profiling/profiling.ipynb

   1 {
   2  "cells": [
   3   {
   4    "cell_type": "code",
   5    "execution_count": 1,
   6    "metadata": {
   7     "Collapsed": "false"
   8    },
   9    "outputs": [],
  10    "source": [
  11     "import glob\n",
  12     "import json\n",
  13     "import pandas as pd\n",
  14     "import numpy as np\n",
  15     "\n",
  16     "import matplotlib.pyplot as plt\n",
  17     "%matplotlib inline"
  18    ]
  19   },
  20   {
  21    "cell_type": "code",
  22    "execution_count": null,
  23    "metadata": {
  24     "Collapsed": "false"
  25    },
  26    "outputs": [],
  27    "source": [
  28     "! for i in {01..25}; do stack build --executable-profiling  --ghc-options=\"-O2 -threaded -fprof-auto -rtsopts\" advent${i} ; stack exec --profile -- advent${i} +RTS -pj ; done\n"
  29    ]
  30   },
  31   {
  32    "cell_type": "code",
  33    "execution_count": 3,
  34    "metadata": {
  35     "Collapsed": "false"
  36    },
  37    "outputs": [
  38     {
  39      "data": {
  40       "text/plain": [
  41        "25"
  42       ]
  43      },
  44      "execution_count": 3,
  45      "metadata": {},
  46      "output_type": "execute_result"
  47     }
  48    ],
  49    "source": [
  50     "len(glob.glob('*prof'))"
  51    ]
  52   },
  53   {
  54    "cell_type": "code",
  55    "execution_count": 6,
  56    "metadata": {
  57     "Collapsed": "false",
  58     "scrolled": true
  59    },
  60    "outputs": [
  61     {
  62      "data": {
  63       "text/plain": [
  64        "[{'program': 'advent01',\n",
  65        "  'total_time': 0.48,\n",
  66        "  'total_alloc': 107029176,\n",
  67        "  'total_ticks': 1644,\n",
  68        "  'initial_capabilities': 12},\n",
  69        " {'program': 'advent16',\n",
  70        "  'total_time': 0.15,\n",
  71        "  'total_alloc': 17242880,\n",
  72        "  'total_ticks': 528,\n",
  73        "  'initial_capabilities': 12},\n",
  74        " {'program': 'advent23',\n",
  75        "  'total_time': 72.61,\n",
  76        "  'total_alloc': 10263690000,\n",
  77        "  'total_ticks': 247608,\n",
  78        "  'initial_capabilities': 12},\n",
  79        " {'program': 'advent15loop',\n",
  80        "  'total_time': 42.69,\n",
  81        "  'total_alloc': 8838275776,\n",
  82        "  'total_ticks': 145584,\n",
  83        "  'initial_capabilities': 12},\n",
  84        " {'program': 'advent24',\n",
  85        "  'total_time': 26.45,\n",
  86        "  'total_alloc': 4352105528,\n",
  87        "  'total_ticks': 90180,\n",
  88        "  'initial_capabilities': 12},\n",
  89        " {'program': 'advent21',\n",
  90        "  'total_time': 0.15,\n",
  91        "  'total_alloc': 9561880,\n",
  92        "  'total_ticks': 528,\n",
  93        "  'initial_capabilities': 12},\n",
  94        " {'program': 'advent19',\n",
  95        "  'total_time': 0.24,\n",
  96        "  'total_alloc': 44456496,\n",
  97        "  'total_ticks': 816,\n",
  98        "  'initial_capabilities': 12},\n",
  99        " {'program': 'advent08',\n",
 100        "  'total_time': 0.4,\n",
 101        "  'total_alloc': 74894192,\n",
 102        "  'total_ticks': 1356,\n",
 103        "  'initial_capabilities': 12},\n",
 104        " {'program': 'advent12',\n",
 105        "  'total_time': 0.03,\n",
 106        "  'total_alloc': 2206400,\n",
 107        "  'total_ticks': 108,\n",
 108        "  'initial_capabilities': 12},\n",
 109        " {'program': 'advent20',\n",
 110        "  'total_time': 34.74,\n",
 111        "  'total_alloc': 3860804096,\n",
 112        "  'total_ticks': 118476,\n",
 113        "  'initial_capabilities': 12},\n",
 114        " {'program': 'advent04',\n",
