</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.456877</td>\n",
-       "      <td>0.032924</td>\n",
-       "      <td>0.507152</td>\n",
-       "      <td>0.003047</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.050230</td>\n",
+       "      <td>0.000932</td>\n",
+       "      <td>0.948421</td>\n",
+       "      <td>0.000417</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_COPY.default</td>\n",
-       "      <td>1.189019</td>\n",
+       "      <td>Stream_COPY</td>\n",
+       "      <td>-1.090369</td>\n",
        "      <td>...</td>\n",
-       "      <td>2.216549</td>\n",
-       "      <td>-1.319538</td>\n",
+       "      <td>1.777283</td>\n",
+       "      <td>-0.566466</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
        "      <td>2</td>\n",
@@ -2693,288 +2717,264 @@
        "      <td>Cluster 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>7</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.201569</td>\n",
-       "      <td>0.000738</td>\n",
-       "      <td>0.797165</td>\n",
-       "      <td>0.000527</td>\n",
-       "      <td>1.658998</td>\n",
-       "      <td>Stream_COPY.default</td>\n",
-       "      <td>-0.565014</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.050061</td>\n",
+       "      <td>0.000932</td>\n",
+       "      <td>0.948870</td>\n",
+       "      <td>0.000137</td>\n",
+       "      <td>1.004292</td>\n",
+       "      <td>Stream_COPY</td>\n",
+       "      <td>-1.104705</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.674826</td>\n",
-       "      <td>0.231424</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Cluster 0</td>\n",
+       "      <td>-0.426611</td>\n",
+       "      <td>-0.131445</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Cluster 2</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Cluster 0</td>\n",
        "      <td>3</td>\n",
        "      <td>Cluster 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-O3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.102321</td>\n",
-       "      <td>0.000995</td>\n",
-       "      <td>0.895513</td>\n",
-       "      <td>0.001172</td>\n",
-       "      <td>1.660180</td>\n",
-       "      <td>Stream_COPY.default</td>\n",
-       "      <td>-1.246873</td>\n",
+       "      <th>5</th>\n",
+       "      <td>-O2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.050511</td>\n",
+       "      <td>0.000783</td>\n",
+       "      <td>0.948439</td>\n",
+       "      <td>0.000267</td>\n",
+       "      <td>1.022479</td>\n",
+       "      <td>Stream_COPY</td>\n",
+       "      <td>-1.066533</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.065228</td>\n",
-       "      <td>0.234208</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Cluster 0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cluster 1</td>\n",
+       "      <td>0.596626</td>\n",
+       "      <td>1.711995</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
        "      <td>3</td>\n",
        "      <td>Cluster 3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Cluster 2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cluster 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>-O2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.209207</td>\n",
-       "      <td>0.000611</td>\n",
-       "      <td>0.789802</td>\n",
-       "      <td>0.000380</td>\n",
-       "      <td>1.709656</td>\n",
-       "      <td>Stream_COPY.default</td>\n",
-       "      <td>-0.512538</td>\n",
+       "      <th>8</th>\n",
+       "      <td>-O3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.084316</td>\n",
+       "      <td>0.009045</td>\n",
+       "      <td>0.906415</td>\n",
+       "      <td>0.000224</td>\n",
+       "      <td>0.991317</td>\n",
+       "      <td>Stream_DOT</td>\n",
+       "      <td>1.801063</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.843490</td>\n",
-       "      <td>0.350649</td>\n",
+       "      <td>0.258170</td>\n",
+       "      <td>-1.446604</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Cluster 2</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cluster 1</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
        "      <td>3</td>\n",
        "      <td>Cluster 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>10</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.520236</td>\n",
-       "      <td>0.027751</td>\n",
-       "      <td>0.451740</td>\n",
-       "      <td>0.000272</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.085235</td>\n",
+       "      <td>0.009152</td>\n",
+       "      <td>0.905275</td>\n",
+       "      <td>0.000337</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_DOT.default</td>\n",
-       "      <td>1.624311</td>\n",
+       "      <td>Stream_DOT</td>\n",
+       "      <td>1.879020</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.967406</td>\n",
-       "      <td>-1.319538</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cluster 1</td>\n",
+       "      <td>1.147599</td>\n",
+       "      <td>-0.566466</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Cluster 2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cluster 0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cluster 0</td>\n",
        "      <td>2</td>\n",
        "      <td>Cluster 2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cluster 1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Cluster 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>11</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.300882</td>\n",
-       "      <td>0.020785</td>\n",
-       "      <td>0.677828</td>\n",
-       "      <td>0.000504</td>\n",
-       "      <td>2.186819</td>\n",
-       "      <td>Stream_DOT.default</td>\n",
-       "      <td>0.117292</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.083823</td>\n",
+       "      <td>0.009310</td>\n",
+       "      <td>0.906485</td>\n",
+       "      <td>0.000381</td>\n",
+       "      <td>1.004005</td>\n",
+       "      <td>Stream_DOT</td>\n",
+       "      <td>1.759243</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.701216</td>\n",
-       "      <td>1.473661</td>\n",
+       "      <td>1.493925</td>\n",
+       "      <td>-0.160496</td>\n",
        "      <td>2</td>\n",
        "      <td>Cluster 2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Cluster 3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cluster 0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cluster 0</td>\n",
        "      <td>2</td>\n",
        "      <td>Cluster 2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cluster 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>-O3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.241718</td>\n",
-       "      <td>0.001036</td>\n",
-       "      <td>0.756053</td>\n",
-       "      <td>0.001194</td>\n",
-       "      <td>2.208209</td>\n",
-       "      <td>Stream_DOT.default</td>\n",
-       "      <td>-0.289180</td>\n",
+       "      <th>9</th>\n",
+       "      <td>-O2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.083024</td>\n",
+       "      <td>0.009174</td>\n",
+       "      <td>0.907857</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.005844</td>\n",
+       "      <td>Stream_DOT</td>\n",
+       "      <td>1.691466</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.090470</td>\n",
-       "      <td>1.524002</td>\n",
+       "      <td>-1.504945</td>\n",
+       "      <td>0.025889</td>\n",
        "      <td>2</td>\n",
        "      <td>Cluster 2</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Cluster 2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cluster 0</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>-O2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.366981</td>\n",
-       "      <td>0.000601</td>\n",
-       "      <td>0.631834</td>\n",
-       "      <td>0.000584</td>\n",
-       "      <td>2.275193</td>\n",
-       "      <td>Stream_DOT.default</td>\n",
-       "      <td>0.571410</td>\n",
+       "      <th>12</th>\n",
+       "      <td>-O3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.067028</td>\n",
+       "      <td>0.007162</td>\n",
+       "      <td>0.925704</td>\n",
+       "      <td>0.000105</td>\n",
+       "      <td>0.998667</td>\n",
+       "      <td>Stream_MUL</td>\n",
+       "      <td>0.334564</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.609426</td>\n",
-       "      <td>1.681651</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Cluster 2</td>\n",
+       "      <td>-0.678484</td>\n",
+       "      <td>-0.701590</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Cluster 2</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Cluster 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>14</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.421374</td>\n",
-       "      <td>0.001907</td>\n",
-       "      <td>0.575349</td>\n",
-       "      <td>0.001371</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.067740</td>\n",
+       "      <td>0.007602</td>\n",
+       "      <td>0.924428</td>\n",
+       "      <td>0.000229</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_MUL.default</td>\n",
-       "      <td>0.945104</td>\n",
+       "      <td>Stream_MUL</td>\n",
+       "      <td>0.394961</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.293555</td>\n",
-       "      <td>-1.319538</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cluster 1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cluster 1</td>\n",
+       "      <td>0.297526</td>\n",
+       "      <td>-0.566466</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>-O3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.126252</td>\n",
-       "      <td>0.015701</td>\n",
-       "      <td>0.856954</td>\n",
-       "      <td>0.001093</td>\n",
-       "      <td>1.960274</td>\n",
-       "      <td>Stream_MUL.default</td>\n",
-       "      <td>-1.082461</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.025414</td>\n",
-       "      <td>0.940483</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Cluster 2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Cluster 3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Cluster 2</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Cluster 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>15</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.251204</td>\n",
-       "      <td>0.030122</td>\n",
-       "      <td>0.717387</td>\n",
-       "      <td>0.001287</td>\n",
-       "      <td>1.962381</td>\n",
-       "      <td>Stream_MUL.default</td>\n",
-       "      <td>-0.224008</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.066369</td>\n",
+       "      <td>0.007401</td>\n",
+       "      <td>0.926359</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.005575</td>\n",
+       "      <td>Stream_MUL</td>\n",
+       "      <td>0.278663</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.197176</td>\n",
-       "      <td>0.945442</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Cluster 2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Cluster 3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Cluster 2</td>\n",
+       "      <td>-1.504945</td>\n",
+       "      <td>-0.001373</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cluster 0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>13</th>\n",
        "      <td>-O2</td>\n",
        "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.262410</td>\n",
-       "      <td>0.017472</td>\n",
-       "      <td>0.719536</td>\n",
-       "      <td>0.000583</td>\n",
-       "      <td>2.031418</td>\n",
-       "      <td>Stream_MUL.default</td>\n",
-       "      <td>-0.147020</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.066528</td>\n",
+       "      <td>0.007375</td>\n",
+       "      <td>0.926011</td>\n",
+       "      <td>0.000086</td>\n",
+       "      <td>1.023686</td>\n",
+       "      <td>Stream_MUL</td>\n",
+       "      <td>0.292150</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.610574</td>\n",
-       "      <td>1.107923</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Cluster 2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Cluster 3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Cluster 2</td>\n",
+       "      <td>-0.828034</td>\n",
+       "      <td>1.834334</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cluster 0</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Cluster 2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cluster 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
+       "      <th>18</th>\n",
        "      <td>-O0</td>\n",
        "      <td>0</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.600742</td>\n",
-       "      <td>0.002234</td>\n",
-       "      <td>0.393250</td>\n",
-       "      <td>0.003773</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.057387</td>\n",
+       "      <td>0.007198</td>\n",
+       "      <td>0.935109</td>\n",
+       "      <td>0.000307</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>Stream_TRIAD.default</td>\n",
-       "      <td>2.177408</td>\n",
+       "      <td>Stream_TRIAD</td>\n",
+       "      <td>-0.483258</td>\n",
        "      <td>...</td>\n",
-       "      <td>3.049541</td>\n",
-       "      <td>-1.319538</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cluster 1</td>\n",
+       "      <td>0.911468</td>\n",
+       "      <td>-0.566466</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cluster 0</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
        "      <td>2</td>\n",
@@ -2984,73 +2984,73 @@
        "      <th>16</th>\n",
        "      <td>-O3</td>\n",
        "      <td>3</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.127593</td>\n",
-       "      <td>0.001151</td>\n",
-       "      <td>0.869938</td>\n",
-       "      <td>0.001317</td>\n",
-       "      <td>1.349819</td>\n",
-       "      <td>Stream_TRIAD.default</td>\n",
-       "      <td>-1.073248</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.057196</td>\n",
+       "      <td>0.006985</td>\n",
+       "      <td>0.935524</td>\n",
+       "      <td>0.000295</td>\n",
+       "      <td>1.000759</td>\n",
+       "      <td>Stream_TRIAD</td>\n",
+       "      <td>-0.499460</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.231597</td>\n",
-       "      <td>-0.496233</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Cluster 0</td>\n",
+       "      <td>0.817015</td>\n",
+       "      <td>-0.489583</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Cluster 0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Cluster 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>19</th>\n",
        "      <td>-O1</td>\n",
        "      <td>1</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.219064</td>\n",
-       "      <td>0.014731</td>\n",
-       "      <td>0.765560</td>\n",
-       "      <td>0.000644</td>\n",
-       "      <td>1.351241</td>\n",
-       "      <td>Stream_TRIAD.default</td>\n",
-       "      <td>-0.444818</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.056760</td>\n",
+       "      <td>0.006755</td>\n",
+       "      <td>0.936456</td>\n",
+       "      <td>0.000030</td>\n",
+       "      <td>1.010570</td>\n",
+       "      <td>Stream_TRIAD</td>\n",
+       "      <td>-0.536445</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.540584</td>\n",
-       "      <td>-0.492885</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Cluster 0</td>\n",
+       "      <td>-1.268813</td>\n",
+       "      <td>0.504867</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Cluster 3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cluster 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>17</th>\n",
        "      <td>-O2</td>\n",
        "      <td>2</td>\n",
-       "      <td>g++-8.3.1</td>\n",
-       "      <td>0.260140</td>\n",
-       "      <td>0.000624</td>\n",
-       "      <td>0.738776</td>\n",
-       "      <td>0.000460</td>\n",
-       "      <td>1.391553</td>\n",
-       "      <td>Stream_TRIAD.default</td>\n",
-       "      <td>-0.162616</td>\n",
+       "      <td>g++-10.3.1</td>\n",
+       "      <td>0.057509</td>\n",
+       "      <td>0.006734</td>\n",
+       "      <td>0.935501</td>\n",
+       "      <td>0.000256</td>\n",
+       "      <td>1.029841</td>\n",
+       "      <td>Stream_TRIAD</td>\n",
+       "      <td>-0.472909</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.751700</td>\n",
-       "      <td>-0.398010</td>\n",
+       "      <td>0.510044</td>\n",
+       "      <td>2.458264</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
        "      <td>1</td>\n",
        "      <td>Cluster 1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Cluster 2</td>\n",
        "      <td>0</td>\n",
        "      <td>Cluster 0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Cluster 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -3058,137 +3058,137 @@
        "</div>"
       ],
       "text/plain": [
-       "   opt_level opt_level_int   compiler  Retiring  Frontend bound  \\\n",
-       "1        -O0             0  g++-8.3.1  0.526971        0.027250   \n",
-       "2        -O1             1  g++-8.3.1  0.182242        0.012817   \n",
-       "0        -O3             3  g++-8.3.1  0.109995        0.001164   \n",
-       "3        -O2             2  g++-8.3.1  0.188412        0.012662   \n",
-       "5        -O0             0  g++-8.3.1  0.456877        0.032924   \n",
-       "6        -O1             1  g++-8.3.1  0.201569        0.000738   \n",
-       "4        -O3             3  g++-8.3.1  0.102321        0.000995   \n",
-       "7        -O2             2  g++-8.3.1  0.209207        0.000611   \n",
-       "9        -O0             0  g++-8.3.1  0.520236        0.027751   \n",
-       "10       -O1             1  g++-8.3.1  0.300882        0.020785   \n",
-       "8        -O3             3  g++-8.3.1  0.241718        0.001036   \n",
-       "11       -O2             2  g++-8.3.1  0.366981        0.000601   \n",
-       "13       -O0             0  g++-8.3.1  0.421374        0.001907   \n",
-       "12       -O3             3  g++-8.3.1  0.126252        0.015701   \n",
-       "14       -O1             1  g++-8.3.1  0.251204        0.030122   \n",
-       "15       -O2             2  g++-8.3.1  0.262410        0.017472   \n",
-       "17       -O0             0  g++-8.3.1  0.600742        0.002234   \n",
-       "16       -O3             3  g++-8.3.1  0.127593        0.001151   \n",
-       "18       -O1             1  g++-8.3.1  0.219064        0.014731   \n",
-       "19       -O2             2  g++-8.3.1  0.260140        0.000624   \n",
-       "\n",
-       "    Backend bound  Bad speculation   speedup                  name  \\\n",
-       "1        0.443978         0.001801  1.000000    Stream_ADD.default   \n",
-       "2        0.804312         0.000629  1.480293    Stream_ADD.default   \n",
-       "0        0.887694         0.001146  1.481885    Stream_ADD.default   \n",
-       "3        0.798407         0.000519  1.505408    Stream_ADD.default   \n",
-       "5        0.507152         0.003047  1.000000   Stream_COPY.default   \n",
-       "6        0.797165         0.000527  1.658998   Stream_COPY.default   \n",
-       "4        0.895513         0.001172  1.660180   Stream_COPY.default   \n",
-       "7        0.789802         0.000380  1.709656   Stream_COPY.default   \n",
-       "9        0.451740         0.000272  1.000000    Stream_DOT.default   \n",
-       "10       0.677828         0.000504  2.186819    Stream_DOT.default   \n",
-       "8        0.756053         0.001194  2.208209    Stream_DOT.default   \n",
-       "11       0.631834         0.000584  2.275193    Stream_DOT.default   \n",
-       "13       0.575349         0.001371  1.000000    Stream_MUL.default   \n",
-       "12       0.856954         0.001093  1.960274    Stream_MUL.default   \n",
-       "14       0.717387         0.001287  1.962381    Stream_MUL.default   \n",
-       "15       0.719536         0.000583  2.031418    Stream_MUL.default   \n",
-       "17       0.393250         0.003773  1.000000  Stream_TRIAD.default   \n",
-       "16       0.869938         0.001317  1.349819  Stream_TRIAD.default   \n",
-       "18       0.765560         0.000644  1.351241  Stream_TRIAD.default   \n",
-       "19       0.738776         0.000460  1.391553  Stream_TRIAD.default   \n",
-       "\n",
-       "    Retiring_norm  ...  Bad speculation_norm  speedup_norm  \\\n",
-       "1        1.670582  ...              0.786925     -1.319538   \n",
-       "2       -0.697795  ...             -0.557795     -0.189160   \n",
-       "0       -1.194151  ...              0.035396     -0.185414   \n",
-       "3       -0.655406  ...             -0.684005     -0.130052   \n",
-       "5        1.189019  ...              2.216549     -1.319538   \n",
-       "6       -0.565014  ...             -0.674826      0.231424   \n",
-       "4       -1.246873  ...              0.065228      0.234208   \n",
-       "7       -0.512538  ...             -0.843490      0.350649   \n",
-       "9        1.624311  ...             -0.967406     -1.319538   \n",
-       "10       0.117292  ...             -0.701216      1.473661   \n",
-       "8       -0.289180  ...              0.090470      1.524002   \n",
-       "11       0.571410  ...             -0.609426      1.681651   \n",
-       "13       0.945104  ...              0.293555     -1.319538   \n",
-       "12      -1.082461  ...             -0.025414      0.940483   \n",
-       "14      -0.224008  ...              0.197176      0.945442   \n",
-       "15      -0.147020  ...             -0.610574      1.107923   \n",
-       "17       2.177408  ...              3.049541     -1.319538   \n",
-       "16      -1.073248  ...              0.231597     -0.496233   \n",
-       "18      -0.444818  ...             -0.540584     -0.492885   \n",
-       "19      -0.162616  ...             -0.751700     -0.398010   \n",
-       "\n",
-       "    Retiring_cluster_id  Retiring_cluster  Frontend bound_cluster_id  \\\n",
-       "1                     1         Cluster 1                          2   \n",
-       "2                     0         Cluster 0                          1   \n",
-       "0                     0         Cluster 0                          1   \n",
-       "3                     0         Cluster 0                          1   \n",
-       "5                     1         Cluster 1                          2   \n",
-       "6                     0         Cluster 0                          1   \n",
-       "4                     0         Cluster 0                          1   \n",
-       "7                     0         Cluster 0                          1   \n",
-       "9                     1         Cluster 1                          2   \n",
-       "10                    2         Cluster 2                          3   \n",
-       "8                     2         Cluster 2                          0   \n",
-       "11                    2         Cluster 2                          0   \n",
-       "13                    1         Cluster 1                          1   \n",
-       "12                    2         Cluster 2                          3   \n",
-       "14                    2         Cluster 2                          3   \n",
-       "15                    2         Cluster 2                          3   \n",
-       "17                    1         Cluster 1                          1   \n",
-       "16                    0         Cluster 0                          1   \n",
-       "18                    0         Cluster 0                          1   \n",
-       "19                    0         Cluster 0                          1   \n",
-       "\n",
-       "   Frontend bound_cluster  Backend bound_cluster_id Backend bound_cluster  \\\n",