 115        "  'total_time': 0.44,\n",
 116        "  'total_alloc': 60820368,\n",
 117        "  'total_ticks': 1512,\n",
 118        "  'initial_capabilities': 12},\n",
 119        " {'program': 'advent09',\n",
 120        "  'total_time': 3.5,\n",
 121        "  'total_alloc': 793279616,\n",
 122        "  'total_ticks': 11928,\n",
 123        "  'initial_capabilities': 12},\n",
 124        " {'program': 'advent10',\n",
 125        "  'total_time': 0.01,\n",
 126        "  'total_alloc': 924456,\n",
 127        "  'total_ticks': 36,\n",
 128        "  'initial_capabilities': 12},\n",
 129        " {'program': 'advent13',\n",
 130        "  'total_time': 0.01,\n",
 131        "  'total_alloc': 542152,\n",
 132        "  'total_ticks': 36,\n",
 133        "  'initial_capabilities': 12},\n",
 134        " {'program': 'advent02',\n",
 135        "  'total_time': 0.26,\n",
 136        "  'total_alloc': 35370072,\n",
 137        "  'total_ticks': 876,\n",
 138        "  'initial_capabilities': 12},\n",
 139        " {'program': 'advent06',\n",
 140        "  'total_time': 0.11,\n",
 141        "  'total_alloc': 11624856,\n",
 142        "  'total_ticks': 372,\n",
 143        "  'initial_capabilities': 12},\n",
 144        " {'program': 'advent15',\n",
 145        "  'total_time': 43.83,\n",
 146        "  'total_alloc': 6662932672,\n",
 147        "  'total_ticks': 149460,\n",
 148        "  'initial_capabilities': 12},\n",
 149        " {'program': 'advent17',\n",
 150        "  'total_time': 21.33,\n",
 151        "  'total_alloc': 4808712520,\n",
 152        "  'total_ticks': 72744,\n",
 153        "  'initial_capabilities': 12},\n",
 154        " {'program': 'advent14',\n",
 155        "  'total_time': 1.66,\n",
 156        "  'total_alloc': 259113488,\n",
 157        "  'total_ticks': 5676,\n",
 158        "  'initial_capabilities': 12},\n",
 159        " {'program': 'advent18',\n",
 160        "  'total_time': 0.51,\n",
 161        "  'total_alloc': 21509984,\n",
 162        "  'total_ticks': 1728,\n",
 163        "  'initial_capabilities': 12},\n",
 164        " {'program': 'advent25',\n",
 165        "  'total_time': 0.35,\n",
 166        "  'total_alloc': 39231576,\n",
 167        "  'total_ticks': 1200,\n",
 168        "  'initial_capabilities': 12},\n",
 169        " {'program': 'advent11',\n",
 170        "  'total_time': 112.58,\n",
 171        "  'total_alloc': 35282262592,\n",
 172        "  'total_ticks': 383916,\n",
 173        "  'initial_capabilities': 12},\n",
 174        " {'program': 'advent07',\n",
 175        "  'total_time': 0.33,\n",
 176        "  'total_alloc': 21605440,\n",
 177        "  'total_ticks': 1128,\n",
 178        "  'initial_capabilities': 12},\n",
 179        " {'program': 'advent03',\n",
 180        "  'total_time': 0.06,\n",
 181        "  'total_alloc': 4017640,\n",
 182        "  'total_ticks': 192,\n",
 183        "  'initial_capabilities': 12},\n",
 184        " {'program': 'advent22',\n",
 185        "  'total_time': 22.64,\n",
 186        "  'total_alloc': 3242847728,\n",
 187        "  'total_ticks': 77208,\n",
 188        "  'initial_capabilities': 12},\n",
 189        " {'program': 'advent05',\n",
 190        "  'total_time': 0.09,\n",
 191        "  'total_alloc': 27810256,\n",
 192        "  'total_ticks': 312,\n",
 193        "  'initial_capabilities': 12}]"
 194       ]
 195      },
 196      "execution_count": 6,
 197      "metadata": {},
 198      "output_type": "execute_result"
 199     }
 200    ],
 201    "source": [
 202     "profs = []\n",
 203     "for fn in glob.glob('*prof'):\n",
 204     "    with open(fn) as f:\n",
 205     "        j = json.load(f)\n",
 206     "        prof = {}\n",
 207     "        for n in 'program total_time total_alloc total_ticks initial_capabilities'.split():\n",
 208     "            prof[n] = j[n]\n",
 209     "        profs.append(prof)\n",
 210     "profs"
 211    ]
 212   },
 213   {
 214    "cell_type": "code",
 215    "execution_count": 80,
 216    "metadata": {
 217     "Collapsed": "false"
 218    },
 219    "outputs": [
 220     {
 221      "data": {
 222       "text/html": [
 223        "<div>\n",
 224        "<style scoped>\n",
 225        "    .dataframe tbody tr th:only-of-type {\n",
 226        "        vertical-align: middle;\n",
 227        "    }\n",
 228        "\n",
 229        "    .dataframe tbody tr th {\n",
 230        "        vertical-align: top;\n",
 231        "    }\n",
 232        "\n",
 233        "    .dataframe thead th {\n",
 234        "        text-align: right;\n",
 235        "    }\n",
 236        "</style>\n",
 237        "<table border=\"1\" class=\"dataframe\">\n",
 238        "  <thead>\n",
 239        "    <tr style=\"text-align: right;\">\n",
 240        "      <th></th>\n",
 241        "      <th>total_time</th>\n",
 242        "      <th>total_alloc</th>\n",
 243        "      <th>total_ticks</th>\n",
 244        "      <th>initial_capabilities</th>\n",
 245        "    </tr>\n",
 246        "    <tr>\n",
 247        "      <th>program</th>\n",
 248        "      <th></th>\n",
 249        "      <th></th>\n",
 250        "      <th></th>\n",
 251        "      <th></th>\n",
 252        "    </tr>\n",
 253        "  </thead>\n",
 254        "  <tbody>\n",
 255        "    <tr>\n",
 256        "      <td>advent01</td>\n",
 257        "      <td>0.48</td>\n",
 258        "      <td>107029176</td>\n",
 259        "      <td>1644</td>\n",
 260        "      <td>12</td>\n",
 261        "    </tr>\n",
 262        "    <tr>\n",
 263        "      <td>advent02</td>\n",
 264        "      <td>0.26</td>\n",
 265        "      <td>35370072</td>\n",
 266        "      <td>876</td>\n",
 267        "      <td>12</td>\n",
 268        "    </tr>\n",
 269        "    <tr>\n",
 270        "      <td>advent03</td>\n",
 271        "      <td>0.06</td>\n",
 272        "      <td>4017640</td>\n",
 273        "      <td>192</td>\n",
 274        "      <td>12</td>\n",
 275        "    </tr>\n",
 276        "    <tr>\n",
 277        "      <td>advent04</td>\n",
 278        "      <td>0.44</td>\n",
 279        "      <td>60820368</td>\n",
 280        "      <td>1512</td>\n",
 281        "      <td>12</td>\n",
 282        "    </tr>\n",
 283        "    <tr>\n",
 284        "      <td>advent05</td>\n",
 285        "      <td>0.09</td>\n",
 286        "      <td>27810256</td>\n",
 287        "      <td>312</td>\n",
 288        "      <td>12</td>\n",
 289        "    </tr>\n",
 290        "    <tr>\n",
 291        "      <td>advent06</td>\n",
 292        "      <td>0.11</td>\n",
 293        "      <td>11624856</td>\n",
 294        "      <td>372</td>\n",
 295        "      <td>12</td>\n",
 296        "    </tr>\n",
 297        "    <tr>\n",
 298        "      <td>advent07</td>\n",
 299        "      <td>0.33</td>\n",
 300        "      <td>21605440</td>\n",
 301        "      <td>1128</td>\n",
 302        "      <td>12</td>\n",
 303        "    </tr>\n",
 304        "    <tr>\n",
 305        "      <td>advent08</td>\n",
 306        "      <td>0.40</td>\n",
 307        "      <td>74894192</td>\n",
 308        "      <td>1356</td>\n",
 309        "      <td>12</td>\n",
 310        "    </tr>\n",
 311        "    <tr>\n",
 312        "      <td>advent09</td>\n",
 313        "      <td>3.50</td>\n",
 314        "      <td>793279616</td>\n",
 315        "      <td>11928</td>\n",