-       "1               Cluster 2                         1             Cluster 1   \n",
-       "2               Cluster 1                         0             Cluster 0   \n",
-       "0               Cluster 1                         0             Cluster 0   \n",
-       "3               Cluster 1                         0             Cluster 0   \n",
-       "5               Cluster 2                         1             Cluster 1   \n",
-       "6               Cluster 1                         0             Cluster 0   \n",
-       "4               Cluster 1                         0             Cluster 0   \n",
-       "7               Cluster 1                         0             Cluster 0   \n",
-       "9               Cluster 2                         1             Cluster 1   \n",
-       "10              Cluster 3                         2             Cluster 2   \n",
-       "8               Cluster 0                         2             Cluster 2   \n",
-       "11              Cluster 0                         2             Cluster 2   \n",
-       "13              Cluster 1                         1             Cluster 1   \n",
-       "12              Cluster 3                         2             Cluster 2   \n",
-       "14              Cluster 3                         2             Cluster 2   \n",
-       "15              Cluster 3                         2             Cluster 2   \n",
-       "17              Cluster 1                         1             Cluster 1   \n",
-       "16              Cluster 1                         0             Cluster 0   \n",
-       "18              Cluster 1                         0             Cluster 0   \n",
-       "19              Cluster 1                         0             Cluster 0   \n",
+       "   opt_level opt_level_int    compiler  Retiring  Frontend bound  \\\n",
+       "0        -O3             3  g++-10.3.1  0.056902        0.006809   \n",
+       "2        -O0             0  g++-10.3.1  0.057756        0.007283   \n",
+       "3        -O1             1  g++-10.3.1  0.056757        0.007064   \n",
+       "1        -O2             2  g++-10.3.1  0.056447        0.006957   \n",
+       "4        -O3             3  g++-10.3.1  0.050100        0.000739   \n",
+       "6        -O0             0  g++-10.3.1  0.050230        0.000932   \n",
+       "7        -O1             1  g++-10.3.1  0.050061        0.000932   \n",
+       "5        -O2             2  g++-10.3.1  0.050511        0.000783   \n",
+       "8        -O3             3  g++-10.3.1  0.084316        0.009045   \n",
+       "10       -O0             0  g++-10.3.1  0.085235        0.009152   \n",
+       "11       -O1             1  g++-10.3.1  0.083823        0.009310   \n",
+       "9        -O2             2  g++-10.3.1  0.083024        0.009174   \n",
+       "12       -O3             3  g++-10.3.1  0.067028        0.007162   \n",
+       "14       -O0             0  g++-10.3.1  0.067740        0.007602   \n",
+       "15       -O1             1  g++-10.3.1  0.066369        0.007401   \n",
+       "13       -O2             2  g++-10.3.1  0.066528        0.007375   \n",
+       "18       -O0             0  g++-10.3.1  0.057387        0.007198   \n",
+       "16       -O3             3  g++-10.3.1  0.057196        0.006985   \n",
+       "19       -O1             1  g++-10.3.1  0.056760        0.006755   \n",
+       "17       -O2             2  g++-10.3.1  0.057509        0.006734   \n",
+       "\n",
+       "    Backend bound  Bad speculation   speedup          name  Retiring_norm  \\\n",
+       "0        0.935997         0.000292  0.994071    Stream_ADD      -0.524400   \n",
+       "2        0.934745         0.000215  1.000000    Stream_ADD      -0.451957   \n",
+       "3        0.936231         0.000000  1.007056    Stream_ADD      -0.536700   \n",
+       "1        0.936493         0.000103  1.015401    Stream_ADD      -0.562996   \n",
+       "4        0.949018         0.000143  0.998211   Stream_COPY      -1.101397   \n",
+       "6        0.948421         0.000417  1.000000   Stream_COPY      -1.090369   \n",
+       "7        0.948870         0.000137  1.004292   Stream_COPY      -1.104705   \n",
+       "5        0.948439         0.000267  1.022479   Stream_COPY      -1.066533   \n",
+       "8        0.906415         0.000224  0.991317    Stream_DOT       1.801063   \n",
+       "10       0.905275         0.000337  1.000000    Stream_DOT       1.879020   \n",
+       "11       0.906485         0.000381  1.004005    Stream_DOT       1.759243   \n",
+       "9        0.907857         0.000000  1.005844    Stream_DOT       1.691466   \n",
+       "12       0.925704         0.000105  0.998667    Stream_MUL       0.334564   \n",
+       "14       0.924428         0.000229  1.000000    Stream_MUL       0.394961   \n",
+       "15       0.926359         0.000000  1.005575    Stream_MUL       0.278663   \n",
+       "13       0.926011         0.000086  1.023686    Stream_MUL       0.292150   \n",
+       "18       0.935109         0.000307  1.000000  Stream_TRIAD      -0.483258   \n",
+       "16       0.935524         0.000295  1.000759  Stream_TRIAD      -0.499460   \n",
+       "19       0.936456         0.000030  1.010570  Stream_TRIAD      -0.536445   \n",
+       "17       0.935501         0.000256  1.029841  Stream_TRIAD      -0.472909   \n",
+       "\n",
+       "    ...  Bad speculation_norm  speedup_norm  Retiring_cluster_id  \\\n",
+       "0   ...              0.793402     -1.167452                    1   \n",
+       "2   ...              0.187331     -0.566466                    1   \n",
+       "3   ...             -1.504945      0.148736                    1   \n",
+       "1   ...             -0.694227      0.994632                    0   \n",
+       "4   ...             -0.379385     -0.747844                    1   \n",
+       "6   ...              1.777283     -0.566466                    1   \n",
+       "7   ...             -0.426611     -0.131445                    1   \n",
+       "5   ...              0.596626      1.711995                    0   \n",
+       "8   ...              0.258170     -1.446604                    2   \n",
+       "10  ...              1.147599     -0.566466                    2   \n",
+       "11  ...              1.493925     -0.160496                    2   \n",
+       "9   ...             -1.504945      0.025889                    2   \n",
+       "12  ...             -0.678484     -0.701590                    1   \n",
+       "14  ...              0.297526     -0.566466                    1   \n",
+       "15  ...             -1.504945     -0.001373                    1   \n",
+       "13  ...             -0.828034      1.834334                    0   \n",
+       "18  ...              0.911468     -0.566466                    1   \n",
+       "16  ...              0.817015     -0.489583                    1   \n",
+       "19  ...             -1.268813      0.504867                    1   \n",
+       "17  ...              0.510044      2.458264                    0   \n",
+       "\n",
+       "    Retiring_cluster  Frontend bound_cluster_id Frontend bound_cluster  \\\n",
+       "0          Cluster 1                          0              Cluster 0   \n",
+       "2          Cluster 1                          0              Cluster 0   \n",
+       "3          Cluster 1                          0              Cluster 0   \n",
+       "1          Cluster 0                          1              Cluster 1   \n",
+       "4          Cluster 1                          2              Cluster 2   \n",
+       "6          Cluster 1                          2              Cluster 2   \n",
+       "7          Cluster 1                          2              Cluster 2   \n",
+       "5          Cluster 0                          3              Cluster 3   \n",
+       "8          Cluster 2                          0              Cluster 0   \n",
+       "10         Cluster 2                          0              Cluster 0   \n",
+       "11         Cluster 2                          0              Cluster 0   \n",
+       "9          Cluster 2                          0              Cluster 0   \n",
+       "12         Cluster 1                          0              Cluster 0   \n",
+       "14         Cluster 1                          0              Cluster 0   \n",
+       "15         Cluster 1                          0              Cluster 0   \n",
+       "13         Cluster 0                          1              Cluster 1   \n",
+       "18         Cluster 1                          0              Cluster 0   \n",
+       "16         Cluster 1                          0              Cluster 0   \n",
+       "19         Cluster 1                          0              Cluster 0   \n",
+       "17         Cluster 0                          1              Cluster 1   \n",
+       "\n",
+       "    Backend bound_cluster_id Backend bound_cluster  \\\n",
+       "0                          1             Cluster 1   \n",
+       "2                          1             Cluster 1   \n",
+       "3                          1             Cluster 1   \n",
+       "1                          2             Cluster 2   \n",
+       "4                          1             Cluster 1   \n",
+       "6                          1             Cluster 1   \n",
+       "7                          1             Cluster 1   \n",
+       "5                          2             Cluster 2   \n",
+       "8                          0             Cluster 0   \n",
+       "10                         0             Cluster 0   \n",
+       "11                         0             Cluster 0   \n",
+       "9                          0             Cluster 0   \n",
+       "12                         1             Cluster 1   \n",
+       "14                         1             Cluster 1   \n",
+       "15                         1             Cluster 1   \n",
+       "13                         2             Cluster 2   \n",
+       "18                         1             Cluster 1   \n",
+       "16                         1             Cluster 1   \n",
+       "19                         1             Cluster 1   \n",
+       "17                         2             Cluster 2   \n",
        "\n",
        "    Bad speculation_cluster_id Bad speculation_cluster  \n",
-       "1                            0               Cluster 0  \n",
+       "0                            2               Cluster 2  \n",
        "2                            3               Cluster 3  \n",
-       "0                            3               Cluster 3  \n",
-       "3                            3               Cluster 3  \n",
-       "5                            2               Cluster 2  \n",
-       "6                            3               Cluster 3  \n",
+       "3                            1               Cluster 1  \n",
+       "1                            1               Cluster 1  \n",
        "4                            3               Cluster 3  \n",
+       "6                            2               Cluster 2  \n",
        "7                            3               Cluster 3  \n",
-       "9                            3               Cluster 3  \n",
-       "10                           1               Cluster 1  \n",
-       "8                            1               Cluster 1  \n",
-       "11                           1               Cluster 1  \n",
-       "13                           0               Cluster 0  \n",
-       "12                           1               Cluster 1  \n",
-       "14                           1               Cluster 1  \n",
+       "5                            0               Cluster 0  \n",
+       "8                            3               Cluster 3  \n",
+       "10                           2               Cluster 2  \n",
+       "11                           2               Cluster 2  \n",
+       "9                            1               Cluster 1  \n",
+       "12                           3               Cluster 3  \n",
+       "14                           3               Cluster 3  \n",
        "15                           1               Cluster 1  \n",
-       "17                           2               Cluster 2  \n",
-       "16                           0               Cluster 0  \n",
-       "18                           3               Cluster 3  \n",
-       "19                           3               Cluster 3  \n",
+       "13                           0               Cluster 0  \n",
+       "18                           2               Cluster 2  \n",
+       "16                           2               Cluster 2  \n",
+       "19                           1               Cluster 1  \n",
+       "17                           0               Cluster 0  \n",
        "\n",
        "[20 rows x 22 columns]"
       ]
@@ -3208,16 +3208,16 @@
    "id": "2cb3f41b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:39.007495Z",
-     "iopub.status.busy": "2023-12-09T20:58:39.007405Z",
-     "iopub.status.idle": "2023-12-09T20:58:39.009719Z",
-     "shell.execute_reply": "2023-12-09T20:58:39.009463Z"
+     "iopub.execute_input": "2023-12-22T05:36:02.149131Z",
+     "iopub.status.busy": "2023-12-22T05:36:02.149011Z",
+     "iopub.status.idle": "2023-12-22T05:36:02.151865Z",
+     "shell.execute_reply": "2023-12-22T05:36:02.151482Z"
     },
     "papermill": {
-     "duration": 0.006517,
-     "end_time": "2023-12-09T20:58:39.010217",
+     "duration": 0.008287,
+     "end_time": "2023-12-22T05:36:02.152515",
      "exception": false,
-     "start_time": "2023-12-09T20:58:39.003700",
+     "start_time": "2023-12-22T05:36:02.144228",
      "status": "completed"
     },
     "tags": []
@@ -3226,20 +3226,20 @@
     {
      "data": {
       "text/plain": [
-       "[[(-0.1194971223729009, -0.7280508837952592),\n",
-       "  (-1.319537507471114, 1.5212848761778315),\n",
-       "  (1.2788602731186134, -0.17566107112197066)],\n",
-       " [(1.6028263419892814, -0.9158633580550423),\n",
-       "  (-0.3376862832998488, -0.5888638671183992),\n",
-       "  (-1.3195375074711138, 1.6063249232993064),\n",
-       "  (1.1168772386832795, 0.8725636211286387)],\n",
-       " [(-0.1194971223729009, 0.7467032925908027),\n",
-       "  (-1.319537507471114, -1.5248619038487723),\n",
-       "  (1.2788602731186134, 0.1506633143211067)],\n",
-       " [(-1.0451027594444489, 0.4373588611594619),\n",
-       "  (1.2788602731186134, -0.27649738854902034),\n",
-       "  (-1.3195375074711138, 2.633045295435591),\n",
-       "  (-0.21097537171512248, -0.5465758714506043)]]"
+       "[[(1.7498062615988024, -0.45257199662610814),\n",
+       "  (-0.4042957384725226, -0.44337527535963617),\n",
+       "  (-0.536919046181291, 1.7826978227050168)],\n",
+       " [(-0.42718993699028085, 0.527446348695999),\n",
+       "  (1.762410152328975, 0.2654491966054017),\n",
+       "  (-0.4819186218405949, -1.9058198610436918),\n",
+       "  (1.7119945894082835, -1.9356905397331179)],\n",
+       " [(-0.536919046181291, -1.7047806736158473),\n",
+       "  (-0.4042957384725226, 0.4225336826027406),\n",
+       "  (1.7498062615988024, 0.4371796258076725)],\n",
+       " [(2.001530918868655, 0.09287838091877254),\n",
+       "  (0.33455027736619464, -1.2955747033245748),\n",
+       "  (-0.5861547516516311, 1.156781868561776),\n",
+       "  (-0.693402605587896, -0.12357547291735019)]]"
       ]
      },
      "execution_count": 26,
@@ -3256,10 +3256,10 @@
    "id": "f0965157",
    "metadata": {
     "papermill": {
-     "duration": 0.003547,
-     "end_time": "2023-12-09T20:58:39.017221",
+     "duration": 0.004986,
+     "end_time": "2023-12-22T05:36:02.161994",
      "exception": false,
-     "start_time": "2023-12-09T20:58:39.013674",
+     "start_time": "2023-12-22T05:36:02.157008",
      "status": "completed"
     },
     "tags": []
@@ -3276,16 +3276,16 @@
    "id": "27ce46ff",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:39.024472Z",
-     "iopub.status.busy": "2023-12-09T20:58:39.024358Z",
-     "iopub.status.idle": "2023-12-09T20:58:39.369768Z",
-     "shell.execute_reply": "2023-12-09T20:58:39.369448Z"
+     "iopub.execute_input": "2023-12-22T05:36:02.172069Z",
+     "iopub.status.busy": "2023-12-22T05:36:02.171930Z",
+     "iopub.status.idle": "2023-12-22T05:36:02.605227Z",
+     "shell.execute_reply": "2023-12-22T05:36:02.604118Z"
     },
     "papermill": {
-     "duration": 0.349652,
-     "end_time": "2023-12-09T20:58:39.370324",
+     "duration": 0.439248,
+     "end_time": "2023-12-22T05:36:02.606259",
      "exception": false,
-     "start_time": "2023-12-09T20:58:39.020672",
+     "start_time": "2023-12-22T05:36:02.167011",
      "status": "completed"
     },
     "scrolled": false,
@@ -3294,7 +3294,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3306,7 +3306,7 @@
     },
     {
      "data": {
-      "image/png": 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IjCC9tkGZWerUGg48Y2a9Zb8uy/3dB1xmZuXu3hSmnUdwH1VvA9I2s/No6lOAPwDfJPsu7iIiMoL01UniXqCOYFijHxLcULs2LU8HsMLdn8hyfzcDlwJ3mtm1wFzgSuCG1K7nZrYaWOjunw6nmX8sdSMpnSRecfdns9y3iIiMIL0GKHd/HngewMyagH+6e93u7Mzd683sRODHwD0EPfpuJAhS6eXSBIgiIqNYtvNB/XqwdhhODX/CLvLM3sXytfQcsFZERPYw2U63UQh8keDm3BlAcXoed588uEUTEZHRLNsbdW8EPgv8A3iUoO1JREQkZ7INUB8E/q+7X5/LwoiIiHTL9j4oA5bmsiAiIiKpsg1Q/wt8KJcFERERSZVtFd9W4CNm9ijwEDsP+Oru/rPBLJiIiIxu2Qaom8LnWWSet8kBBSgRERk02d4H1Z8x+0RERHabAo+IiOSlrCcsDKe5+CxwDDAB2A48Adzi7g25KJyIiIxe2U5YuA/wCnA1wfQX68Pnq4Gl4XIREZFB05+RJBqAI919U3eimU0nGPX8BuCMQS+diIiMWv2ZUfc7qcEJIHx/NTvP1yQiIrJbsg1QTu/TX0TIMNuuiIjI7sg2QD0KfNfM9k5NDN9fDTwy2AUTEZHRLds2qC8RBKHXzewFgpElJgOHARuAL+emeCIiMlpldQXl7muA+QTTtb8GFALLgEuA/cMJBEVERAbNLq+gzKwYuBv4nrvfDNyc81KJiMiot8srKHdvBw6n904SIiIigy7bThJ3A2fmsBwiIiI9ZNtJ4gHgOjObSnBj7lbSupa7+72DXDYRERnFsg1Qvw2fzw4f6fq6T0pERKTfsg1Qc3JaChERkTS9BigzexD4gruvdPd1YdoJwLPu3jJUBRQRkdGpr04SJwHjut+YWZRguvf9cl0oERGR/k5YaDkphYiISBrNqCsiInlpVwEq0yjlGrlcRERyble9+B4ws660tEcypOHukwevWCIiMtr1FaCuGrJSiIiIpOk1QLm7ApSIiAybIe8kYWYHmNkjZtZqZtVmdnXYhb2vdQ43s9vMbHW43kozuyIcaV1ERPZA2Y4kMSjMrBJ4mGAuqTOAfYDrCQLl5X2sel6Y91rgdeAQ4Lvh8zk5LLKIiAyTIQ1QwMVACXC2u+8AHjKzCuBKM/t+mJbJf7t7Xcr7x8ysHfi5me3dPdKFiIjsOYa6iu9U4IG0QHQHQdA6vreV0oJTtxfD52mDVzwREckXQx2g5gMrUhPcfT3QGi7rj6OABPDG4BRNRETyyVAHqEqgIUN6fbgsK2Y2haDN6jfuXtNLnovMbLGZLa6trR1IWUVEZBiNuKGOzKwI+BPQDHypt3zufou7L3D3BZMmTRqy8omIyOAY6k4S9aSMkJ6iMlzWJzMz4HbgQOBod9/lOiIiMjINdYBaQVpbk5nNBMaS1jbVi5sIuqef7O7Z5BcRkRFqqKv47gNOMbPylLTzgDZgYV8rmtk3gEuAj7r7k7krooiI5IOhDlA3AzHgTjM7ycwuAq4Ebkjteh6OGPGLlPcfBr5HUL23ycyOTHmogUlEZA80pFV87l5vZicCPwbuIejRdyNBkEovV+rwR+8Lny8IH6k+CfxqUAsqIiLDbqjboHD3ZcAJu8gzO+39BewcmEREZA824rqZi4jI6KAAJSIieUkBSkRE8pIClIiI5CUFKBERyUsKUCIikpcUoEREJC8pQImISF5SgBIRkbykACUiInlJAUpERPKSApSIiOQlBSgREclLClAiIpKXFKBERCQvKUCJiEheUoASEZG8pAAlIiJ5SQFKRETykgKUiIjkJQUoERHJSwpQIiKSlxSgREQkLylAiYhIXioY7gKIyOixo6Od9ngnAMXRQiqKioe5RJLPFKD6YUd7J+2diYzLigsjVBQXDnGJREaW9ngnU++4CoDN519BBQpQ0jsFqH7oSjg1LbGMy6ZV6B9NZE+nK8ChpQDVDx1dCQ75wUIAygqhrMBoi0Nrl7P2WycNc+nySzwRpyORoKSg51Vla1cHBRahKDr4H72OeNdO2+3q6qCgoKhHmsc7saiudodC6hf6uEQXFZ0xNpz2eQDK4p3UtDUl8/b1hZ+I7cDj7cn3Fi0mMqYihyXPTFeAQ0sBaoBOnVHE5w9668O56qXneCMaZdq0acyZM4f29nZWrVpFNBqloKCAaDRKNBqluLiYadOmAVBXV4eZ9VgejUYpLCzEzIbr0HZbPBFnXUsDH3nsd/z1xAuYNjb4Iqlta+acf/2Knxx1DvuNmzSoQWprWxOrd9TxjgnTGVsYBKRY23Y6Nj9N8dSjKCyZAECis4WO2qUUVr6NaEnVoO1fMkv9Qt9w2udp+No+JArH0jHraGKf+CH7/esXxHGg7y98j7ez/pYZyfezLtoIDH2AkqGlANUPUYNZlcVEOjv49PwxADR1JCiKRhgTBTNjy5YtbNiwgYqKCpqbm0kkEkQiEdwdd6ewsJDt27ezfft23D3jfg444ACam5vZsGEDBQXBKerq6qK8vJx4PE5XVxcLFixgw4YNbN68menTp9Pe3k59fT1z584lFotRU1PDwQcfTF1dHZs3b+aQQw6hvr6eTZs2cfDBBydfH3TQQdTX11NdXc2BBx5IQ0ND8nV9fT1btmxh//33p6GhgS1btjB//nwaGhrYunUr++23H42NjWzdupV58+YlXxdOreJzj/ye48ZM4KR//pS7j/0YrfUNXLjyQRLtMX696CE+fuSJTIsU01jfwOzZs2lubmbbtm3svffeNDc3s337dmbNmkVLSwvbt29n5syZtLS0UF9f3+P1jBkz2LC9lh88dz+3163kb8d+lDnRUqZMLKVm6e9pfGMhVVV3Mvbw79La1klF1+tUP3AJ8b1OZN+Tr6DTymhqamLSpEm0t7fT3NxMVVUVsViMlpYWJkyY0ON1R0cHra2tjB8/no6ODtra2hg3bhydnZ3J1x0dHbS3t1NRUUFnZyft7e2Ul5fT2dlJLBajrKyMrq4uYrEYpaWldHV10dnZSUlJCfF4nM7OToqLi5PnesyYMSQSCbq6uigqKiKRSBCPxyksLMTdicfjFBQUJD9jkUjQOdfdd/qhkw8/fLomzqPp+G/RtHYr/5xzKms7mri+dikATU1NlJSUJD/3Mrqpm3l/mPHMF45lyrixPLW1k1WNcaZMrqJyfAVjiospKyujqKiIoqIiurq6kldGkUgk+aXR2dnJtm3beg1OAMuWLWP9+vW4O52dnXR2duLu7Nixg5aWFmKxGIsWLWLDhg10dnaydu1atmzZQiwWY/ny5axfv55YLMZrr73G66+/TnNzM4sWLWL58uXs2LGDF154gbq6OmKxGC+99BJr1qyhqamJ5cuXs2HDBpqbm2lsbKSlpYWWlhZqampobGykubmZ9vZ2YrEYzc3NuDsdHR3s2LEDdycWi9HY2EhRJMIX57yTD4yfTXNXjGuev491WzZz7MTp/OGIszmpbDpFkShtzY1srq7G3Wlubmbjxo0k4p3sqK9NHv+OHTtYt25d8vXatWtJJBI0NjayZs0aGtpb+cfKFzm3dDZrz/wqT61Zxvo1a2lvrmHH5lXUz7yUgpKpbH7hd7z+xlq2/uNDtJW9nZrK/6DhpZ+zraaaFStWkEgk2L59O8uXLyeRSLBt2zZee+21nV7X1dXxyiuvJF8vXbqURCJBbW0tL7/8MvF4nNraWl566SXi8Tg1NTW8+OKLxJobqKmp4YUXXiAej7Nx/RqWLFlCPB5n69atPP/888TjcTZvXMtzzz1HPB5ny5YtPPvss3S21lO9aSPPPPNMkGfzZp5++mni8TjV1dUsWrQo+frJJ58kHo+zadMmnnjiieTrxx9/PNjvxo0sXLiQrq4uNm7cyOOPP05XV1cyf/frJ598kq6uLqqrq3nqqaeSrxctWkRXVxebN2/mmWeeSb5+9tln6erqYsuWLTz33HPJ12+8soyxVsBJZdNZt2YL7TOPIjbzKKL1a5g8pZLFrbVMKyil0+M01m3nhRde4KmnnuLNN9/kpReXsHHNSlavfIWXX32deKSY1nHvpmbuNXQ011Kz6Q1eeXkJna31yXMUj8fZtm0by5YtIx6PJ89p9+uVK1cSj8epr69n1apVydevv/468XichoYGVq9enXy9fNVKtrQ0srZmM6+uWkFtWxNvKxpHZXQM7fFO2nfU0NGwmdi29XQ21ZHoaMvqqyTR1Ua8rY54aw3xtm0kOpp2vdIoNOQ/U8zsAOBHwFFAA3ArcJW7x3ex3jjgJuBMgsD6D+BSd9+Ww+Im1TbHOPO25ykbU8Cdnz6KD96+mN+dehhXPbiSJ9fU8+jn3s20cbuuj+7+lZtIJLJ67CpvPB7v8Ugkgl6GZkZnZyeFhYXJ7XQ/2traaGvb+R9p+/btydevvPJK8vWqVauSrxcvXpx8/cwzzyS3uWjRIsyMSCTCxldWMKmrDdtRzR/3PYXlDbV0RhKcX7AX9S88yMx5RxOpq6ezbSOTWcfmTVMpHFPG/Pnz2F69kq6Vf+Dgd15CW1sblZWVHH744XR1dTF58mQmT56MmTFt2jSmTp1KJBLhw+88lq7tG9l+3Ylc+Jnb+c8Vz3HaNviP479G9S8vpvjki5nY+CwFr34Y69pB2bb7mTC+nMoTrsULypk4eRrRaJS99tqLyspKotEokydPZvz48cnX48aNIxqNMmnSJCoqKpKvy8rKiEajVFVVUVpamnw9duxYIpEIFSVRJq+/j7ax2xi373EceOCBdLQ1wor7mBZ34m0HM2HChODKqK0Wf+1a5h38NSKRCJWVlew7Zzr1C/+T8nd8g7ftuw+RSITx48ez7777EolEGDduHPvss0/y9dy5c4P9VlQwZ86c5OvZs2cTiUQoLy9n1qxZydczZswgEokwduxYpk6dSiKRoKSkhClTpiSvzKqqqjAzCgsLGT9+PK2trSQSCcrKygAoLCykpKSExsZGotEoZWVl1NfX09bWRlEkwvLjPklXPE5jQyNNCy7Co2MoaFzHjBLjggPfydZtDbyy4AvUNLdRWlpKVVVV8LmNd/Hmm6vxgmA/zQf+nvLav5EorKT6jqNoG3c0TZM/iM+dTmcntLa2Jn/Udf+AisViPX5A1dfXJ/8Htm3bxty5c2lra6OmpoY5c+bQ0tLCli1bmD17Ni0tLdRureGIRbfx/vKZfHbiAZy96FecOW4On5g0H5YvZNPfryC2IfhfiRSXM/74C6n6929RUDax1++AeFsdjYuvp+nVW0nEGiFaROnbzmXCu68mUjqFSLSo13VHG+vrl/yg78ysEngNWAZcC+wDXA/c6O6X72LdB4B5wFeBRLj+Vnc/dlf7XbBggad+sfZXU3sXZ9z2HI+9EcTCk+dN4q5PHs4ld77Cbc9vAGDepFIW/+dxlI3J/6qJvoJed7WSmeHutLa20tnZuVMgLCkpIRKJ0NLSQltb21tBMJEgkejCmrYQ6WghUT6FRFF5To7DzIhGo5gniDfXQUcr5nEK95rLxL2qiP7mAtreeBYvGU/hJ++guXU741/+BCVzP8Ckk28lMmY8ZpYM5qlBvLstMBaL0dnZSUFBAcXFxcRiMdrb23vkraysxN2TV8YTJkygoKCATetWs/2p39Kx7kWIFLLXez7OlENP4M1Hf0vj8kUUNK5n6kFHUXDs52loqKd55Z+JdzQRKZnEQe/5NAkKWPbUHXS11jBh253MOvcuVm1qJ5F4a9+TJk1i2rRpbNiwgdraWsaPH8/cuXPZvHkzGzZs6FHOI444gkQiwbPPPgvAO97xDkpLS3niiSeSP2wAZsyYwT777MPrr79OdXU1VVVVHHjggaxfv541a9b0OAfHHHMM8Xicp59+GoAFCxZk3GbJK3dQ9vzNNB1zGSTilC+6ocd25v1oKwUVk3ukxVtrWHvrvnQWz6GjZB86x+5DxebfUtDV8zfprIs2Eh3bc93BUtPWlGxD63bD/u/hpNYm7NcfJlE8jkh7Y4/lxbPfyazLHqKgbMJO24u31lD9x+Ppanxjp2VWVM6085+iaML8wT2IHDOzJe6+IBfbHupv04uBEuBsd98BPGRmFcCVZvb9MG0nZnYU8D7geHd/PEzbBDxrZie5+8O5LHTCE1x5yn6ccsszxLoSPLSqlklXPEBrRzwsH3zn5HnEuhKUjcllSQam+0dIahtFJBIhHg/KP2bMGCKRCG1tbUQikWR7RlFREVVVVbS1tRGLxSgoKKCsrIy2tjZaWlpwdyoqKpJflACbN66nY+tqWu64hEjrdloPPJe2t38MjxRSuO4JrLOd2H6nMaVqAs1v3EWk9U08Wkpi4pHEEmNJWASsECKFvG3efCgoTVa5pItGo+ErI1EyAcKOEPFYnE3rtlC49wfoPPoqiI6BdgObSsfc71FYOoFVzwW/eg877DDGjh3L008/3aPadfLkycyePZuNGzf2+JKuqanhzTff7FGOo48+Gndn2bJlyW2WlZXx5vrNJKa/F6a/NyjX4j8RX/gj6ssPpP2wT1O0diEUQktLG5trGvDyd2PeBZ5g850fwIon0jL+k9jYCSRq48Tb64nFIriTDKzdZY5EIhQWFib/JoWFhZSVlSXzdbc9RSIRpkyZkuycAzBr1qxke5WZUVERdD6YNGkSpaWllJSUADBx4kSKi4t32p6Zccghh2BmFBcHtQhvf/vbMTMSLfWs/a9jiMSCL/HS536GdbZm/dmNJNoZ07qcMa3LYUjqSnoqjhay+fwrku+3xVqo7Gyn/hv703LAObQe+lEq7/kc0eYtyTzta19gx/N/ofL4C7HIW60oic4W6p+5JmNwAvCOJmrv/wRTzrqXaEnvV2CjyVBfQT0OVLv7+Slps4B1wL+7+z29rHc1cJG7T0lLfxP4m7t/pa/97u4VFEBjWwcvb25KBqnjpxRw3LQCzODImZXsVT6Gqgnjg7yNjZSVlTFu3Ljkl3v3w90ZO3Ys5eXlyTal4uJi5s+fT3V1NVu2bKGoqCh5JTNjxgzcPflrePr06YwZMybZ/pRIJDAzqqqqGD9+PFu3bqW+vp6ysjJmzpzJtm3b2LJlS49jOeqoo4jFYrzwwgsA7LvvvpSUlCTbWbrttdde7LXXXtTV1VFdXZ2sNqqtraW6urrHNt/97nfT0dGRrAKcUdJC81++RsP7fgCFJcl8Y2tepHC/4yguKqJmay2e6KSs7l4iiTZaJpwEHicSKaCgfDrvPOxwAF5++WXMjPnz51NSUpJsA+oOtIUlxRRZB3VP/ZWO4iri42bgkQLAoLCEwgh4Vwve1YJ1NeHRYhJFkxlbWs4hh74DIHkFkKqgoIBDDjmE9vZ23nzzTTo7O5NfyJFIhGg0yt57701VVRU1NTVs376daDTKmDFjklWrY6IJdtz7X7S8+A+8oBhLdAVlc2f8iZ9j4rEfp6xyLxLxLlq3vcHWu88i0V6PkSDS1YDhWLSYKec+TFHVwURS/pYjQbxtB94ZdA+PN9XxxjcPTC6b+71XKSgPfthYYTHRkp698lK7lsfbtrHpN4cml03/2MvJL/Gh7HLeEGul8anf0PLri+mqmE77/H+n9LmbiVfMINK2HYt3EC+fSnHFBGZf9gDxonKKioowM9p2bGXr7w6FziYSBeOweDMR79hpHzMuWE7h+H2G5HgGw550BTUf+FdqgruvN7PWcFnGABUuW5EhfXm4LOfGlRTx9mkVHDZjHIvW1nPijEKOnRreSxNvYUdDCzsa3mrD2bZtGzNmzGDjxo0UFBQQj8eTv3YbGxvZvHlzMm9TUxPz5s2jqamJlpaWZJVTLBbr0f4DJH+9T548mdbWVpqbm5k0aRLxeDz5Cx5gx44dvPbaa8n33T3futuZGhoakstWr1690/HOnz+flpYWli5dyjveEXyJV1dX8/LLL/f6N+redvnbZrK+rpbI6T9JLhu7+FbGvvoH5vxkG8c99EuuO+LfOHjCfqx/9WGaqk6nqPV1pr/2ESw6hlmfrSaSUi142GGH9djPIYcc8tZxdrRzxYsPsKGlnpvfezY13zqIpqMvo32/0yla9yQzp02ivLKd9s1PMXbO6dQ9/HnwIAiPf9flFHAAVljGkUcemfxR0F2NCVBeXk55eXmyai+9qrOiogIzo6mpibq6uh4BHmDevHnM/thNvLqjmYZ3XtRjWQPQtrWe+ZV70dHZxZLlW+BtP0sun770bKLxJiaedDNjJh2KFeTh5fkuREsqoCRz8Cgon7RTtV6qIOhkXjdaMjFn1Xp9iViE6NbXASjYsYmy535Gx5RDaTzlOsof/38U1L9J/dm/puLx79Ee6+DFl55j//33p7S0lMUvr2Ji6aEUta9h8/6/YOKaayhtWLjTPjob3hhRASqXhvoKqhO4zN1vSkvfCNzu7t/sZb2HgBZ3PzMt/bfAXHd/d4Z1LgK6vxH2A1bu9gHsrAqoy8F2h9OeeEyg4xpJ9sRjgj33uPZz95w0NOd/i/4AufstwC253IeZLc7Vpe1w2ROPCXRcI8meeEywZx9XrrY91PdB1QPjMqRXhssGez0RERmhhjpArSCtzcjMZgJjydzG1Ot6od7apkREZIQb6gB1H3CKmaXWV54HtAE7txb2XG+KmR3TnWBmC4C54bLhktMqxGGyJx4T6LhGkj3xmEDH1W/DcaPuMuBVghtt5wI3ADel3qhrZquBhe7+6ZS0B4C30fNG3ZpsbtQVEZGRZ0ivoNy9HjgRiBJ0Kb8KuBG4Ii1rQZgn1XkEV1m/BG4HlgBn5bK8IiIyfIb0CkpERCRbo340czPb18x+bmZLzSxuZo9lud44M7vNzOrNrNHMfmdmO41PYmZnmNkrZtZuZsvM7LxBP4id99nvYzKzw8PjWW1mrWa20syuMLPitHxXmplneLw/Zwf01r4HclyzeynvHRnyDvm5Cvc7kOPq7Ty4mX0jJd+vesmT0xvczeyDZna3mW0ys2YzW2JmH8pivTFmdr2Z1ZhZi5n908xmZ8h3tJk9G56rNWZ2aU4OZOf99vu4zGw/M/uJmS0P/7feNLP/MbPxafku6OVcXZxvxxSul6msz2TIN+BztcfeB9UPBwKnAc8A/Zlm9U8Eg9deyFttYncByTYxCzp1/BX4KXBpuJ8/mFm9uz84GIXvxUCO6TyCwXuvBV4HDgG+Gz6fk5a3EUgPSMsHWth+GOi5gqDt8qmU9z1umBzGcwUDO65bgfvT0s4Evs7OHYdWAJ9MS1vbrxL235eBNcCXCP7WpwG/N7Mqd/9RH+v9EDg3XK8WuJJgzM6D3b0dgoAOPEAwo8E3gCOAG8ys1d1vzdHxdBvIcZ0MHA38DFhK0PZ+DXCUmR3p7om0/CcQdBzr9ia5NdBzBcFg339Jed9j3pDdPlepox2PxgcQSXn9F+CxLNY5CnDguJS0I8K0k1LSHgD+lbbuvcCTeXhMVRnSLgqPae+UtCuBuhF0rmaHx/CBXeQblnM10OPqZTv/BJanpf0KWDwM5yrT5+n3wJo+1pkBdAEfT0mbDnQAF6ak/RxYBRSkpP0U2EDYbJFnxzUxvVwEg187wQDY3WkXhGll+X6uwjwOXLKLPLt1rkZ9FZ/v/OslG6cSTPXxeMp2niP4FXIqBFUVwHsJrrRS3UHwyynTjceDYiDH5O6ZhmB5MXyetnslGhwDPFe7NJznCgbnuCyoXj4Z+MPul2j39fF56uuz9L7w+c6U7WwCniT8vwqdCtzp7l0paXcQBLiDBlTgLA3kuNx9m4ffzGnr0Nd6Q2WA5ypbu3WuRn2AGqBsBq/dh6C6Jj3fcoK/+7yclW7wHEVQfZk+P8B4M6szs04ze9HMzh6GsvXXbWH7zmYzu8HMUocF3xPO1TkEx5ApQB1gZjvMLGZmT5rZ8UNctm5HEfya7s18YKO7N6elJ/+vzKwUmEnmc9W9jaG2q+PqbR16We8NM+uyoB34s7tXtAHL9piuDMtaZ2a/NLPkJFiDca7UBjUwlQSDUaerJ6hf7s5Dhnz1acvzkplNAS4HfuPuNSmLVgNfI/iFVQ58FvirmZ3j7nfuvKVhFwN+AjwI7ADeQ9BOsw9wRphnRJ+r0PnAC+7+elr6i8CzBPcfTgK+QtCmc0x41T8kzOxEgjayT/WRra//q+5zMD58Ts83LOcqy+NKX2csQVvvQndfkrJoM/Bt4DmC22zOB242s7HufuOgFXrX5cv2mH5NcLtQLbCAoOyHmtkRHsyQPj7M15C2XtbnSgFKdmJmRQTVXc0EDadJ7v7btLz3AIuA75BSNZMv3H0zcElK0mNmthX4qZkd6u69zx8yQpjZVOB4gsDbg7v/T1reewlmtf4mwZfQUJRvNkGbxt/d/VdDsc+hMJDjMjMDfgFMBk5PXebuDxC0hXa7z4JetJeb2f/kqoo7rXyzyfKY3P2ClLePm9lygnbbfyPoMLbbVMU3MNkMXtv9nJ6vMm15Xgn/gW4n7Fnmwc3VvQrr1u8EDjGz9Jur81V3r6PuiaZG5LlK8R+AAX/cVUZ3byX4EnlnrgsFEFb53EcwKelHdpE9m/+rhvB5WM9VP48r1bUEAwyc6e7Z9M77CzCBoLNPTu3GMXW7n+BHbfdnqyF8HvC5UoAamGwGr30D6MyQbz5Bu05/66yHyk0EVV9nuHu2A/F6+BgpPO15pJ6rbucT9DbckGX+ITlfYVXWP4Aigl6Uu5rrfQUwM2y7SJX8v3L3FoIeYJnOVfc2cmoAx9W93pcIbnf4uLs/keXu0j+rOTHQY0qV0hHEw/e7fa4UoAZml4PXunsMeBT4YNq65wFPu3vjEJU1axbc4HkJ8FF3fzLLdYyggf7lsN55JDg3fF4CI/NcdQurZI4ky957YeeQ0wmPPVfMrAD4M8H4me9Pa8fsTff9ZskhzMxsGsG9han3dt0HnJV2xX4ewZfhq7tT7l0Z4HFhZh8huGfoy+6e3lu0L+cS3Ju0rr9lzdZAjynDdt4PlNHzs7Vb52rUt0GFvxxOC99OByrMrPsL7F53b7W0wWvd/WkzexC43cxSB6990t0fTtn8dwnaPG4iqJM9LXzkdNSFgRyTmX0Y+B7BfTObzOzIlE2+4e61Yb6FBDe0rgBKgc8A72II2jMGeFxXEnTmeIqgk8RxwGUEXV+Xpmx+WM5VWMZ+H1eK8wnuHfpzhu2OI/hV/FuCzi1VBG2K09g5GA+2nxIc0xeBidZzlJUX3T1mZo8AuPuJ4fNGM/sFcFP4w6f7Rt114TF0u46gCuo3Zva/wOEEnXU+l6E792Dr93GFvSZvIwjAz6T9b210941hvr8SdJBYStBJ4rzwcWmO258GckwXEXSMeJgggL6ToFPVcwT343XbvXOV7c1ce+qDt27kzPSYHeZZC/wqbb3xBB+6BoIvvt+T+Ya3Mwl+KcQIvtTPz8djIghMva1zQUq+XxDc2d4GtABPAKfm67ki+AJfTDD6RQfBF/XVwJh8OFe78xkM018C7u9lu8UE7YMbwmNqJGgnOHIIjmltFsf0GGk3JQNjCGY4qA0/X/cCczJs/xiCL8P2cF+XDtG56vdxEQTZ3ta5MiXf94CVQGv4/7UE+FieHtOJBD/6thFUj28gGAVk3GCeKw0WKyIieUltUCIikpcUoEREJC8pQImISF5SgBIRkbykACUiInlJAUpERPKSApRIBhZMv73EzJrMrD6cVuSG4S5XOjP7gZmtHe5yiOSCApRImnDIp1sJRpY+G/g48Hfg34ezXCKjjW7UFUljZpuAu9z9/6Slm+fZP4yZ/QA4191nD3dZRAabrqBEdjYe2JKemBqczGy2mbmZfdjMfhNWBdaY2RXp65nZQWb2zzBPk5n9OZwQMjXPBDO7xcy2mlm7mS0ys3el5RlvZr83s2YLZgb+VoZ9XWlmO03hHZb1kpT3a8PqwW+b2ZZwm7+zHE9vL9IfClAiO3sB+IKZfSJt4MxMriMYO+1c4H+BK8wseeVlZvsSjFlWDHwUuIBgrq17wgFRMbMxBINunkQwkO2ZBGPRPZwWyG4DTiUY8PUi4H0EYw0O1IfCfX4G+DLBKOe37sb2RAbVqB/NXCSD/0MwovmvAA9nCv0r8AN335GW9zV3/2z4+gEzmwx808x+5sEI1FcQXI2d6u4dAGa2lGAw2tMIRn7+KHAQcKCHU7ab2cMEA4d+BbjMzA4kCFznu/sfwzyPAusJBiseiBLgdHdvDrfXQjDq9P7uvnyA2xQZNLqCEknjwTQc+xN0ivgpwWy13wYWm1lZWva/pb2/k2A6ixnh+5PCPAkzKwjn3llDMKrzgpQ8S4A1KXkAFqbkOTx8/ntKOZuBhwZ4mAAPdQenlGOxlH2JDCsFKJEM3D3m7ve4+yXufgBwIcGEbunzMaVP7tb9fmr4XAV8nWBKgtTHXGBmSp4jM+T5ZEqeKUCTu7fvYv/90WNdD2ZRbU4pu8iwUhWfSBbc/Rdm9n12nr56ci/vN4fP2wmuTDK17dSl5FkMfC5Dnlj4vAUoN7PitCCVvv92gmm7k8ysMsN2d1o3nDixLKXsIsNKAUokjZlN9rRpr81sEjAO2JqW/SzgZynvzyb4gt8Yvn+EoFPEkj66qD9C0OFhffp+UzwfPp8BdLdBlQEn07MNaiNBIJvu7pvCtPf1ss2TzawspZrvLIJJ6hb3kl9kSClAiezsFTP7O8EU3TXA3sBXCXrr/Tot74Fm9nOCThTHEVQBftHfmqL7SsJpsM3slwRXTdMJAsuv3P0x4HbgYoIp539AMGPxROAIYIu73+jur5nZ3cDPzKyCIAheFpYp1f0Es7H+0syuB+aE286kLSzXdQTVetcBf3P3ZVn/pURySAFKZGdXE1yp/BCYQFC9tgg4z93XpOX9GvABggDVDnwX+HH3QndfZWZHAtcAtxD0nNtEcNW0OszTbmbvDfd7FbAXQWB8Drg7ZV8XEFyt3UTQVvQTgiurc1P2V2dm5wA/IOiJuAT4MJAp6NwBNAG/IKjau5vM1Ywiw0IjSYgMgJnNJuiN92/u/o9hLk6/heP3/cXdvzrcZRHpjXrxiYhIXlKAEhGRvKQqPhERyUu6ghIRkbykACUiInlJAUpERPKSApSIiOQlBSgREclL/x++Kk9Cw4n/uQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3318,7 +3318,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3330,7 +3330,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3375,8 +3375,8 @@
     "        fontvariant=ylab.get_fontvariant(),\n",
     "        fontweight=ylab.get_fontweight(),\n",
     "    )\n",
-    "    ax.set_xlim(0.9, 2.5)\n",
-    "    ax.set_ylim(0.0, 1.0)\n",
+    "    ax.set_xlim(0.99, 1.04)\n",
+    "    ax.set_ylim(-0.05, 1.0)\n",
     "    plot_lines_by_profile(df, mets[0], mets[1], \"name\", ax)\n",
     "    legend = ax.legend(loc=\"center left\", bbox_to_anchor=(1.25, 0.5), ncol=2)\n",
     "    legend.get_texts()[0].set_text(\"Optimization Level\")\n",
@@ -3413,14 +3413,14 @@
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 3.119832,
-   "end_time": "2023-12-09T20:58:39.691358",
+   "duration": 3.515267,
+   "end_time": "2023-12-22T05:36:03.024456",
    "environment_variables": {},
    "exception": null,
    "input_path": "02_thicket_rajaperf_clustering.ipynb",
    "output_path": "02_thicket_rajaperf_clustering.ipynb",
    "parameters": {},
-   "start_time": "2023-12-09T20:58:36.571526",
+   "start_time": "2023-12-22T05:35:59.509189",
    "version": "2.5.0"
   }
  },
diff --git a/docs/thicket_tutorial.ipynb b/docs/thicket_tutorial.ipynb
index 879e02bc..26dcf36f 100644
--- a/docs/thicket_tutorial.ipynb
+++ b/docs/thicket_tutorial.ipynb
@@ -2,13 +2,13 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c39ad05c",
+   "id": "dfbc35de",
    "metadata": {
     "papermill": {
-     "duration": 0.015835,
-     "end_time": "2023-12-09T20:58:33.820710",
+     "duration": 0.016627,
+     "end_time": "2023-12-22T05:35:56.501288",
      "exception": false,
-     "start_time": "2023-12-09T20:58:33.804875",
+     "start_time": "2023-12-22T05:35:56.484661",
      "status": "completed"
     },
     "tags": []
@@ -32,19 +32,19 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "9041ad44",
+   "id": "f37d0e45",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:33.838606Z",
-     "iopub.status.busy": "2023-12-09T20:58:33.838289Z",
-     "iopub.status.idle": "2023-12-09T20:58:34.355792Z",
-     "shell.execute_reply": "2023-12-09T20:58:34.355406Z"
+     "iopub.execute_input": "2023-12-22T05:35:56.519216Z",
+     "iopub.status.busy": "2023-12-22T05:35:56.518969Z",
+     "iopub.status.idle": "2023-12-22T05:35:57.063135Z",
+     "shell.execute_reply": "2023-12-22T05:35:57.062769Z"
     },
     "papermill": {
-     "duration": 0.526922,
-     "end_time": "2023-12-09T20:58:34.356500",
+     "duration": 0.552842,
+     "end_time": "2023-12-22T05:35:57.063917",
      "exception": false,
-     "start_time": "2023-12-09T20:58:33.829578",
+     "start_time": "2023-12-22T05:35:56.511075",
      "status": "completed"
     },
     "scrolled": true,
@@ -411,13 +411,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab6ac9e9",
+   "id": "8e30d55b",
    "metadata": {
     "papermill": {
-     "duration": 0.002868,
-     "end_time": "2023-12-09T20:58:34.362408",
+     "duration": 0.002869,
+     "end_time": "2023-12-22T05:35:57.069821",
      "exception": false,
-     "start_time": "2023-12-09T20:58:34.359540",
+     "start_time": "2023-12-22T05:35:57.066952",
      "status": "completed"
     },
     "tags": []
@@ -431,19 +431,19 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "fbc01567",
+   "id": "33c8b375",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:34.367949Z",
-     "iopub.status.busy": "2023-12-09T20:58:34.367828Z",
-     "iopub.status.idle": "2023-12-09T20:58:34.508474Z",
-     "shell.execute_reply": "2023-12-09T20:58:34.508185Z"
+     "iopub.execute_input": "2023-12-22T05:35:57.075731Z",
+     "iopub.status.busy": "2023-12-22T05:35:57.075597Z",
+     "iopub.status.idle": "2023-12-22T05:35:57.239852Z",
+     "shell.execute_reply": "2023-12-22T05:35:57.239465Z"
     },
     "papermill": {
-     "duration": 0.143973,
-     "end_time": "2023-12-09T20:58:34.509053",
+     "duration": 0.16797,
+     "end_time": "2023-12-22T05:35:57.240665",
      "exception": false,
-     "start_time": "2023-12-09T20:58:34.365080",
+     "start_time": "2023-12-22T05:35:57.072695",
      "status": "completed"
     },
     "tags": []
@@ -465,13 +465,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45126eb3",
+   "id": "1080f41d",
    "metadata": {
     "papermill": {
-     "duration": 0.002446,
-     "end_time": "2023-12-09T20:58:34.514114",
+     "duration": 0.002737,
+     "end_time": "2023-12-22T05:35:57.246604",
      "exception": false,
-     "start_time": "2023-12-09T20:58:34.511668",
+     "start_time": "2023-12-22T05:35:57.243867",
      "status": "completed"
     },
     "tags": []
@@ -486,19 +486,19 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "c34a165e",
+   "id": "e63042b7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:34.519124Z",
-     "iopub.status.busy": "2023-12-09T20:58:34.519026Z",
-     "iopub.status.idle": "2023-12-09T20:58:34.521071Z",
-     "shell.execute_reply": "2023-12-09T20:58:34.520815Z"
+     "iopub.execute_input": "2023-12-22T05:35:57.252016Z",
+     "iopub.status.busy": "2023-12-22T05:35:57.251912Z",
+     "iopub.status.idle": "2023-12-22T05:35:57.254017Z",
+     "shell.execute_reply": "2023-12-22T05:35:57.253765Z"
     },
     "papermill": {
-     "duration": 0.005081,
-     "end_time": "2023-12-09T20:58:34.521545",
+     "duration": 0.005476,
+     "end_time": "2023-12-22T05:35:57.254564",
      "exception": false,
-     "start_time": "2023-12-09T20:58:34.516464",
+     "start_time": "2023-12-22T05:35:57.249088",
      "status": "completed"
     },
     "tags": []
@@ -531,13 +531,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e0ba182",
+   "id": "c951f368",
    "metadata": {
     "papermill": {
-     "duration": 0.002478,
-     "end_time": "2023-12-09T20:58:34.526501",
+     "duration": 0.002511,
+     "end_time": "2023-12-22T05:35:57.259681",
      "exception": false,
-     "start_time": "2023-12-09T20:58:34.524023",
+     "start_time": "2023-12-22T05:35:57.257170",
      "status": "completed"
     },
     "tags": []
@@ -556,19 +556,19 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "86e55dcc",
+   "id": "ba98be9d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:34.531658Z",
-     "iopub.status.busy": "2023-12-09T20:58:34.531566Z",
-     "iopub.status.idle": "2023-12-09T20:58:34.566795Z",
-     "shell.execute_reply": "2023-12-09T20:58:34.566533Z"
+     "iopub.execute_input": "2023-12-22T05:35:57.265182Z",
+     "iopub.status.busy": "2023-12-22T05:35:57.265069Z",
+     "iopub.status.idle": "2023-12-22T05:35:57.304208Z",
+     "shell.execute_reply": "2023-12-22T05:35:57.303873Z"
     },
     "papermill": {
-     "duration": 0.038409,
-     "end_time": "2023-12-09T20:58:34.567357",
+     "duration": 0.042557,
+     "end_time": "2023-12-22T05:35:57.304812",
      "exception": false,
-     "start_time": "2023-12-09T20:58:34.528948",
+     "start_time": "2023-12-22T05:35:57.262255",
      "status": "completed"
     },
     "scrolled": true,
@@ -620,7 +620,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'RAJAPerf', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>1</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>1.780923</td>\n",
@@ -637,24 +637,7 @@
        "      <td>RAJAPerf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>1</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>6.766278</td>\n",
-       "      <td>6.766278</td>\n",
-       "      <td>6.766278</td>\n",
-       "      <td>6.766278</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.342898e+10</td>\n",
-       "      <td>3.576198e+10</td>\n",
-       "      <td>5.035623e+08</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>4437343.0</td>\n",
-       "      <td>2500.0</td>\n",
-       "      <td>RAJAPerf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>1</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>14.407950</td>\n",
@@ -671,7 +654,24 @@
        "      <td>RAJAPerf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>1</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>6.766278</td>\n",
+       "      <td>6.766278</td>\n",
+       "      <td>6.766278</td>\n",
+       "      <td>6.766278</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.342898e+10</td>\n",
+       "      <td>3.576198e+10</td>\n",
+       "      <td>5.035623e+08</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>4437343.0</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>RAJAPerf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>1</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>3.381119</td>\n",
@@ -689,7 +689,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Algorithm', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>10</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.006810</td>\n",
@@ -706,24 +706,7 @@
        "      <td>Algorithm</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>10</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.020310</td>\n",
-       "      <td>0.020310</td>\n",
-       "      <td>0.020310</td>\n",
-       "      <td>0.020310</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>6.710886e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>Algorithm</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>10</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.037690</td>\n",
@@ -740,7 +723,24 @@
        "      <td>Algorithm</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>10</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.020310</td>\n",
+       "      <td>0.020310</td>\n",
+       "      <td>0.020310</td>\n",
+       "      <td>0.020310</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>6.710886e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>Algorithm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>10</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.010733</td>\n",
@@ -758,7 +758,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Algorithm_MEMCPY', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>13</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.002439</td>\n",
@@ -775,41 +775,41 @@
        "      <td>Algorithm_MEMCPY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>13</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.008672</td>\n",
-       "      <td>0.008672</td>\n",
-       "      <td>0.008672</td>\n",
-       "      <td>0.008672</td>\n",
+       "      <td>0.016936</td>\n",
+       "      <td>0.016936</td>\n",
+       "      <td>0.016936</td>\n",
+       "      <td>0.016936</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>6.710886e+07</td>\n",
+       "      <td>1.342177e+08</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>4.194304e+06</td>\n",
+       "      <td>8.388608e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>100.0</td>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>13</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.016936</td>\n",
-       "      <td>0.016936</td>\n",
-       "      <td>0.016936</td>\n",
-       "      <td>0.016936</td>\n",
+       "      <td>0.008672</td>\n",
+       "      <td>0.008672</td>\n",
+       "      <td>0.008672</td>\n",
+       "      <td>0.008672</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>1.342177e+08</td>\n",
+       "      <td>6.710886e+07</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>8.388608e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>100.0</td>\n",
        "      <td>Algorithm_MEMCPY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>13</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.004507</td>\n",
@@ -827,7 +827,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Algorithm_MEMSET', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>12</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.001710</td>\n",
@@ -844,41 +844,41 @@
        "      <td>Algorithm_MEMSET</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>12</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.005782</td>\n",
-       "      <td>0.005782</td>\n",
-       "      <td>0.005782</td>\n",
-       "      <td>0.005782</td>\n",
+       "      <td>0.011187</td>\n",
+       "      <td>0.011187</td>\n",
+       "      <td>0.011187</td>\n",
+       "      <td>0.011187</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>3.355444e+07</td>\n",
+       "      <td>6.710887e+07</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>4.194304e+06</td>\n",
+       "      <td>8.388608e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>100.0</td>\n",
        "      <td>Algorithm_MEMSET</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>12</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.011187</td>\n",
-       "      <td>0.011187</td>\n",
-       "      <td>0.011187</td>\n",
-       "      <td>0.011187</td>\n",
+       "      <td>0.005782</td>\n",
+       "      <td>0.005782</td>\n",
+       "      <td>0.005782</td>\n",
+       "      <td>0.005782</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>6.710887e+07</td>\n",
+       "      <td>3.355444e+07</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>8.388608e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>100.0</td>\n",
        "      <td>Algorithm_MEMSET</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>12</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.002665</td>\n",
@@ -896,7 +896,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Algorithm_REDUCE_SUM', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>11</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.002638</td>\n",
@@ -913,24 +913,7 @@
        "      <td>Algorithm_REDUCE_SUM</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>11</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.005833</td>\n",
-       "      <td>0.005833</td>\n",
-       "      <td>0.005833</td>\n",
-       "      <td>0.005833</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>3.355444e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>Algorithm_REDUCE_SUM</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>11</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.009542</td>\n",
@@ -947,7 +930,24 @@
        "      <td>Algorithm_REDUCE_SUM</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>11</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.005833</td>\n",
+       "      <td>0.005833</td>\n",
+       "      <td>0.005833</td>\n",
+       "      <td>0.005833</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>3.355444e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>Algorithm_REDUCE_SUM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>11</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.003538</td>\n",
@@ -965,7 +965,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Apps', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>4</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.185395</td>\n",
@@ -982,24 +982,7 @@
        "      <td>Apps</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>4</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.562581</td>\n",
-       "      <td>0.562581</td>\n",
-       "      <td>0.562581</td>\n",
-       "      <td>0.562581</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>9.059697e+08</td>\n",
-       "      <td>3.194887e+08</td>\n",
-       "      <td>2.516582e+07</td>\n",
-       "      <td>156.0</td>\n",
-       "      <td>4437343.0</td>\n",
-       "      <td>700.0</td>\n",
-       "      <td>Apps</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>4</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>1.092784</td>\n",
@@ -1016,7 +999,24 @@
        "      <td>Apps</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>4</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.562581</td>\n",
+       "      <td>0.562581</td>\n",
+       "      <td>0.562581</td>\n",
+       "      <td>0.562581</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>9.059697e+08</td>\n",
+       "      <td>3.194887e+08</td>\n",
+       "      <td>2.516582e+07</td>\n",
+       "      <td>156.0</td>\n",
+       "      <td>4437343.0</td>\n",
+       "      <td>700.0</td>\n",
+       "      <td>Apps</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>4</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.308868</td>\n",
@@ -1034,7 +1034,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Apps_DEL_DOT_VEC_2D', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>59</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.006987</td>\n",
@@ -1051,24 +1051,7 @@
        "      <td>Apps_DEL_DOT_VEC_2D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>59</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.026393</td>\n",
-       "      <td>0.026393</td>\n",
-       "      <td>0.026393</td>\n",
-       "      <td>0.026393</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>2.014577e+08</td>\n",
-       "      <td>2.264924e+08</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>Apps_DEL_DOT_VEC_2D</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>59</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.052154</td>\n",
@@ -1085,7 +1068,24 @@
        "      <td>Apps_DEL_DOT_VEC_2D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>59</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.026393</td>\n",
+       "      <td>0.026393</td>\n",
+       "      <td>0.026393</td>\n",
+       "      <td>0.026393</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>2.014577e+08</td>\n",
+       "      <td>2.264924e+08</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>Apps_DEL_DOT_VEC_2D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>59</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.013385</td>\n",
@@ -1103,7 +1103,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Apps_ENERGY', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>60</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.039157</td>\n",
@@ -1120,24 +1120,7 @@