 316        "      <td>12</td>\n",
 317        "    </tr>\n",
 318        "    <tr>\n",
 319        "      <td>advent10</td>\n",
 320        "      <td>0.01</td>\n",
 321        "      <td>924456</td>\n",
 322        "      <td>36</td>\n",
 323        "      <td>12</td>\n",
 324        "    </tr>\n",
 325        "    <tr>\n",
 326        "      <td>advent11</td>\n",
 327        "      <td>112.58</td>\n",
 328        "      <td>35282262592</td>\n",
 329        "      <td>383916</td>\n",
 330        "      <td>12</td>\n",
 331        "    </tr>\n",
 332        "    <tr>\n",
 333        "      <td>advent12</td>\n",
 334        "      <td>0.03</td>\n",
 335        "      <td>2206400</td>\n",
 336        "      <td>108</td>\n",
 337        "      <td>12</td>\n",
 338        "    </tr>\n",
 339        "    <tr>\n",
 340        "      <td>advent13</td>\n",
 341        "      <td>0.01</td>\n",
 342        "      <td>542152</td>\n",
 343        "      <td>36</td>\n",
 344        "      <td>12</td>\n",
 345        "    </tr>\n",
 346        "    <tr>\n",
 347        "      <td>advent14</td>\n",
 348        "      <td>1.66</td>\n",
 349        "      <td>259113488</td>\n",
 350        "      <td>5676</td>\n",
 351        "      <td>12</td>\n",
 352        "    </tr>\n",
 353        "    <tr>\n",
 354        "      <td>advent15</td>\n",
 355        "      <td>43.83</td>\n",
 356        "      <td>6662932672</td>\n",
 357        "      <td>149460</td>\n",
 358        "      <td>12</td>\n",
 359        "    </tr>\n",
 360        "    <tr>\n",
 361        "      <td>advent15loop</td>\n",
 362        "      <td>42.69</td>\n",
 363        "      <td>8838275776</td>\n",
 364        "      <td>145584</td>\n",
 365        "      <td>12</td>\n",
 366        "    </tr>\n",
 367        "    <tr>\n",
 368        "      <td>advent16</td>\n",
 369        "      <td>0.15</td>\n",
 370        "      <td>17242880</td>\n",
 371        "      <td>528</td>\n",
 372        "      <td>12</td>\n",
 373        "    </tr>\n",
 374        "    <tr>\n",
 375        "      <td>advent17</td>\n",
 376        "      <td>21.33</td>\n",
 377        "      <td>4808712520</td>\n",
 378        "      <td>72744</td>\n",
 379        "      <td>12</td>\n",
 380        "    </tr>\n",
 381        "    <tr>\n",
 382        "      <td>advent18</td>\n",
 383        "      <td>0.51</td>\n",
 384        "      <td>21509984</td>\n",
 385        "      <td>1728</td>\n",
 386        "      <td>12</td>\n",
 387        "    </tr>\n",
 388        "    <tr>\n",
 389        "      <td>advent19</td>\n",
 390        "      <td>0.24</td>\n",
 391        "      <td>44456496</td>\n",
 392        "      <td>816</td>\n",
 393        "      <td>12</td>\n",
 394        "    </tr>\n",
 395        "    <tr>\n",
 396        "      <td>advent20</td>\n",
 397        "      <td>34.74</td>\n",
 398        "      <td>3860804096</td>\n",
 399        "      <td>118476</td>\n",
 400        "      <td>12</td>\n",
 401        "    </tr>\n",
 402        "    <tr>\n",
 403        "      <td>advent21</td>\n",
 404        "      <td>0.15</td>\n",
 405        "      <td>9561880</td>\n",
 406        "      <td>528</td>\n",
 407        "      <td>12</td>\n",
 408        "    </tr>\n",
 409        "    <tr>\n",
 410        "      <td>advent22</td>\n",
 411        "      <td>22.64</td>\n",
 412        "      <td>3242847728</td>\n",
 413        "      <td>77208</td>\n",
 414        "      <td>12</td>\n",
 415        "    </tr>\n",
 416        "    <tr>\n",
 417        "      <td>advent23</td>\n",
 418        "      <td>72.61</td>\n",
 419        "      <td>10263690000</td>\n",
 420        "      <td>247608</td>\n",
 421        "      <td>12</td>\n",