        "      <td>Apps_ENERGY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>60</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.147702</td>\n",
-       "      <td>0.147702</td>\n",
-       "      <td>0.147702</td>\n",
-       "      <td>0.147702</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>9.059697e+08</td>\n",
-       "      <td>2.306867e+08</td>\n",
-       "      <td>2.516582e+07</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>Apps_ENERGY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>60</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.291748</td>\n",
@@ -1154,7 +1137,24 @@
        "      <td>Apps_ENERGY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>60</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.147702</td>\n",
+       "      <td>0.147702</td>\n",
+       "      <td>0.147702</td>\n",
+       "      <td>0.147702</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>9.059697e+08</td>\n",
+       "      <td>2.306867e+08</td>\n",
+       "      <td>2.516582e+07</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>Apps_ENERGY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>60</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.075688</td>\n",
@@ -1172,7 +1172,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Apps_FIR', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>61</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.004270</td>\n",
@@ -1189,41 +1189,41 @@
        "      <td>Apps_FIR</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>61</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.014277</td>\n",
-       "      <td>0.014277</td>\n",
-       "      <td>0.014277</td>\n",
-       "      <td>0.014277</td>\n",
+       "      <td>0.027741</td>\n",
+       "      <td>0.027741</td>\n",
+       "      <td>0.027741</td>\n",
+       "      <td>0.027741</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>6.710874e+07</td>\n",
-       "      <td>1.342172e+08</td>\n",
-       "      <td>4.194288e+06</td>\n",
+       "      <td>1.342176e+08</td>\n",
+       "      <td>2.684349e+08</td>\n",
+       "      <td>8.388592e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>160.0</td>\n",
        "      <td>Apps_FIR</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>61</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.027741</td>\n",
-       "      <td>0.027741</td>\n",
-       "      <td>0.027741</td>\n",
-       "      <td>0.027741</td>\n",
+       "      <td>0.014277</td>\n",
+       "      <td>0.014277</td>\n",
+       "      <td>0.014277</td>\n",
+       "      <td>0.014277</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>1.342176e+08</td>\n",
-       "      <td>2.684349e+08</td>\n",
-       "      <td>8.388592e+06</td>\n",
+       "      <td>6.710874e+07</td>\n",
+       "      <td>1.342172e+08</td>\n",
+       "      <td>4.194288e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>160.0</td>\n",
        "      <td>Apps_FIR</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>61</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.007529</td>\n",
@@ -1241,7 +1241,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Apps_HALOEXCHANGE', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>62</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.035092</td>\n",
@@ -1258,41 +1258,41 @@
        "      <td>Apps_HALOEXCHANGE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>62</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.032822</td>\n",
-       "      <td>0.032822</td>\n",
-       "      <td>0.032822</td>\n",
-       "      <td>0.032822</td>\n",
+       "      <td>0.036228</td>\n",
+       "      <td>0.036228</td>\n",
+       "      <td>0.036228</td>\n",
+       "      <td>0.036228</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>1.889592e+07</td>\n",
+       "      <td>2.996376e+07</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>4.723980e+05</td>\n",
+       "      <td>7.490940e+05</td>\n",
        "      <td>156.0</td>\n",
-       "      <td>4173281.0</td>\n",
+       "      <td>8365427.0</td>\n",
        "      <td>50.0</td>\n",
        "      <td>Apps_HALOEXCHANGE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>62</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.036228</td>\n",
-       "      <td>0.036228</td>\n",
-       "      <td>0.036228</td>\n",
-       "      <td>0.036228</td>\n",
+       "      <td>0.032822</td>\n",
+       "      <td>0.032822</td>\n",
+       "      <td>0.032822</td>\n",
+       "      <td>0.032822</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>2.996376e+07</td>\n",
+       "      <td>1.889592e+07</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>7.490940e+05</td>\n",
+       "      <td>4.723980e+05</td>\n",
        "      <td>156.0</td>\n",
-       "      <td>8365427.0</td>\n",
+       "      <td>4173281.0</td>\n",
        "      <td>50.0</td>\n",
        "      <td>Apps_HALOEXCHANGE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>62</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.034307</td>\n",
@@ -1310,7 +1310,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Apps_HALOEXCHANGE_FUSED', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>63</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.005166</td>\n",
@@ -1327,41 +1327,41 @@
        "      <td>Apps_HALOEXCHANGE_FUSED</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>63</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.008232</td>\n",
-       "      <td>0.008232</td>\n",
-       "      <td>0.008232</td>\n",
-       "      <td>0.008232</td>\n",
+       "      <td>0.011687</td>\n",
+       "      <td>0.011687</td>\n",
+       "      <td>0.011687</td>\n",
+       "      <td>0.011687</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>1.889592e+07</td>\n",
+       "      <td>2.996376e+07</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>4.723980e+05</td>\n",
+       "      <td>7.490940e+05</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>4173281.0</td>\n",
+       "      <td>8365427.0</td>\n",
        "      <td>50.0</td>\n",
        "      <td>Apps_HALOEXCHANGE_FUSED</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>63</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.011687</td>\n",
-       "      <td>0.011687</td>\n",
-       "      <td>0.011687</td>\n",
-       "      <td>0.011687</td>\n",
+       "      <td>0.008232</td>\n",
+       "      <td>0.008232</td>\n",
+       "      <td>0.008232</td>\n",
+       "      <td>0.008232</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>2.996376e+07</td>\n",
+       "      <td>1.889592e+07</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>7.490940e+05</td>\n",
+       "      <td>4.723980e+05</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>8365427.0</td>\n",
+       "      <td>4173281.0</td>\n",
        "      <td>50.0</td>\n",
        "      <td>Apps_HALOEXCHANGE_FUSED</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>63</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.006447</td>\n",
@@ -1379,7 +1379,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Apps_LTIMES', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>64</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.014145</td>\n",
@@ -1396,24 +1396,7 @@
        "      <td>Apps_LTIMES</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>64</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.047071</td>\n",
-       "      <td>0.047071</td>\n",
-       "      <td>0.047071</td>\n",
-       "      <td>0.047071</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>5.978163e+07</td>\n",
-       "      <td>2.097152e+08</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>Apps_LTIMES</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>64</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.109502</td>\n",
@@ -1430,7 +1413,24 @@
        "      <td>Apps_LTIMES</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>64</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.047071</td>\n",
+       "      <td>0.047071</td>\n",
+       "      <td>0.047071</td>\n",
+       "      <td>0.047071</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>5.978163e+07</td>\n",
+       "      <td>2.097152e+08</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>Apps_LTIMES</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>64</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.023788</td>\n",
@@ -1448,7 +1448,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Apps_LTIMES_NOVIEW', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>5</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.014158</td>\n",
@@ -1465,24 +1465,7 @@
        "      <td>Apps_LTIMES_NOVIEW</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>5</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.047085</td>\n",
-       "      <td>0.047085</td>\n",
-       "      <td>0.047085</td>\n",
-       "      <td>0.047085</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>5.978163e+07</td>\n",
-       "      <td>2.097152e+08</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>Apps_LTIMES_NOVIEW</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>5</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.093582</td>\n",
@@ -1499,7 +1482,24 @@
        "      <td>Apps_LTIMES_NOVIEW</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>5</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.047085</td>\n",
+       "      <td>0.047085</td>\n",
+       "      <td>0.047085</td>\n",
+       "      <td>0.047085</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>5.978163e+07</td>\n",
+       "      <td>2.097152e+08</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>Apps_LTIMES_NOVIEW</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>5</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.023791</td>\n",
@@ -1517,7 +1517,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Apps_NODAL_ACCUMULATION_3D', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>6</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.007990</td>\n",
@@ -1534,24 +1534,7 @@
        "      <td>Apps_NODAL_ACCUMULATION_3D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>6</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.027399</td>\n",
-       "      <td>0.027399</td>\n",
-       "      <td>0.027399</td>\n",
-       "      <td>0.027399</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.347969e+08</td>\n",
-       "      <td>3.755953e+07</td>\n",
-       "      <td>4.173281e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4173281.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>Apps_NODAL_ACCUMULATION_3D</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>6</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.054349</td>\n",
@@ -1568,7 +1551,24 @@
        "      <td>Apps_NODAL_ACCUMULATION_3D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>6</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.027399</td>\n",
+       "      <td>0.027399</td>\n",
+       "      <td>0.027399</td>\n",
+       "      <td>0.027399</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.347969e+08</td>\n",
+       "      <td>3.755953e+07</td>\n",
+       "      <td>4.173281e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4173281.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>Apps_NODAL_ACCUMULATION_3D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>6</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.014248</td>\n",
@@ -1586,7 +1586,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Apps_PRESSURE', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>7</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.048417</td>\n",
@@ -1603,24 +1603,7 @@
        "      <td>Apps_PRESSURE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>7</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.176734</td>\n",
-       "      <td>0.176734</td>\n",
-       "      <td>0.176734</td>\n",
-       "      <td>0.176734</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.677722e+08</td>\n",
-       "      <td>1.258291e+07</td>\n",
-       "      <td>8.388608e+06</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>700.0</td>\n",
-       "      <td>Apps_PRESSURE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>7</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.347866</td>\n",
@@ -1637,7 +1620,24 @@
        "      <td>Apps_PRESSURE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>7</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.176734</td>\n",
+       "      <td>0.176734</td>\n",
+       "      <td>0.176734</td>\n",
+       "      <td>0.176734</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.677722e+08</td>\n",
+       "      <td>1.258291e+07</td>\n",
+       "      <td>8.388608e+06</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>700.0</td>\n",
+       "      <td>Apps_PRESSURE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>7</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.091304</td>\n",
@@ -1655,7 +1655,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Apps_VOL3D', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>8</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.006177</td>\n",
@@ -1672,24 +1672,7 @@
        "      <td>Apps_VOL3D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>8</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.021192</td>\n",
-       "      <td>0.021192</td>\n",
-       "      <td>0.021192</td>\n",
-       "      <td>0.021192</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.426524e+08</td>\n",
-       "      <td>3.194887e+08</td>\n",
-       "      <td>4.437343e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4437343.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>Apps_VOL3D</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>8</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.041352</td>\n",
@@ -1706,7 +1689,24 @@
        "      <td>Apps_VOL3D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>8</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.021192</td>\n",
+       "      <td>0.021192</td>\n",
+       "      <td>0.021192</td>\n",
+       "      <td>0.021192</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.426524e+08</td>\n",
+       "      <td>3.194887e+08</td>\n",
+       "      <td>4.437343e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4437343.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>Apps_VOL3D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>8</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.011187</td>\n",
@@ -1724,7 +1724,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Apps_ZONAL_ACCUMULATION_3D', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>9</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.003759</td>\n",
@@ -1741,24 +1741,7 @@
        "      <td>Apps_ZONAL_ACCUMULATION_3D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>9</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.013590</td>\n",
-       "      <td>0.013590</td>\n",
-       "      <td>0.013590</td>\n",
-       "      <td>0.013590</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.007847e+08</td>\n",
-       "      <td>3.338625e+07</td>\n",
-       "      <td>4.173281e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4173281.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>Apps_ZONAL_ACCUMULATION_3D</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>9</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.026485</td>\n",
@@ -1775,7 +1758,24 @@
        "      <td>Apps_ZONAL_ACCUMULATION_3D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>9</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.013590</td>\n",
+       "      <td>0.013590</td>\n",
+       "      <td>0.013590</td>\n",
+       "      <td>0.013590</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.007847e+08</td>\n",
+       "      <td>3.338625e+07</td>\n",
+       "      <td>4.173281e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4173281.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>Apps_ZONAL_ACCUMULATION_3D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>9</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.007109</td>\n",
@@ -1793,7 +1793,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>2</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.358472</td>\n",
@@ -1810,24 +1810,7 @@
        "      <td>Basic</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>2</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>1.212360</td>\n",
-       "      <td>1.212360</td>\n",
-       "      <td>1.212360</td>\n",
-       "      <td>1.212360</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>5.368709e+08</td>\n",
-       "      <td>4.613734e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>2500.0</td>\n",
-       "      <td>Basic</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>2</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>2.390606</td>\n",
@@ -1844,7 +1827,24 @@
        "      <td>Basic</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>2</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>1.212360</td>\n",
+       "      <td>1.212360</td>\n",
+       "      <td>1.212360</td>\n",
+       "      <td>1.212360</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>5.368709e+08</td>\n",
+       "      <td>4.613734e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>Basic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>2</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.660031</td>\n",
@@ -1862,7 +1862,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_COPY8', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>3</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.008633</td>\n",
@@ -1879,41 +1879,41 @@
        "      <td>Basic_COPY8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>3</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.033142</td>\n",
-       "      <td>0.033142</td>\n",
-       "      <td>0.033142</td>\n",
-       "      <td>0.033142</td>\n",
+       "      <td>0.065729</td>\n",
+       "      <td>0.065729</td>\n",
+       "      <td>0.065729</td>\n",
+       "      <td>0.065729</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>5.368709e+08</td>\n",
+       "      <td>1.073742e+09</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>4.194304e+06</td>\n",
+       "      <td>8.388608e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>50.0</td>\n",
        "      <td>Basic_COPY8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>3</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.065729</td>\n",
-       "      <td>0.065729</td>\n",
-       "      <td>0.065729</td>\n",
-       "      <td>0.065729</td>\n",
+       "      <td>0.033142</td>\n",
+       "      <td>0.033142</td>\n",
+       "      <td>0.033142</td>\n",
+       "      <td>0.033142</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>1.073742e+09</td>\n",
+       "      <td>5.368709e+08</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>8.388608e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>50.0</td>\n",
        "      <td>Basic_COPY8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>3</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.016652</td>\n",
@@ -1931,7 +1931,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_DAXPY', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>14</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.016969</td>\n",
@@ -1948,41 +1948,41 @@
        "      <td>Basic_DAXPY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>14</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.061511</td>\n",
-       "      <td>0.061511</td>\n",
-       "      <td>0.061511</td>\n",
-       "      <td>0.061511</td>\n",
+       "      <td>0.120519</td>\n",
+       "      <td>0.120519</td>\n",
+       "      <td>0.120519</td>\n",
+       "      <td>0.120519</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>1.006633e+08</td>\n",
+       "      <td>2.013266e+08</td>\n",
+       "      <td>1.677722e+07</td>\n",
        "      <td>8.388608e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>500.0</td>\n",
        "      <td>Basic_DAXPY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>14</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.120519</td>\n",
-       "      <td>0.120519</td>\n",
-       "      <td>0.120519</td>\n",
-       "      <td>0.120519</td>\n",
+       "      <td>0.061511</td>\n",
+       "      <td>0.061511</td>\n",
+       "      <td>0.061511</td>\n",
+       "      <td>0.061511</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>2.013266e+08</td>\n",
-       "      <td>1.677722e+07</td>\n",
+       "      <td>1.006633e+08</td>\n",
        "      <td>8.388608e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>500.0</td>\n",
        "      <td>Basic_DAXPY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>14</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.031757</td>\n",
@@ -2000,7 +2000,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_DAXPY_ATOMIC', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>15</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.016964</td>\n",
@@ -2017,41 +2017,41 @@
        "      <td>Basic_DAXPY_ATOMIC</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>15</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.062023</td>\n",
-       "      <td>0.062023</td>\n",
-       "      <td>0.062023</td>\n",
-       "      <td>0.062023</td>\n",
+       "      <td>0.121737</td>\n",
+       "      <td>0.121737</td>\n",
+       "      <td>0.121737</td>\n",
+       "      <td>0.121737</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>1.006633e+08</td>\n",
+       "      <td>2.013266e+08</td>\n",
+       "      <td>1.677722e+07</td>\n",
        "      <td>8.388608e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>500.0</td>\n",
        "      <td>Basic_DAXPY_ATOMIC</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>15</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.121737</td>\n",
-       "      <td>0.121737</td>\n",
-       "      <td>0.121737</td>\n",
-       "      <td>0.121737</td>\n",
+       "      <td>0.062023</td>\n",
+       "      <td>0.062023</td>\n",
+       "      <td>0.062023</td>\n",
+       "      <td>0.062023</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>2.013266e+08</td>\n",
-       "      <td>1.677722e+07</td>\n",
+       "      <td>1.006633e+08</td>\n",
        "      <td>8.388608e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>500.0</td>\n",
        "      <td>Basic_DAXPY_ATOMIC</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>15</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.031928</td>\n",
@@ -2069,7 +2069,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_IF_QUAD', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>16</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.012196</td>\n",
@@ -2086,24 +2086,7 @@
        "      <td>Basic_IF_QUAD</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>16</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.047678</td>\n",
-       "      <td>0.047678</td>\n",
-       "      <td>0.047678</td>\n",
-       "      <td>0.047678</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.677722e+08</td>\n",
-       "      <td>4.613734e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>Basic_IF_QUAD</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>16</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.095443</td>\n",
@@ -2120,7 +2103,24 @@
        "      <td>Basic_IF_QUAD</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>16</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.047678</td>\n",
+       "      <td>0.047678</td>\n",
+       "      <td>0.047678</td>\n",
+       "      <td>0.047678</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.677722e+08</td>\n",
+       "      <td>4.613734e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>180.0</td>\n",
+       "      <td>Basic_IF_QUAD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>16</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.024207</td>\n",
@@ -2138,7 +2138,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_INIT3', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>17</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.028175</td>\n",
@@ -2155,24 +2155,7 @@
        "      <td>Basic_INIT3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>17</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.106234</td>\n",
-       "      <td>0.106234</td>\n",
-       "      <td>0.106234</td>\n",
-       "      <td>0.106234</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.677722e+08</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>500.0</td>\n",
-       "      <td>Basic_INIT3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>17</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.210474</td>\n",
@@ -2189,7 +2172,24 @@
        "      <td>Basic_INIT3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>17</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.106234</td>\n",
+       "      <td>0.106234</td>\n",
+       "      <td>0.106234</td>\n",
+       "      <td>0.106234</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.677722e+08</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>Basic_INIT3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>17</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.054312</td>\n",
@@ -2207,7 +2207,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_INIT_VIEW1D', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>18</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.042175</td>\n",
@@ -2224,24 +2224,7 @@
        "      <td>Basic_INIT_VIEW1D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>18</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.123815</td>\n",
-       "      <td>0.123815</td>\n",
-       "      <td>0.123815</td>\n",
-       "      <td>0.123815</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>3.355443e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>2500.0</td>\n",
-       "      <td>Basic_INIT_VIEW1D</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>18</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.239395</td>\n",
@@ -2258,7 +2241,24 @@
        "      <td>Basic_INIT_VIEW1D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>18</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.123815</td>\n",
+       "      <td>0.123815</td>\n",
+       "      <td>0.123815</td>\n",
+       "      <td>0.123815</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>3.355443e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>Basic_INIT_VIEW1D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>18</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.076089</td>\n",
@@ -2276,7 +2276,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_INIT_VIEW1D_OFFSET', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>19</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.042183</td>\n",
@@ -2293,24 +2293,7 @@
        "      <td>Basic_INIT_VIEW1D_OFFSET</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>19</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.123782</td>\n",
-       "      <td>0.123782</td>\n",
-       "      <td>0.123782</td>\n",
-       "      <td>0.123782</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>3.355443e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>2500.0</td>\n",
-       "      <td>Basic_INIT_VIEW1D_OFFSET</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>19</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.239354</td>\n",
@@ -2327,25 +2310,42 @@
        "      <td>Basic_INIT_VIEW1D_OFFSET</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>19</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.066220</td>\n",
-       "      <td>0.066220</td>\n",
-       "      <td>0.066220</td>\n",
-       "      <td>0.066220</td>\n",
+       "      <td>0.123782</td>\n",
+       "      <td>0.123782</td>\n",
+       "      <td>0.123782</td>\n",
+       "      <td>0.123782</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>1.677722e+07</td>\n",
-       "      <td>2.097152e+06</td>\n",
-       "      <td>2.097152e+06</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>3.355443e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>Basic_INIT_VIEW1D_OFFSET</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
+       "      <td>19</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.066220</td>\n",
+       "      <td>0.066220</td>\n",
+       "      <td>0.066220</td>\n",
+       "      <td>0.066220</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.677722e+07</td>\n",
+       "      <td>2.097152e+06</td>\n",
+       "      <td>2.097152e+06</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>2097152.0</td>\n",
        "      <td>2500.0</td>\n",
        "      <td>Basic_INIT_VIEW1D_OFFSET</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_MULADDSUB', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>20</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.019719</td>\n",
@@ -2362,24 +2362,7 @@
        "      <td>Basic_MULADDSUB</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>20</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.074557</td>\n",
-       "      <td>0.074557</td>\n",
-       "      <td>0.074557</td>\n",
-       "      <td>0.074557</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.677722e+08</td>\n",
-       "      <td>1.258291e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>Basic_MULADDSUB</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>20</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.148068</td>\n",
@@ -2396,7 +2379,24 @@
        "      <td>Basic_MULADDSUB</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>20</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.074557</td>\n",
+       "      <td>0.074557</td>\n",
+       "      <td>0.074557</td>\n",
+       "      <td>0.074557</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.677722e+08</td>\n",
+       "      <td>1.258291e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>Basic_MULADDSUB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>20</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.038028</td>\n",
@@ -2414,7 +2414,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_NESTED_INIT', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>21</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.020746</td>\n",
@@ -2431,24 +2431,7 @@
        "      <td>Basic_NESTED_INIT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>21</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.059267</td>\n",
-       "      <td>0.059267</td>\n",
-       "      <td>0.059267</td>\n",
-       "      <td>0.059267</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>3.338625e+07</td>\n",
-       "      <td>1.251984e+07</td>\n",
-       "      <td>4.173281e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4173281.0</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>Basic_NESTED_INIT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>21</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.105673</td>\n",
@@ -2465,7 +2448,24 @@
        "      <td>Basic_NESTED_INIT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>21</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.059267</td>\n",
+       "      <td>0.059267</td>\n",
+       "      <td>0.059267</td>\n",
+       "      <td>0.059267</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>3.338625e+07</td>\n",
+       "      <td>1.251984e+07</td>\n",
+       "      <td>4.173281e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4173281.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>Basic_NESTED_INIT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>21</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.030605</td>\n",
@@ -2483,7 +2483,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_PI_ATOMIC', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>22</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.127441</td>\n",
@@ -2500,41 +2500,41 @@
        "      <td>Basic_PI_ATOMIC</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>22</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.436268</td>\n",
-       "      <td>0.436268</td>\n",
-       "      <td>0.436268</td>\n",
-       "      <td>0.436268</td>\n",
+       "      <td>0.871399</td>\n",
+       "      <td>0.871399</td>\n",
+       "      <td>0.871399</td>\n",
+       "      <td>0.871399</td>\n",
        "      <td>128.0</td>\n",
        "      <td>1.600000e+01</td>\n",
-       "      <td>2.516582e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
+       "      <td>5.033165e+07</td>\n",
+       "      <td>8.388608e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>50.0</td>\n",
        "      <td>Basic_PI_ATOMIC</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>22</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.871399</td>\n",
-       "      <td>0.871399</td>\n",
-       "      <td>0.871399</td>\n",
-       "      <td>0.871399</td>\n",
+       "      <td>0.436268</td>\n",
+       "      <td>0.436268</td>\n",
+       "      <td>0.436268</td>\n",
+       "      <td>0.436268</td>\n",
        "      <td>128.0</td>\n",
        "      <td>1.600000e+01</td>\n",
-       "      <td>5.033165e+07</td>\n",
-       "      <td>8.388608e+06</td>\n",
+       "      <td>2.516582e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>50.0</td>\n",
        "      <td>Basic_PI_ATOMIC</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>22</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.247100</td>\n",
@@ -2552,7 +2552,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_PI_REDUCE', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>23</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.002507</td>\n",
@@ -2569,41 +2569,41 @@
        "      <td>Basic_PI_REDUCE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>23</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.004897</td>\n",
-       "      <td>0.004897</td>\n",
-       "      <td>0.004897</td>\n",
-       "      <td>0.004897</td>\n",
+       "      <td>0.008562</td>\n",
+       "      <td>0.008562</td>\n",
+       "      <td>0.008562</td>\n",
+       "      <td>0.008562</td>\n",
        "      <td>128.0</td>\n",
        "      <td>1.600000e+01</td>\n",
-       "      <td>2.516582e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
+       "      <td>5.033165e+07</td>\n",
+       "      <td>8.388608e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>50.0</td>\n",
        "      <td>Basic_PI_REDUCE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>23</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.008562</td>\n",
-       "      <td>0.008562</td>\n",
-       "      <td>0.008562</td>\n",
-       "      <td>0.008562</td>\n",
+       "      <td>0.004897</td>\n",
+       "      <td>0.004897</td>\n",
+       "      <td>0.004897</td>\n",
+       "      <td>0.004897</td>\n",
        "      <td>128.0</td>\n",
        "      <td>1.600000e+01</td>\n",
-       "      <td>5.033165e+07</td>\n",
-       "      <td>8.388608e+06</td>\n",
+       "      <td>2.516582e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>50.0</td>\n",
        "      <td>Basic_PI_REDUCE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>23</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.003325</td>\n",
@@ -2621,7 +2621,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_REDUCE3_INT', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>24</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.002289</td>\n",
@@ -2638,24 +2638,7 @@
        "      <td>Basic_REDUCE3_INT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>24</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.005095</td>\n",
-       "      <td>0.005095</td>\n",
-       "      <td>0.005095</td>\n",
-       "      <td>0.005095</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.677724e+07</td>\n",
-       "      <td>4.194305e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>Basic_REDUCE3_INT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>24</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.008823</td>\n",
@@ -2672,7 +2655,24 @@
        "      <td>Basic_REDUCE3_INT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>24</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.005095</td>\n",
+       "      <td>0.005095</td>\n",
+       "      <td>0.005095</td>\n",
+       "      <td>0.005095</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.677724e+07</td>\n",
+       "      <td>4.194305e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>Basic_REDUCE3_INT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>24</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.003286</td>\n",
@@ -2690,7 +2690,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_REDUCE_STRUCT', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>25</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.015869</td>\n",
@@ -2707,24 +2707,7 @@
        "      <td>Basic_REDUCE_STRUCT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>25</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.069074</td>\n",
-       "      <td>0.069074</td>\n",
-       "      <td>0.069074</td>\n",
-       "      <td>0.069074</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>6.710891e+07</td>\n",
-       "      <td>8.388610e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>Basic_REDUCE_STRUCT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>25</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.146754</td>\n",
@@ -2741,7 +2724,24 @@
        "      <td>Basic_REDUCE_STRUCT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>25</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.069074</td>\n",
+       "      <td>0.069074</td>\n",
+       "      <td>0.069074</td>\n",
+       "      <td>0.069074</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>6.710891e+07</td>\n",
+       "      <td>8.388610e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>Basic_REDUCE_STRUCT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>25</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.033105</td>\n",
@@ -2759,7 +2759,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Basic_TRAP_INT', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>26</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.002508</td>\n",
@@ -2776,41 +2776,41 @@
        "      <td>Basic_TRAP_INT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>26</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.004914</td>\n",
-       "      <td>0.004914</td>\n",
-       "      <td>0.004914</td>\n",
-       "      <td>0.004914</td>\n",
+       "      <td>0.008568</td>\n",
+       "      <td>0.008568</td>\n",
+       "      <td>0.008568</td>\n",
+       "      <td>0.008568</td>\n",
        "      <td>128.0</td>\n",
        "      <td>1.600000e+01</td>\n",
-       "      <td>4.194304e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
+       "      <td>8.388608e+07</td>\n",
+       "      <td>8.388608e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>50.0</td>\n",
        "      <td>Basic_TRAP_INT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>26</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.008568</td>\n",
-       "      <td>0.008568</td>\n",
-       "      <td>0.008568</td>\n",
-       "      <td>0.008568</td>\n",
+       "      <td>0.004914</td>\n",
+       "      <td>0.004914</td>\n",
+       "      <td>0.004914</td>\n",
+       "      <td>0.004914</td>\n",
        "      <td>128.0</td>\n",
        "      <td>1.600000e+01</td>\n",
-       "      <td>8.388608e+07</td>\n",
-       "      <td>8.388608e+06</td>\n",
+       "      <td>4.194304e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>50.0</td>\n",
        "      <td>Basic_TRAP_INT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>26</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.003319</td>\n",
@@ -2828,7 +2828,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Lcals', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>27</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.386209</td>\n",
@@ -2845,24 +2845,7 @@
        "      <td>Lcals</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>27</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>1.449984</td>\n",
-       "      <td>1.449984</td>\n",
-       "      <td>1.449984</td>\n",
-       "      <td>1.449984</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>6.710886e+08</td>\n",
-       "      <td>1.841891e+08</td>\n",
-       "      <td>1.258291e+07</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>Lcals</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>27</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>2.866099</td>\n",
@@ -2879,7 +2862,24 @@
        "      <td>Lcals</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>27</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>1.449984</td>\n",
+       "      <td>1.449984</td>\n",
+       "      <td>1.449984</td>\n",
+       "      <td>1.449984</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>6.710886e+08</td>\n",
+       "      <td>1.841891e+08</td>\n",
+       "      <td>1.258291e+07</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>Lcals</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>27</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.740920</td>\n",
@@ -2897,7 +2897,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Lcals_DIFF_PREDICT', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>28</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.061602</td>\n",
@@ -2914,24 +2914,7 @@
        "      <td>Lcals_DIFF_PREDICT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>28</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.236979</td>\n",
-       "      <td>0.236979</td>\n",
-       "      <td>0.236979</td>\n",
-       "      <td>0.236979</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>6.710886e+08</td>\n",
-       "      <td>3.774874e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>200.0</td>\n",
-       "      <td>Lcals_DIFF_PREDICT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>28</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.471917</td>\n",
@@ -2948,7 +2931,24 @@
        "      <td>Lcals_DIFF_PREDICT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>28</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.236979</td>\n",
+       "      <td>0.236979</td>\n",
+       "      <td>0.236979</td>\n",
+       "      <td>0.236979</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>6.710886e+08</td>\n",
+       "      <td>3.774874e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>Lcals_DIFF_PREDICT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>28</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.119687</td>\n",
@@ -2966,7 +2966,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Lcals_EOS', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>29</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.022674</td>\n",
@@ -2975,32 +2975,15 @@
        "      <td>0.022674</td>\n",
        "      <td>128.0</td>\n",
        "      <td>3.355449e+07</td>\n",
-       "      <td>1.677722e+07</td>\n",
-       "      <td>1.048576e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1048576.0</td>\n",
-       "      <td>500.0</td>\n",
-       "      <td>Lcals_EOS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>29</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.084055</td>\n",
-       "      <td>0.084055</td>\n",
-       "      <td>0.084055</td>\n",
-       "      <td>0.084055</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.342178e+08</td>\n",
-       "      <td>6.710886e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
+       "      <td>1.677722e+07</td>\n",
+       "      <td>1.048576e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>1048576.0</td>\n",
        "      <td>500.0</td>\n",
        "      <td>Lcals_EOS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>29</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.163718</td>\n",
@@ -3017,7 +3000,24 @@
        "      <td>Lcals_EOS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>29</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.084055</td>\n",
+       "      <td>0.084055</td>\n",
+       "      <td>0.084055</td>\n",
+       "      <td>0.084055</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.342178e+08</td>\n",
+       "      <td>6.710886e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>Lcals_EOS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>29</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.043212</td>\n",
@@ -3035,7 +3035,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Lcals_FIRST_DIFF', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>30</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.048094</td>\n",
@@ -3052,24 +3052,7 @@
        "      <td>Lcals_FIRST_DIFF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>30</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.173070</td>\n",
-       "      <td>0.173070</td>\n",
-       "      <td>0.173070</td>\n",
-       "      <td>0.173070</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>6.710887e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>Lcals_FIRST_DIFF</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>30</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.339211</td>\n",
@@ -3086,7 +3069,24 @@
        "      <td>Lcals_FIRST_DIFF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>30</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.173070</td>\n",
+       "      <td>0.173070</td>\n",
+       "      <td>0.173070</td>\n",
+       "      <td>0.173070</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>6.710887e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>Lcals_FIRST_DIFF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>30</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.089910</td>\n",
@@ -3104,7 +3104,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Lcals_FIRST_MIN', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>31</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.005570</td>\n",
@@ -3121,41 +3121,41 @@
        "      <td>Lcals_FIRST_MIN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>31</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.012164</td>\n",
-       "      <td>0.012164</td>\n",
-       "      <td>0.012164</td>\n",
-       "      <td>0.012164</td>\n",
+       "      <td>0.021264</td>\n",
+       "      <td>0.021264</td>\n",
+       "      <td>0.021264</td>\n",
+       "      <td>0.021264</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>3.355446e+07</td>\n",
+       "      <td>6.710890e+07</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>4.194304e+06</td>\n",
+       "      <td>8.388608e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>100.0</td>\n",
        "      <td>Lcals_FIRST_MIN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>31</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.021264</td>\n",
-       "      <td>0.021264</td>\n",
-       "      <td>0.021264</td>\n",
-       "      <td>0.021264</td>\n",
+       "      <td>0.012164</td>\n",
+       "      <td>0.012164</td>\n",
+       "      <td>0.012164</td>\n",
+       "      <td>0.012164</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>6.710890e+07</td>\n",
+       "      <td>3.355446e+07</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>8.388608e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>100.0</td>\n",
        "      <td>Lcals_FIRST_MIN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>31</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.007701</td>\n",
@@ -3173,7 +3173,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Lcals_FIRST_SUM', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>32</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.048174</td>\n",
@@ -3190,24 +3190,7 @@
        "      <td>Lcals_FIRST_SUM</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>32</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.173145</td>\n",
-       "      <td>0.173145</td>\n",
-       "      <td>0.173145</td>\n",
-       "      <td>0.173145</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>6.710886e+07</td>\n",
-       "      <td>4.194303e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>Lcals_FIRST_SUM</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>32</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.339436</td>\n",
@@ -3224,7 +3207,24 @@
        "      <td>Lcals_FIRST_SUM</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>32</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.173145</td>\n",
+       "      <td>0.173145</td>\n",
+       "      <td>0.173145</td>\n",
+       "      <td>0.173145</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>6.710886e+07</td>\n",
+       "      <td>4.194303e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>Lcals_FIRST_SUM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>32</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.090045</td>\n",
@@ -3242,7 +3242,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Lcals_GEN_LIN_RECUR', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>33</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.049221</td>\n",
@@ -3259,24 +3259,7 @@
        "      <td>Lcals_GEN_LIN_RECUR</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>33</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.202627</td>\n",
-       "      <td>0.202627</td>\n",
-       "      <td>0.202627</td>\n",
-       "      <td>0.202627</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>3.355443e+08</td>\n",
-       "      <td>2.516582e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>500.0</td>\n",
-       "      <td>Lcals_GEN_LIN_RECUR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>33</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.407307</td>\n",
@@ -3293,7 +3276,24 @@
        "      <td>Lcals_GEN_LIN_RECUR</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>33</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.202627</td>\n",
+       "      <td>0.202627</td>\n",
+       "      <td>0.202627</td>\n",
+       "      <td>0.202627</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>3.355443e+08</td>\n",
+       "      <td>2.516582e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>Lcals_GEN_LIN_RECUR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>33</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.100018</td>\n",
@@ -3311,7 +3311,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Lcals_HYDRO_1D', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>34</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.033902</td>\n",
@@ -3328,24 +3328,7 @@
        "      <td>Lcals_HYDRO_1D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>34</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.123286</td>\n",
-       "      <td>0.123286</td>\n",
-       "      <td>0.123286</td>\n",
-       "      <td>0.123286</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.006633e+08</td>\n",
-       "      <td>2.097152e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>Lcals_HYDRO_1D</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>34</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.242067</td>\n",
@@ -3362,7 +3345,24 @@
        "      <td>Lcals_HYDRO_1D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>34</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.123286</td>\n",
+       "      <td>0.123286</td>\n",
+       "      <td>0.123286</td>\n",
+       "      <td>0.123286</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.006633e+08</td>\n",
+       "      <td>2.097152e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>Lcals_HYDRO_1D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>34</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.063721</td>\n",
@@ -3380,7 +3380,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Lcals_HYDRO_2D', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>35</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.022876</td>\n",
@@ -3397,24 +3397,7 @@
        "      <td>Lcals_HYDRO_2D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>35</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.086800</td>\n",
-       "      <td>0.086800</td>\n",
-       "      <td>0.086800</td>\n",
-       "      <td>0.086800</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>6.033247e+08</td>\n",
-       "      <td>1.841891e+08</td>\n",
-       "      <td>1.258291e+07</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>Lcals_HYDRO_2D</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>35</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.171752</td>\n",
@@ -3431,7 +3414,24 @@
        "      <td>Lcals_HYDRO_2D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>35</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.086800</td>\n",
+       "      <td>0.086800</td>\n",
+       "      <td>0.086800</td>\n",
+       "      <td>0.086800</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>6.033247e+08</td>\n",
+       "      <td>1.841891e+08</td>\n",
+       "      <td>1.258291e+07</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>Lcals_HYDRO_2D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>35</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.043652</td>\n",
@@ -3449,7 +3449,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Lcals_INT_PREDICT', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>36</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.046661</td>\n",
@@ -3466,24 +3466,7 @@
        "      <td>Lcals_INT_PREDICT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>36</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.182561</td>\n",
-       "      <td>0.182561</td>\n",
-       "      <td>0.182561</td>\n",
-       "      <td>0.182561</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>3.690988e+08</td>\n",
-       "      <td>7.130317e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>400.0</td>\n",
-       "      <td>Lcals_INT_PREDICT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>36</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.363553</td>\n",
@@ -3500,7 +3483,24 @@
        "      <td>Lcals_INT_PREDICT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>36</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.182561</td>\n",
+       "      <td>0.182561</td>\n",
+       "      <td>0.182561</td>\n",
+       "      <td>0.182561</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>3.690988e+08</td>\n",
+       "      <td>7.130317e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>Lcals_INT_PREDICT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>36</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.091770</td>\n",
@@ -3518,7 +3518,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Lcals_PLANCKIAN', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>37</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.002758</td>\n",
@@ -3535,24 +3535,7 @@
        "      <td>Lcals_PLANCKIAN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>37</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.010287</td>\n",
-       "      <td>0.010287</td>\n",
-       "      <td>0.010287</td>\n",
-       "      <td>0.010287</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.677722e+08</td>\n",
-       "      <td>1.677722e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>Lcals_PLANCKIAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>37</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.020310</td>\n",
@@ -3569,7 +3552,24 @@
        "      <td>Lcals_PLANCKIAN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>37</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.010287</td>\n",
+       "      <td>0.010287</td>\n",
+       "      <td>0.010287</td>\n",
+       "      <td>0.010287</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.677722e+08</td>\n",
+       "      <td>1.677722e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>Lcals_PLANCKIAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>37</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.005265</td>\n",
@@ -3587,7 +3587,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Lcals_TRIDIAG_ELIM', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>38</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.044595</td>\n",
@@ -3604,24 +3604,7 @@
        "      <td>Lcals_TRIDIAG_ELIM</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>38</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.164922</td>\n",
-       "      <td>0.164922</td>\n",
-       "      <td>0.164922</td>\n",
-       "      <td>0.164922</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.342177e+08</td>\n",
-       "      <td>8.388606e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>Lcals_TRIDIAG_ELIM</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>38</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.325473</td>\n",
@@ -3638,7 +3621,24 @@
        "      <td>Lcals_TRIDIAG_ELIM</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>38</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.164922</td>\n",
+       "      <td>0.164922</td>\n",
+       "      <td>0.164922</td>\n",
+       "      <td>0.164922</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.342177e+08</td>\n",
+       "      <td>8.388606e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>Lcals_TRIDIAG_ELIM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>38</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.085855</td>\n",
@@ -3656,7 +3656,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>39</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.582509</td>\n",
@@ -3673,24 +3673,7 @@
        "      <td>Polybench</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>39</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>2.746726</td>\n",
-       "      <td>2.746726</td>\n",
-       "      <td>2.746726</td>\n",
-       "      <td>2.746726</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.342898e+10</td>\n",
-       "      <td>3.576198e+10</td>\n",
-       "      <td>5.035623e+08</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>4198401.0</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>Polybench</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>39</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>6.560859</td>\n",
@@ -3707,7 +3690,24 @@
        "      <td>Polybench</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>39</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>2.746726</td>\n",
+       "      <td>2.746726</td>\n",
+       "      <td>2.746726</td>\n",
+       "      <td>2.746726</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.342898e+10</td>\n",
+       "      <td>3.576198e+10</td>\n",
+       "      <td>5.035623e+08</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>4198401.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>Polybench</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>39</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>1.226385</td>\n",
@@ -3725,7 +3725,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_2MM', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>40</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.006010</td>\n",
@@ -3742,24 +3742,7 @@
        "      <td>Polybench_2MM</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>40</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.032475</td>\n",
-       "      <td>0.032475</td>\n",
-       "      <td>0.032475</td>\n",
-       "      <td>0.032475</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.710669e+08</td>\n",
-       "      <td>3.131167e+10</td>\n",
-       "      <td>8.396802e+06</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4198401.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>Polybench_2MM</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>40</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.080898</td>\n",
@@ -3776,7 +3759,24 @@
        "      <td>Polybench_2MM</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>40</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.032475</td>\n",
+       "      <td>0.032475</td>\n",
+       "      <td>0.032475</td>\n",
+       "      <td>0.032475</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.710669e+08</td>\n",
+       "      <td>3.131167e+10</td>\n",
+       "      <td>8.396802e+06</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4198401.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Polybench_2MM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>40</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.013480</td>\n",
@@ -3794,7 +3794,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_3MM', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>41</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.009086</td>\n",
@@ -3811,24 +3811,7 @@
        "      <td>Polybench_3MM</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>41</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.043815</td>\n",
-       "      <td>0.043815</td>\n",
-       "      <td>0.043815</td>\n",
-       "      <td>0.043815</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>2.403887e+08</td>\n",
-       "      <td>3.576198e+10</td>\n",
-       "      <td>1.259520e+07</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4198401.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>Polybench_3MM</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>41</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.103543</td>\n",
@@ -3845,7 +3828,24 @@
        "      <td>Polybench_3MM</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>41</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.043815</td>\n",
+       "      <td>0.043815</td>\n",
+       "      <td>0.043815</td>\n",
+       "      <td>0.043815</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>2.403887e+08</td>\n",
+       "      <td>3.576198e+10</td>\n",
+       "      <td>1.259520e+07</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4198401.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Polybench_3MM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>41</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.019303</td>\n",
@@ -3863,7 +3863,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_ADI', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>42</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.037492</td>\n",
@@ -3880,24 +3880,7 @@
        "      <td>Polybench_ADI</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>42</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.080987</td>\n",
-       "      <td>0.080987</td>\n",
-       "      <td>0.080987</td>\n",
-       "      <td>0.080987</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.610612e+09</td>\n",
-       "      <td>5.698684e+08</td>\n",
-       "      <td>1.637600e+04</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>4190209.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>Polybench_ADI</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>42</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.128455</td>\n",
@@ -3914,7 +3897,24 @@
        "      <td>Polybench_ADI</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>42</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.080987</td>\n",
+       "      <td>0.080987</td>\n",
+       "      <td>0.080987</td>\n",
+       "      <td>0.080987</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.610612e+09</td>\n",
+       "      <td>5.698684e+08</td>\n",
+       "      <td>1.637600e+04</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>4190209.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Polybench_ADI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>42</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.055645</td>\n",
@@ -3932,7 +3932,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_ATAX', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>43</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.025928</td>\n",
@@ -3949,24 +3949,7 @@
        "      <td>Polybench_ATAX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>43</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.053237</td>\n",
-       "      <td>0.053237</td>\n",
-       "      <td>0.053237</td>\n",
-       "      <td>0.053237</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>6.725638e+07</td>\n",
-       "      <td>1.679360e+07</td>\n",
-       "      <td>4.098000e+03</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4198401.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>Polybench_ATAX</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>43</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.080220</td>\n",
@@ -3983,7 +3966,24 @@
        "      <td>Polybench_ATAX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>43</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.053237</td>\n",
+       "      <td>0.053237</td>\n",
+       "      <td>0.053237</td>\n",
+       "      <td>0.053237</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>6.725638e+07</td>\n",
+       "      <td>1.679360e+07</td>\n",
+       "      <td>4.098000e+03</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4198401.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>Polybench_ATAX</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>43</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.038216</td>\n",
@@ -4001,7 +4001,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_FDTD_2D', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>44</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.037030</td>\n",
@@ -4018,24 +4018,7 @@
        "      <td>Polybench_FDTD_2D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>44</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.133299</td>\n",
-       "      <td>0.133299</td>\n",
-       "      <td>0.133299</td>\n",
-       "      <td>0.133299</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.342898e+10</td>\n",
-       "      <td>1.845985e+09</td>\n",
-       "      <td>5.035623e+08</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>4196352.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>Polybench_FDTD_2D</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>44</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.261247</td>\n",
@@ -4052,7 +4035,24 @@
        "      <td>Polybench_FDTD_2D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>44</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.133299</td>\n",
+       "      <td>0.133299</td>\n",
+       "      <td>0.133299</td>\n",
+       "      <td>0.133299</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.342898e+10</td>\n",
+       "      <td>1.845985e+09</td>\n",
+       "      <td>5.035623e+08</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>4196352.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>Polybench_FDTD_2D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>44</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.069062</td>\n",
@@ -4070,7 +4070,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_FLOYD_WARSHALL', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>45</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.205779</td>\n",
@@ -4082,29 +4082,12 @@
        "      <td>1.076891e+09</td>\n",
        "      <td>1.050625e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1050625.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>Polybench_FLOYD_WARSHALL</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>45</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>1.518138</td>\n",
-       "      <td>1.518138</td>\n",
-       "      <td>1.518138</td>\n",
-       "      <td>1.518138</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>6.717442e+07</td>\n",
-       "      <td>8.602524e+09</td>\n",
-       "      <td>4.198401e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4198401.0</td>\n",
+       "      <td>1050625.0</td>\n",
        "      <td>8.0</td>\n",
        "      <td>Polybench_FLOYD_WARSHALL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>45</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>4.208355</td>\n",
@@ -4121,7 +4104,24 @@
        "      <td>Polybench_FLOYD_WARSHALL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>45</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>1.518138</td>\n",
+       "      <td>1.518138</td>\n",
+       "      <td>1.518138</td>\n",
+       "      <td>1.518138</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>6.717442e+07</td>\n",
+       "      <td>8.602524e+09</td>\n",
+       "      <td>4.198401e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4198401.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>Polybench_FLOYD_WARSHALL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>45</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.558492</td>\n",
@@ -4139,7 +4139,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_GEMM', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>46</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.006179</td>\n",
@@ -4156,24 +4156,7 @@
        "      <td>Polybench_GEMM</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>46</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.041807</td>\n",
-       "      <td>0.041807</td>\n",
-       "      <td>0.041807</td>\n",
-       "      <td>0.041807</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>7.292801e+07</td>\n",
-       "      <td>1.511844e+10</td>\n",
-       "      <td>4.198401e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4198401.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>Polybench_GEMM</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>46</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.099276</td>\n",
@@ -4190,7 +4173,24 @@
        "      <td>Polybench_GEMM</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>46</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.041807</td>\n",
+       "      <td>0.041807</td>\n",
+       "      <td>0.041807</td>\n",
+       "      <td>0.041807</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>7.292801e+07</td>\n",
+       "      <td>1.511844e+10</td>\n",
+       "      <td>4.198401e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4198401.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Polybench_GEMM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>46</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.012245</td>\n",
@@ -4208,7 +4208,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_GEMVER', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>47</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.006881</td>\n",
@@ -4225,24 +4225,7 @@
        "      <td>Polybench_GEMVER</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>47</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.015631</td>\n",
-       "      <td>0.015631</td>\n",
-       "      <td>0.015631</td>\n",
-       "      <td>0.015631</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.345619e+08</td>\n",
-       "      <td>4.198606e+07</td>\n",
-       "      <td>1.259725e+07</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4198401.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>Polybench_GEMVER</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>47</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.022255</td>\n",
@@ -4259,7 +4242,24 @@
        "      <td>Polybench_GEMVER</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>47</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.015631</td>\n",
+       "      <td>0.015631</td>\n",
+       "      <td>0.015631</td>\n",
+       "      <td>0.015631</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.345619e+08</td>\n",
+       "      <td>4.198606e+07</td>\n",
+       "      <td>1.259725e+07</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4198401.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>Polybench_GEMVER</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>47</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.010060</td>\n",
@@ -4277,7 +4277,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_GESUMMV', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>48</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.025768</td>\n",
@@ -4294,24 +4294,7 @@
        "      <td>Polybench_GESUMMV</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>48</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.056035</td>\n",
-       "      <td>0.056035</td>\n",
-       "      <td>0.056035</td>\n",
-       "      <td>0.056035</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>6.722359e+07</td>\n",
-       "      <td>1.679975e+07</td>\n",
-       "      <td>2.049000e+03</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4198401.0</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>Polybench_GESUMMV</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>48</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.084444</td>\n",
@@ -4328,7 +4311,24 @@
        "      <td>Polybench_GESUMMV</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>48</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.056035</td>\n",
+       "      <td>0.056035</td>\n",
+       "      <td>0.056035</td>\n",
+       "      <td>0.056035</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>6.722359e+07</td>\n",
+       "      <td>1.679975e+07</td>\n",
+       "      <td>2.049000e+03</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4198401.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>Polybench_GESUMMV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>48</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.038593</td>\n",
@@ -4346,7 +4346,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_HEAT_3D', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>49</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.021772</td>\n",
@@ -4363,24 +4363,7 @@
        "      <td>Polybench_HEAT_3D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>49</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.077665</td>\n",
-       "      <td>0.077665</td>\n",
-       "      <td>0.077665</td>\n",
-       "      <td>0.077665</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>2.670592e+09</td>\n",
-       "      <td>2.457600e+09</td>\n",
-       "      <td>1.638400e+08</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>4096000.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>Polybench_HEAT_3D</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>49</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.152381</td>\n",
@@ -4397,7 +4380,24 @@
        "      <td>Polybench_HEAT_3D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>49</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.077665</td>\n",
+       "      <td>0.077665</td>\n",
+       "      <td>0.077665</td>\n",
+       "      <td>0.077665</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>2.670592e+09</td>\n",
+       "      <td>2.457600e+09</td>\n",
+       "      <td>1.638400e+08</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>4096000.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>Polybench_HEAT_3D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>49</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.040473</td>\n",
@@ -4415,7 +4415,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_JACOBI_1D', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>50</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.076720</td>\n",
@@ -4432,24 +4432,7 @@
        "      <td>Polybench_JACOBI_1D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>50</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.277465</td>\n",
-       "      <td>0.277465</td>\n",
-       "      <td>0.277465</td>\n",
-       "      <td>0.277465</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>2.147483e+09</td>\n",
-       "      <td>4.026530e+08</td>\n",
-       "      <td>1.342177e+08</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>4194302.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>Polybench_JACOBI_1D</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>50</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.543910</td>\n",
@@ -4466,7 +4449,24 @@
        "      <td>Polybench_JACOBI_1D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>50</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.277465</td>\n",
+       "      <td>0.277465</td>\n",
+       "      <td>0.277465</td>\n",
+       "      <td>0.277465</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>2.147483e+09</td>\n",
+       "      <td>4.026530e+08</td>\n",
+       "      <td>1.342177e+08</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>4194302.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>Polybench_JACOBI_1D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>50</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.144260</td>\n",
@@ -4484,7 +4484,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_JACOBI_2D', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>51</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.101590</td>\n",
@@ -4501,24 +4501,7 @@
        "      <td>Polybench_JACOBI_2D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>51</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.370396</td>\n",
-       "      <td>0.370396</td>\n",
-       "      <td>0.370396</td>\n",
-       "      <td>0.370396</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>5.368708e+09</td>\n",
-       "      <td>1.676084e+09</td>\n",
-       "      <td>3.352167e+08</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4190209.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>Polybench_JACOBI_2D</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>51</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.727029</td>\n",
@@ -4535,7 +4518,24 @@
        "      <td>Polybench_JACOBI_2D</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>51</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.370396</td>\n",
+       "      <td>0.370396</td>\n",
+       "      <td>0.370396</td>\n",
+       "      <td>0.370396</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>5.368708e+09</td>\n",
+       "      <td>1.676084e+09</td>\n",
+       "      <td>3.352167e+08</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4190209.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>Polybench_JACOBI_2D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>51</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.193490</td>\n",
@@ -4553,7 +4553,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Polybench_MVT', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>52</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.022179</td>\n",
@@ -4570,24 +4570,7 @@
        "      <td>Polybench_MVT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>52</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.045677</td>\n",
-       "      <td>0.045677</td>\n",
-       "      <td>0.045677</td>\n",
-       "      <td>0.045677</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>6.727277e+07</td>\n",
-       "      <td>1.679360e+07</td>\n",
-       "      <td>4.098000e+03</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4198401.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>Polybench_MVT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>52</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.068737</td>\n",
@@ -4604,7 +4587,24 @@
        "      <td>Polybench_MVT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>52</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.045677</td>\n",
+       "      <td>0.045677</td>\n",
+       "      <td>0.045677</td>\n",
+       "      <td>0.045677</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>6.727277e+07</td>\n",
+       "      <td>1.679360e+07</td>\n",
+       "      <td>4.098000e+03</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4198401.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>Polybench_MVT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>52</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.032960</td>\n",
@@ -4622,7 +4622,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Stream', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>53</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.261048</td>\n",
@@ -4639,41 +4639,41 @@
        "      <td>Stream</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>53</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.773695</td>\n",
-       "      <td>0.773695</td>\n",
-       "      <td>0.773695</td>\n",
-       "      <td>0.773695</td>\n",
+       "      <td>1.459231</td>\n",
+       "      <td>1.459231</td>\n",
+       "      <td>1.459231</td>\n",
+       "      <td>1.459231</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>1.006633e+08</td>\n",
+       "      <td>2.013266e+08</td>\n",
+       "      <td>1.677722e+07</td>\n",
        "      <td>8.388608e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>2000.0</td>\n",
        "      <td>Stream</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>53</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>1.459231</td>\n",
-       "      <td>1.459231</td>\n",
-       "      <td>1.459231</td>\n",
-       "      <td>1.459231</td>\n",
+       "      <td>0.773695</td>\n",
+       "      <td>0.773695</td>\n",
+       "      <td>0.773695</td>\n",
+       "      <td>0.773695</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>2.013266e+08</td>\n",
-       "      <td>1.677722e+07</td>\n",
+       "      <td>1.006633e+08</td>\n",
        "      <td>8.388608e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>2000.0</td>\n",
        "      <td>Stream</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>53</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.433616</td>\n",
@@ -4691,7 +4691,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Stream_ADD', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>54</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.033609</td>\n",
@@ -4708,24 +4708,7 @@
        "      <td>Stream_ADD</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>54</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.123005</td>\n",
-       "      <td>0.123005</td>\n",
-       "      <td>0.123005</td>\n",
-       "      <td>0.123005</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>1.006633e+08</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>Stream_ADD</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>54</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.241648</td>\n",
@@ -4742,7 +4725,24 @@
        "      <td>Stream_ADD</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>54</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.123005</td>\n",
+       "      <td>0.123005</td>\n",
+       "      <td>0.123005</td>\n",
+       "      <td>0.123005</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>1.006633e+08</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>Stream_ADD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>54</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.063223</td>\n",
@@ -4760,7 +4760,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Stream_COPY', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>55</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.042624</td>\n",
@@ -4777,41 +4777,41 @@
        "      <td>Stream_COPY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>55</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.154585</td>\n",
-       "      <td>0.154585</td>\n",
-       "      <td>0.154585</td>\n",
-       "      <td>0.154585</td>\n",
+       "      <td>0.303031</td>\n",
+       "      <td>0.303031</td>\n",
+       "      <td>0.303031</td>\n",
+       "      <td>0.303031</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>6.710886e+07</td>\n",
+       "      <td>1.342177e+08</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>4.194304e+06</td>\n",
+       "      <td>8.388608e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>1800.0</td>\n",
        "      <td>Stream_COPY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>55</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.303031</td>\n",
-       "      <td>0.303031</td>\n",
-       "      <td>0.303031</td>\n",
-       "      <td>0.303031</td>\n",
+       "      <td>0.154585</td>\n",
+       "      <td>0.154585</td>\n",
+       "      <td>0.154585</td>\n",
+       "      <td>0.154585</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>1.342177e+08</td>\n",
+       "      <td>6.710886e+07</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>8.388608e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>1800.0</td>\n",
        "      <td>Stream_COPY</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>55</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.080353</td>\n",
@@ -4829,7 +4829,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Stream_DOT', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>56</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.108451</td>\n",
@@ -4846,41 +4846,41 @@
        "      <td>Stream_DOT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>56</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.218470</td>\n",
-       "      <td>0.218470</td>\n",
-       "      <td>0.218470</td>\n",
-       "      <td>0.218470</td>\n",
+       "      <td>0.370400</td>\n",
+       "      <td>0.370400</td>\n",
+       "      <td>0.370400</td>\n",
+       "      <td>0.370400</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>6.710888e+07</td>\n",
+       "      <td>1.342177e+08</td>\n",
+       "      <td>1.677722e+07</td>\n",
        "      <td>8.388608e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>2000.0</td>\n",
        "      <td>Stream_DOT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>56</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.370400</td>\n",
-       "      <td>0.370400</td>\n",
-       "      <td>0.370400</td>\n",
-       "      <td>0.370400</td>\n",
+       "      <td>0.218470</td>\n",
+       "      <td>0.218470</td>\n",
+       "      <td>0.218470</td>\n",
+       "      <td>0.218470</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>1.342177e+08</td>\n",
-       "      <td>1.677722e+07</td>\n",
+       "      <td>6.710888e+07</td>\n",
        "      <td>8.388608e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>2000.0</td>\n",
        "      <td>Stream_DOT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>56</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.146039</td>\n",
@@ -4898,7 +4898,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Stream_MUL', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>57</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.042654</td>\n",
@@ -4915,24 +4915,7 @@
        "      <td>Stream_MUL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
-       "      <td>57</td>\n",
-       "      <td>regionprofile</td>\n",
-       "      <td>0.154602</td>\n",
-       "      <td>0.154602</td>\n",
-       "      <td>0.154602</td>\n",
-       "      <td>0.154602</td>\n",
-       "      <td>128.0</td>\n",
-       "      <td>6.710886e+07</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
-       "      <td>1800.0</td>\n",
-       "      <td>Stream_MUL</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>57</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.303001</td>\n",
@@ -4949,7 +4932,24 @@
        "      <td>Stream_MUL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>4737801908790309618</th>\n",
+       "      <td>57</td>\n",
+       "      <td>regionprofile</td>\n",
+       "      <td>0.154602</td>\n",
+       "      <td>0.154602</td>\n",
+       "      <td>0.154602</td>\n",
+       "      <td>0.154602</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>6.710886e+07</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4194304.0</td>\n",
+       "      <td>1800.0</td>\n",
+       "      <td>Stream_MUL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>57</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.080400</td>\n",
@@ -4967,7 +4967,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">{'name': 'Stream_TRIAD', 'type': 'function'}</th>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>58</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.033675</td>\n",
@@ -4984,41 +4984,41 @@
        "      <td>Stream_TRIAD</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>58</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.122992</td>\n",
-       "      <td>0.122992</td>\n",
-       "      <td>0.122992</td>\n",
-       "      <td>0.122992</td>\n",
+       "      <td>0.241107</td>\n",
+       "      <td>0.241107</td>\n",
+       "      <td>0.241107</td>\n",
+       "      <td>0.241107</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>1.006633e+08</td>\n",
+       "      <td>2.013266e+08</td>\n",
+       "      <td>1.677722e+07</td>\n",
        "      <td>8.388608e+06</td>\n",
-       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>1000.0</td>\n",
        "      <td>Stream_TRIAD</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>58</td>\n",
        "      <td>regionprofile</td>\n",
-       "      <td>0.241107</td>\n",
-       "      <td>0.241107</td>\n",
-       "      <td>0.241107</td>\n",
-       "      <td>0.241107</td>\n",
+       "      <td>0.122992</td>\n",
+       "      <td>0.122992</td>\n",
+       "      <td>0.122992</td>\n",
+       "      <td>0.122992</td>\n",
        "      <td>128.0</td>\n",
-       "      <td>2.013266e+08</td>\n",
-       "      <td>1.677722e+07</td>\n",
+       "      <td>1.006633e+08</td>\n",
        "      <td>8.388608e+06</td>\n",
+       "      <td>4.194304e+06</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>1000.0</td>\n",
        "      <td>Stream_TRIAD</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>58</td>\n",
        "      <td>regionprofile</td>\n",
        "      <td>0.063563</td>\n",
@@ -5051,13 +5051,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cccf3558",
+   "id": "2090c1a7",
    "metadata": {
     "papermill": {
-     "duration": 0.003885,
-     "end_time": "2023-12-09T20:58:34.575446",
+     "duration": 0.004294,
+     "end_time": "2023-12-22T05:35:57.314072",
      "exception": false,
-     "start_time": "2023-12-09T20:58:34.571561",
+     "start_time": "2023-12-22T05:35:57.309778",
      "status": "completed"
     },
     "tags": []
@@ -5070,19 +5070,19 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "e9cb4b5f",
+   "id": "cabcf904",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:34.583743Z",
-     "iopub.status.busy": "2023-12-09T20:58:34.583638Z",
-     "iopub.status.idle": "2023-12-09T20:58:34.585614Z",
-     "shell.execute_reply": "2023-12-09T20:58:34.585414Z"
+     "iopub.execute_input": "2023-12-22T05:35:57.322635Z",
+     "iopub.status.busy": "2023-12-22T05:35:57.322524Z",
+     "iopub.status.idle": "2023-12-22T05:35:57.324510Z",
+     "shell.execute_reply": "2023-12-22T05:35:57.324219Z"
     },
     "papermill": {
-     "duration": 0.006854,
-     "end_time": "2023-12-09T20:58:34.586178",
+     "duration": 0.006943,
+     "end_time": "2023-12-22T05:35:57.325027",
      "exception": false,
-     "start_time": "2023-12-09T20:58:34.579324",
+     "start_time": "2023-12-22T05:35:57.318084",
      "status": "completed"
     },
     "tags": []
@@ -5100,19 +5100,19 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "48156875",
+   "id": "63680a96",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:34.594569Z",
-     "iopub.status.busy": "2023-12-09T20:58:34.594488Z",
-     "iopub.status.idle": "2023-12-09T20:58:34.826670Z",
-     "shell.execute_reply": "2023-12-09T20:58:34.826360Z"
+     "iopub.execute_input": "2023-12-22T05:35:57.333669Z",
+     "iopub.status.busy": "2023-12-22T05:35:57.333567Z",
+     "iopub.status.idle": "2023-12-22T05:35:57.607950Z",
+     "shell.execute_reply": "2023-12-22T05:35:57.607605Z"
     },
     "papermill": {
-     "duration": 0.23707,
-     "end_time": "2023-12-09T20:58:34.827334",
+     "duration": 0.279856,
+     "end_time": "2023-12-22T05:35:57.608770",
      "exception": false,
-     "start_time": "2023-12-09T20:58:34.590264",
+     "start_time": "2023-12-22T05:35:57.328914",
      "status": "completed"
     },
     "tags": []
@@ -5128,19 +5128,19 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "6e370f6f",
+   "id": "3b42351d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:34.836818Z",
-     "iopub.status.busy": "2023-12-09T20:58:34.836707Z",
-     "iopub.status.idle": "2023-12-09T20:58:34.868107Z",
-     "shell.execute_reply": "2023-12-09T20:58:34.867851Z"
+     "iopub.execute_input": "2023-12-22T05:35:57.618402Z",
+     "iopub.status.busy": "2023-12-22T05:35:57.618297Z",
+     "iopub.status.idle": "2023-12-22T05:35:57.653609Z",
+     "shell.execute_reply": "2023-12-22T05:35:57.653292Z"
     },
     "papermill": {
-     "duration": 0.036826,
-     "end_time": "2023-12-09T20:58:34.868742",
+     "duration": 0.040564,
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      "exception": false,
-     "start_time": "2023-12-09T20:58:34.831916",
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      "status": "completed"
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     "tags": []
@@ -5156,13 +5156,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3aad4590",
+   "id": "2c6e41ca",
    "metadata": {
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-     "end_time": "2023-12-09T20:58:34.876890",
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      "exception": false,
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      "status": "completed"
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     "tags": []
@@ -5174,19 +5174,19 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "a792befd",
+   "id": "40e9c7a0",
    "metadata": {
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-     "iopub.status.busy": "2023-12-09T20:58:34.885258Z",
-     "iopub.status.idle": "2023-12-09T20:58:35.014785Z",
-     "shell.execute_reply": "2023-12-09T20:58:35.014524Z"
+     "iopub.execute_input": "2023-12-22T05:35:57.671999Z",
+     "iopub.status.busy": "2023-12-22T05:35:57.671884Z",
+     "iopub.status.idle": "2023-12-22T05:35:57.818231Z",
+     "shell.execute_reply": "2023-12-22T05:35:57.817926Z"
     },
     "papermill": {
-     "duration": 0.135609,
-     "end_time": "2023-12-09T20:58:35.016535",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:34.880926",
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      "status": "completed"
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     "tags": []
@@ -20414,13 +20414,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73c04f6c",
+   "id": "b254b545",
    "metadata": {
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-     "duration": 0.008872,
-     "end_time": "2023-12-09T20:58:35.034762",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:35.025890",
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      "status": "completed"
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     "tags": []
@@ -20436,19 +20436,19 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "db5f20d0",
+   "id": "478bd23e",
    "metadata": {
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-     "shell.execute_reply": "2023-12-09T20:58:35.057357Z"
+     "iopub.execute_input": "2023-12-22T05:35:57.859152Z",
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     },
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-     "end_time": "2023-12-09T20:58:35.058077",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:35.043687",
+     "start_time": "2023-12-22T05:35:57.849416",
      "status": "completed"
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     "scrolled": true,
@@ -20533,7 +20533,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>2.9.0-dev</td>\n",
        "      <td>min#inclusive#sum#time.duration,max#inclusive#sum#time.duration,avg#inclusive#sum#time.duration,sum#inclusive#sum#time.duration,any#any#max#BlockSize,any#any#max#Bytes/Rep,any#any#max#Flops/Rep,any#any#max#Iterations/Rep,any#any#max#Kernels/Rep,any#any#max#ProblemSize,any#any#max#Reps</td>\n",
        "      <td></td>\n",
@@ -20568,7 +20568,7 @@
        "      <td>block_128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-4950540859942973586</th>\n",
+       "      <th>1246693022946911723</th>\n",
        "      <td>2.9.0-dev</td>\n",
        "      <td>min#inclusive#sum#time.duration,max#inclusive#sum#time.duration,avg#inclusive#sum#time.duration,sum#inclusive#sum#time.duration,any#any#max#BlockSize,any#any#max#Bytes/Rep,any#any#max#Flops/Rep,any#any#max#Iterations/Rep,any#any#max#Kernels/Rep,any#any#max#ProblemSize,any#any#max#Reps</td>\n",
        "      <td></td>\n",
@@ -20577,9 +20577,9 @@
        "      <td>regionprofile</td>\n",
        "      <td>spot</td>\n",
        "      <td>mckinsey</td>\n",
-       "      <td>1691446876</td>\n",
+       "      <td>1691449546</td>\n",
        "      <td>[/lib64/libdl.so.2, /lib64/libpthread.so.0, /lib64/librt.so.1, /usr/workspace/asde/caliper-lassen/lib64/libcaliper.so.2, /usr/workspace/wsb/asde/adiak-lassen/lib/libadiak.so, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libmpiprofilesupport.so.3, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libmpi_ibm.so.3, /usr/tce/packages/gcc/gcc-4.9.3/lib64/libstdc++.so.6, /lib64/libm.so.6, /usr/tce/packages/clang/clang-10.0.1/release/lib/libomp.so, /usr/tce/packages/gcc/gcc-4.9.3/lib64/libgcc_s.so.1, /lib64/libc.so.6, /lib64/ld64.so.2, /usr/tce/packages/cuda/cuda-10.1.243/nvidia/lib64/libnvToolsExt.so.1, /usr/tce/packages/cuda/cuda-10.1.243/bin/../extras/CUPTI/lib64/libcupti.so.10.1, /lib64/libpapi.so.5.2.0.0, /lib64/libdw.so.1, /lib64/libunwind.so.8, /usr/tce/packages/xl/xl-2021.09.22/lib/libibmc++.so.1, /lib64/libutil.so.1, /lib64/libz.so.1, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libhwloc_ompi.so.15, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libevent-2.1.so.6, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libevent_pthreads-2.1.so.6, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libopen-rte.so.3, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libopen-pal.so.3, /lib64/libpfm.so.4, /lib64/libsensors.so.4, /lib64/libelf.so.1, /lib64/liblzma.so.5, /lib64/libbz2.so.1, /usr/tce/packages/clang/clang-10.0.1/release/lib/libarcher.so, /lib64/libnss_files.so.2]</td>\n",
-       "      <td>[../bin/nvcc10.2.89_clang10.0.1.exe, --variants, Base_CUDA, RAJA_CUDA, --tunings, block_128, block_256, block_512, block_1024, library, --size, 4194304, --outdir, /usr/workspace/thicket/rajaperf-july-2023/lassen/clang10.0.1_nvcc10.2.89_4194304/1, -sp]</td>\n",
+       "      <td>[../bin/nvcc10.2.89_clang10.0.1.exe, --variants, Base_CUDA, RAJA_CUDA, --tunings, block_128, block_256, block_512, block_1024, library, --size, 8388608, --outdir, /usr/workspace/thicket/rajaperf-july-2023/lassen/clang10.0.1_nvcc10.2.89_8388608/1, -sp]</td>\n",
        "      <td>lassen</td>\n",
        "      <td>1.10.0</td>\n",
        "      <td>2022.10.5</td>\n",
@@ -20595,7 +20595,7 @@
        "      <td>128,256,512,1024</td>\n",
        "      <td>blueos_3_ppc64le_ib_p9</td>\n",
        "      <td>lassen709</td>\n",
-       "      <td>4194304.0</td>\n",
+       "      <td>8388608.0</td>\n",
        "      <td>Direct</td>\n",
        "      <td>4.5</td>\n",
        "      <td>20</td>\n",
@@ -20603,7 +20603,7 @@
        "      <td>block_128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-3550476907752802047</th>\n",
+       "      <th>4737801908790309618</th>\n",
        "      <td>2.9.0-dev</td>\n",
        "      <td>min#inclusive#sum#time.duration,max#inclusive#sum#time.duration,avg#inclusive#sum#time.duration,sum#inclusive#sum#time.duration,any#any#max#BlockSize,any#any#max#Bytes/Rep,any#any#max#Flops/Rep,any#any#max#Iterations/Rep,any#any#max#Kernels/Rep,any#any#max#ProblemSize,any#any#max#Reps</td>\n",
        "      <td></td>\n",
@@ -20612,9 +20612,9 @@
        "      <td>regionprofile</td>\n",
        "      <td>spot</td>\n",
        "      <td>mckinsey</td>\n",
-       "      <td>1691449546</td>\n",
+       "      <td>1691446876</td>\n",
        "      <td>[/lib64/libdl.so.2, /lib64/libpthread.so.0, /lib64/librt.so.1, /usr/workspace/asde/caliper-lassen/lib64/libcaliper.so.2, /usr/workspace/wsb/asde/adiak-lassen/lib/libadiak.so, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libmpiprofilesupport.so.3, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libmpi_ibm.so.3, /usr/tce/packages/gcc/gcc-4.9.3/lib64/libstdc++.so.6, /lib64/libm.so.6, /usr/tce/packages/clang/clang-10.0.1/release/lib/libomp.so, /usr/tce/packages/gcc/gcc-4.9.3/lib64/libgcc_s.so.1, /lib64/libc.so.6, /lib64/ld64.so.2, /usr/tce/packages/cuda/cuda-10.1.243/nvidia/lib64/libnvToolsExt.so.1, /usr/tce/packages/cuda/cuda-10.1.243/bin/../extras/CUPTI/lib64/libcupti.so.10.1, /lib64/libpapi.so.5.2.0.0, /lib64/libdw.so.1, /lib64/libunwind.so.8, /usr/tce/packages/xl/xl-2021.09.22/lib/libibmc++.so.1, /lib64/libutil.so.1, /lib64/libz.so.1, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libhwloc_ompi.so.15, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libevent-2.1.so.6, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libevent_pthreads-2.1.so.6, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libopen-rte.so.3, /usr/tce/packages/spectrum-mpi/ibm/spectrum-mpi-rolling-release/lib/libopen-pal.so.3, /lib64/libpfm.so.4, /lib64/libsensors.so.4, /lib64/libelf.so.1, /lib64/liblzma.so.5, /lib64/libbz2.so.1, /usr/tce/packages/clang/clang-10.0.1/release/lib/libarcher.so, /lib64/libnss_files.so.2]</td>\n",
-       "      <td>[../bin/nvcc10.2.89_clang10.0.1.exe, --variants, Base_CUDA, RAJA_CUDA, --tunings, block_128, block_256, block_512, block_1024, library, --size, 8388608, --outdir, /usr/workspace/thicket/rajaperf-july-2023/lassen/clang10.0.1_nvcc10.2.89_8388608/1, -sp]</td>\n",
+       "      <td>[../bin/nvcc10.2.89_clang10.0.1.exe, --variants, Base_CUDA, RAJA_CUDA, --tunings, block_128, block_256, block_512, block_1024, library, --size, 4194304, --outdir, /usr/workspace/thicket/rajaperf-july-2023/lassen/clang10.0.1_nvcc10.2.89_4194304/1, -sp]</td>\n",
        "      <td>lassen</td>\n",
        "      <td>1.10.0</td>\n",
        "      <td>2022.10.5</td>\n",
@@ -20630,7 +20630,7 @@
        "      <td>128,256,512,1024</td>\n",
        "      <td>blueos_3_ppc64le_ib_p9</td>\n",
        "      <td>lassen709</td>\n",
-       "      <td>8388608.0</td>\n",
+       "      <td>4194304.0</td>\n",
        "      <td>Direct</td>\n",
        "      <td>4.5</td>\n",
        "      <td>20</td>\n",
@@ -20638,7 +20638,7 @@
        "      <td>block_128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2970602217065607616</th>\n",
+       "      <th>5829755423718090206</th>\n",
        "      <td>2.9.0-dev</td>\n",
        "      <td>min#inclusive#sum#time.duration,max#inclusive#sum#time.duration,avg#inclusive#sum#time.duration,sum#inclusive#sum#time.duration,any#any#max#BlockSize,any#any#max#Bytes/Rep,any#any#max#Flops/Rep,any#any#max#Iterations/Rep,any#any#max#Kernels/Rep,any#any#max#ProblemSize,any#any#max#Reps</td>\n",
        "      <td></td>\n",
@@ -20689,13 +20689,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c05a311",
+   "id": "afaf63ad",
    "metadata": {
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-     "end_time": "2023-12-09T20:58:35.075430",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:35.066972",
+     "start_time": "2023-12-22T05:35:57.873842",
      "status": "completed"
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     "tags": []
@@ -20709,19 +20709,19 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "6b4af5d5",
+   "id": "ce30dabd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:35.093381Z",
-     "iopub.status.busy": "2023-12-09T20:58:35.093266Z",
-     "iopub.status.idle": "2023-12-09T20:58:35.099594Z",
-     "shell.execute_reply": "2023-12-09T20:58:35.099392Z"
+     "iopub.execute_input": "2023-12-22T05:35:57.901770Z",
+     "iopub.status.busy": "2023-12-22T05:35:57.901657Z",
+     "iopub.status.idle": "2023-12-22T05:35:57.908107Z",
+     "shell.execute_reply": "2023-12-22T05:35:57.907805Z"
     },
     "papermill": {
-     "duration": 0.015763,
-     "end_time": "2023-12-09T20:58:35.100128",
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+     "end_time": "2023-12-22T05:35:57.908649",
      "exception": false,
-     "start_time": "2023-12-09T20:58:35.084365",
+     "start_time": "2023-12-22T05:35:57.892367",
      "status": "completed"
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     "tags": []
@@ -20805,7 +20805,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>-8213322894603097307</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>2.9.0-dev</td>\n",
        "      <td>min#inclusive#sum#time.duration,max#inclusive#sum#time.duration,avg#inclusive#sum#time.duration,sum#inclusive#sum#time.duration,any#any#max#BlockSize,any#any#max#Bytes/Rep,any#any#max#Flops/Rep,any#any#max#Iterations/Rep,any#any#max#Kernels/Rep,any#any#max#ProblemSize,any#any#max#Reps</td>\n",
        "      <td></td>\n",
@@ -20837,10 +20837,10 @@
        "      <td>4.5</td>\n",
        "      <td>20</td>\n",
        "      <td>Base_CUDA</td>\n",
-       "      <td>block_256</td>\n",
+       "      <td>block_128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-420726814338077851</th>\n",
        "      <td>2.9.0-dev</td>\n",
        "      <td>min#inclusive#sum#time.duration,max#inclusive#sum#time.duration,avg#inclusive#sum#time.duration,sum#inclusive#sum#time.duration,any#any#max#BlockSize,any#any#max#Bytes/Rep,any#any#max#Flops/Rep,any#any#max#Iterations/Rep,any#any#max#Kernels/Rep,any#any#max#ProblemSize,any#any#max#Reps</td>\n",
        "      <td></td>\n",
@@ -20872,7 +20872,7 @@
        "      <td>4.5</td>\n",
        "      <td>20</td>\n",
        "      <td>Base_CUDA</td>\n",
-       "      <td>block_128</td>\n",
+       "      <td>block_256</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
@@ -20894,13 +20894,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21903265",
+   "id": "c4361b92",
    "metadata": {
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      "exception": false,
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      "status": "completed"
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     "tags": []
@@ -20914,19 +20914,19 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "91facd3d",
+   "id": "8f3aaa83",
    "metadata": {
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-     "iopub.status.idle": "2023-12-09T20:58:35.157333Z",
-     "shell.execute_reply": "2023-12-09T20:58:35.157092Z"
+     "iopub.execute_input": "2023-12-22T05:35:57.946431Z",
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     },
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      "exception": false,
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      "status": "completed"
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@@ -21010,7 +21010,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>-5686358164659988175</th>\n",
+       "      <th>-6232983296743574852</th>\n",
        "      <td>2.9.0-dev</td>\n",
        "      <td>min#inclusive#sum#time.duration,max#inclusive#...</td>\n",
        "      <td></td>\n",
@@ -21041,43 +21041,43 @@
       "text/plain": [
        "                     cali.caliper.version  \\\n",
        "profile                                     \n",
-       "-5686358164659988175            2.9.0-dev   \n",
+       "-6232983296743574852            2.9.0-dev   \n",
        "\n",
        "                                                           spot.metrics  \\\n",
        "profile                                                                   \n",
-       "-5686358164659988175  min#inclusive#sum#time.duration,max#inclusive#...   \n",
+       "-6232983296743574852  min#inclusive#sum#time.duration,max#inclusive#...   \n",
        "\n",
        "                     spot.timeseries.metrics  spot.format.version  \\\n",
        "profile                                                             \n",
-       "-5686358164659988175                                            2   \n",
+       "-6232983296743574852                                            2   \n",
        "\n",
        "                     spot.options  spot.channels cali.channel      user  \\\n",
        "profile                                                                   \n",
-       "-5686358164659988175               regionprofile         spot  mckinsey   \n",
+       "-6232983296743574852               regionprofile         spot  mckinsey   \n",
        "\n",
        "                      launchdate  \\\n",
        "profile                            \n",
-       "-5686358164659988175  1691446615   \n",
+       "-6232983296743574852  1691446615   \n",
        "\n",
        "                                                              libraries  ...  \\\n",
        "profile                                                                  ...   \n",
-       "-5686358164659988175  [/lib64/libdl.so.2, /lib64/libpthread.so.0, /l...  ...   \n",
+       "-6232983296743574852  [/lib64/libdl.so.2, /lib64/libpthread.so.0, /l...  ...   \n",
        "\n",
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        "profile                                                              \n",
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@@ -21940,13 +21932,13 @@
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@@ -21962,19 +21954,19 @@
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@@ -22792,13 +22784,13 @@
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-     "iopub.status.busy": "2023-12-09T20:58:35.441843Z",
-     "iopub.status.idle": "2023-12-09T20:58:35.447101Z",
-     "shell.execute_reply": "2023-12-09T20:58:35.446873Z"
+     "iopub.execute_input": "2023-12-22T05:35:58.277357Z",
+     "iopub.status.busy": "2023-12-22T05:35:58.277232Z",
+     "iopub.status.idle": "2023-12-22T05:35:58.282880Z",
+     "shell.execute_reply": "2023-12-22T05:35:58.282660Z"
     },
     "papermill": {
-     "duration": 0.016,
-     "end_time": "2023-12-09T20:58:35.447589",
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      "exception": false,
-     "start_time": "2023-12-09T20:58:35.431589",
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      "status": "completed"
     },
     "tags": []
@@ -23458,13 +23450,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7389ebce",
+   "id": "d1d7b9e3",
    "metadata": {
     "papermill": {
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-     "end_time": "2023-12-09T20:58:35.468815",
+     "duration": 0.01124,
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      "exception": false,
-     "start_time": "2023-12-09T20:58:35.458255",
+     "start_time": "2023-12-22T05:35:58.293708",
      "status": "completed"
     },
     "tags": []
@@ -23480,19 +23472,19 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "a9550e8a",
+   "id": "883eed2c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:35.490431Z",
-     "iopub.status.busy": "2023-12-09T20:58:35.490312Z",
-     "iopub.status.idle": "2023-12-09T20:58:35.495635Z",
-     "shell.execute_reply": "2023-12-09T20:58:35.495387Z"
+     "iopub.execute_input": "2023-12-22T05:35:58.327088Z",
+     "iopub.status.busy": "2023-12-22T05:35:58.326955Z",
+     "iopub.status.idle": "2023-12-22T05:35:58.332679Z",
+     "shell.execute_reply": "2023-12-22T05:35:58.332409Z"
     },
     "papermill": {
-     "duration": 0.016576,
-     "end_time": "2023-12-09T20:58:35.496146",
+     "duration": 0.017445,
+     "end_time": "2023-12-22T05:35:58.333245",
      "exception": false,
-     "start_time": "2023-12-09T20:58:35.479570",
+     "start_time": "2023-12-22T05:35:58.315800",
      "status": "completed"
     },
     "tags": []
@@ -23592,13 +23584,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ccbd22b",
+   "id": "6bda321d",
    "metadata": {
     "papermill": {
-     "duration": 0.010065,
-     "end_time": "2023-12-09T20:58:35.516705",
+     "duration": 0.010981,
+     "end_time": "2023-12-22T05:35:58.354249",
      "exception": false,
-     "start_time": "2023-12-09T20:58:35.506640",
+     "start_time": "2023-12-22T05:35:58.343268",
      "status": "completed"
     },
     "tags": []
@@ -23614,19 +23606,19 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "1fbc648b",
+   "id": "58f98758",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:35.538260Z",
-     "iopub.status.busy": "2023-12-09T20:58:35.538154Z",
-     "iopub.status.idle": "2023-12-09T20:58:35.608057Z",
-     "shell.execute_reply": "2023-12-09T20:58:35.607788Z"
+     "iopub.execute_input": "2023-12-22T05:35:58.375962Z",
+     "iopub.status.busy": "2023-12-22T05:35:58.375843Z",
+     "iopub.status.idle": "2023-12-22T05:35:58.448955Z",
+     "shell.execute_reply": "2023-12-22T05:35:58.448567Z"
     },
     "papermill": {
-     "duration": 0.081656,
-     "end_time": "2023-12-09T20:58:35.608607",
+     "duration": 0.084827,
+     "end_time": "2023-12-22T05:35:58.449514",
      "exception": false,
-     "start_time": "2023-12-09T20:58:35.526951",
+     "start_time": "2023-12-22T05:35:58.364687",
      "status": "completed"
     },
     "tags": []
@@ -23699,13 +23691,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96c631c6",
+   "id": "392d764d",
    "metadata": {
     "papermill": {
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-     "end_time": "2023-12-09T20:58:35.629536",
+     "duration": 0.011158,
+     "end_time": "2023-12-22T05:35:58.472746",
      "exception": false,
-     "start_time": "2023-12-09T20:58:35.619241",
+     "start_time": "2023-12-22T05:35:58.461588",
      "status": "completed"
     },
     "tags": []
@@ -23721,19 +23713,19 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "f9f1bb08",
+   "id": "93a1efae",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:35.651208Z",
-     "iopub.status.busy": "2023-12-09T20:58:35.651108Z",
-     "iopub.status.idle": "2023-12-09T20:58:35.705497Z",
-     "shell.execute_reply": "2023-12-09T20:58:35.705217Z"
+     "iopub.execute_input": "2023-12-22T05:35:58.494755Z",
+     "iopub.status.busy": "2023-12-22T05:35:58.494627Z",
+     "iopub.status.idle": "2023-12-22T05:35:58.553616Z",
+     "shell.execute_reply": "2023-12-22T05:35:58.553244Z"
     },
     "papermill": {
-     "duration": 0.065723,
-     "end_time": "2023-12-09T20:58:35.706073",
+     "duration": 0.070682,
+     "end_time": "2023-12-22T05:35:58.554235",
      "exception": false,
-     "start_time": "2023-12-09T20:58:35.640350",
+     "start_time": "2023-12-22T05:35:58.483553",
      "status": "completed"
     },
     "tags": []
@@ -23799,13 +23791,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "258892d6",
+   "id": "2cccfe48",
    "metadata": {
     "papermill": {
-     "duration": 0.010229,
-     "end_time": "2023-12-09T20:58:35.726809",
+     "duration": 0.011955,
+     "end_time": "2023-12-22T05:35:58.576657",
      "exception": false,
-     "start_time": "2023-12-09T20:58:35.716580",
+     "start_time": "2023-12-22T05:35:58.564702",
      "status": "completed"
     },
     "tags": []
@@ -23830,19 +23822,19 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "530cc687",
+   "id": "a0ebbf42",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:35.748490Z",
-     "iopub.status.busy": "2023-12-09T20:58:35.748362Z",
-     "iopub.status.idle": "2023-12-09T20:58:35.750669Z",
-     "shell.execute_reply": "2023-12-09T20:58:35.750421Z"
+     "iopub.execute_input": "2023-12-22T05:35:58.614162Z",
+     "iopub.status.busy": "2023-12-22T05:35:58.614005Z",
+     "iopub.status.idle": "2023-12-22T05:35:58.617423Z",
+     "shell.execute_reply": "2023-12-22T05:35:58.617072Z"
     },
     "papermill": {
-     "duration": 0.013861,
-     "end_time": "2023-12-09T20:58:35.751167",
+     "duration": 0.017326,
+     "end_time": "2023-12-22T05:35:58.618022",
      "exception": false,
-     "start_time": "2023-12-09T20:58:35.737306",
+     "start_time": "2023-12-22T05:35:58.600696",
      "status": "completed"
     },
     "tags": []
@@ -23855,19 +23847,19 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "f0e55734",
+   "id": "92f03c64",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:35.772939Z",
-     "iopub.status.busy": "2023-12-09T20:58:35.772839Z",
-     "iopub.status.idle": "2023-12-09T20:58:35.868193Z",
-     "shell.execute_reply": "2023-12-09T20:58:35.867550Z"
+     "iopub.execute_input": "2023-12-22T05:35:58.641212Z",
+     "iopub.status.busy": "2023-12-22T05:35:58.641089Z",
+     "iopub.status.idle": "2023-12-22T05:35:58.773440Z",
+     "shell.execute_reply": "2023-12-22T05:35:58.773100Z"
     },
     "papermill": {
-     "duration": 0.107673,
-     "end_time": "2023-12-09T20:58:35.869482",
+     "duration": 0.144613,
+     "end_time": "2023-12-22T05:35:58.774075",
      "exception": false,
-     "start_time": "2023-12-09T20:58:35.761809",
+     "start_time": "2023-12-22T05:35:58.629462",
      "status": "completed"
     },
     "tags": []
@@ -23876,7 +23868,7 @@
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0xffff3c6fd730>"
+       "<seaborn.axisgrid.FacetGrid at 0xffff48c6b970>"
       ]
      },
      "execution_count": 22,
@@ -23902,13 +23894,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0aaeda5",
+   "id": "41fbd290",
    "metadata": {
     "papermill": {
-     "duration": 0.010987,
-     "end_time": "2023-12-09T20:58:35.891174",
+     "duration": 0.012189,
+     "end_time": "2023-12-22T05:35:58.797210",
      "exception": false,
-     "start_time": "2023-12-09T20:58:35.880187",
+     "start_time": "2023-12-22T05:35:58.785021",
      "status": "completed"
     },
     "tags": []
@@ -23930,19 +23922,19 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "6dd80fc4",
+   "id": "34927c7a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-09T20:58:35.913758Z",
-     "iopub.status.busy": "2023-12-09T20:58:35.913643Z",
-     "iopub.status.idle": "2023-12-09T20:58:36.053132Z",
-     "shell.execute_reply": "2023-12-09T20:58:36.052829Z"
+     "iopub.execute_input": "2023-12-22T05:35:58.820269Z",
+     "iopub.status.busy": "2023-12-22T05:35:58.820146Z",
+     "iopub.status.idle": "2023-12-22T05:35:58.974177Z",
+     "shell.execute_reply": "2023-12-22T05:35:58.973839Z"
     },
     "papermill": {
-     "duration": 0.151175,
-     "end_time": "2023-12-09T20:58:36.053706",
+     "duration": 0.166385,
+     "end_time": "2023-12-22T05:35:58.974976",
      "exception": false,
-     "start_time": "2023-12-09T20:58:35.902531",
+     "start_time": "2023-12-22T05:35:58.808591",
      "status": "completed"
     },
     "tags": []
@@ -23998,14 +23990,14 @@
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 3.341359,
-   "end_time": "2023-12-09T20:58:36.380154",
+   "duration": 3.435857,
+   "end_time": "2023-12-22T05:35:59.293375",
    "environment_variables": {},
    "exception": null,
    "input_path": "01_thicket_tutorial.ipynb",
    "output_path": "01_thicket_tutorial.ipynb",
    "parameters": {},
-   "start_time": "2023-12-09T20:58:33.038795",
+   "start_time": "2023-12-22T05:35:55.857518",
    "version": "2.5.0"
   }
  },
diff --git a/docs/tutorial_materials.rst b/docs/tutorial_materials.rst
index 87ae0332..1e581ec9 100644
--- a/docs/tutorial_materials.rst
+++ b/docs/tutorial_materials.rst
@@ -24,13 +24,5 @@ August 14, 2023, alongside Caliper.
 We provide scripts that take you through some of the available features in
 Thicket. They correspond to sections in the slides above.
 
-To run through the scripts, we provide a docker image within the instance.
-After logging on to an instance, you can invoke the following:
-
-.. code:: console
-
-   $ docker run -p 8888:8888 myimage
-
-Then, find the URL in the docker output, copy the URL into browser, and replace
-``127.0.0.1`` (localhost) in the URL with InstanceIP. It will look similar to:
-``http://<InstanceIP>:8888/?token=9f60c09dcb63a0c6cb9d9e2a436ee541beabf83e67aadcde``.
+To run through the scripts, you can follow the instructions to build the docker
+image in `thicket-tutorial <https://github.com/llnl/thicket-tutorial>`_.