 422        "    </tr>\n",
 423        "    <tr>\n",
 424        "      <td>advent24</td>\n",
 425        "      <td>26.45</td>\n",
 426        "      <td>4352105528</td>\n",
 427        "      <td>90180</td>\n",
 428        "      <td>12</td>\n",
 429        "    </tr>\n",
 430        "    <tr>\n",
 431        "      <td>advent25</td>\n",
 432        "      <td>0.35</td>\n",
 433        "      <td>39231576</td>\n",
 434        "      <td>1200</td>\n",
 435        "      <td>12</td>\n",
 436        "    </tr>\n",
 437        "  </tbody>\n",
 438        "</table>\n",
 439        "</div>"
 440       ],
 441       "text/plain": [
 442        "              total_time  total_alloc  total_ticks  initial_capabilities\n",
 443        "program                                                                 \n",
 444        "advent01            0.48    107029176         1644                    12\n",
 445        "advent02            0.26     35370072          876                    12\n",
 446        "advent03            0.06      4017640          192                    12\n",
 447        "advent04            0.44     60820368         1512                    12\n",
 448        "advent05            0.09     27810256          312                    12\n",
 449        "advent06            0.11     11624856          372                    12\n",
 450        "advent07            0.33     21605440         1128                    12\n",
 451        "advent08            0.40     74894192         1356                    12\n",
 452        "advent09            3.50    793279616        11928                    12\n",
 453        "advent10            0.01       924456           36                    12\n",
 454        "advent11          112.58  35282262592       383916                    12\n",
 455        "advent12            0.03      2206400          108                    12\n",
 456        "advent13            0.01       542152           36                    12\n",
 457        "advent14            1.66    259113488         5676                    12\n",
 458        "advent15           43.83   6662932672       149460                    12\n",
 459        "advent15loop       42.69   8838275776       145584                    12\n",
 460        "advent16            0.15     17242880          528                    12\n",
 461        "advent17           21.33   4808712520        72744                    12\n",
 462        "advent18            0.51     21509984         1728                    12\n",
 463        "advent19            0.24     44456496          816                    12\n",
 464        "advent20           34.74   3860804096       118476                    12\n",
 465        "advent21            0.15      9561880          528                    12\n",
 466        "advent22           22.64   3242847728        77208                    12\n",
 467        "advent23           72.61  10263690000       247608                    12\n",
 468        "advent24           26.45   4352105528        90180                    12\n",
 469        "advent25            0.35     39231576         1200                    12"
 470       ]
 471      },
 472      "execution_count": 80,
 473      "metadata": {},
 474      "output_type": "execute_result"
 475     }
 476    ],
 477    "source": [
 478     "performance = pd.DataFrame(profs).set_index('program').sort_index()\n",
 479     "performance"
 480    ]
 481   },
 482   {
 483    "cell_type": "code",
 484    "execution_count": 81,
 485    "metadata": {
 486     "Collapsed": "false"
 487    },
 488    "outputs": [
 489     {
 490      "data": {
 491       "text/plain": [
 492        "<matplotlib.axes._subplots.AxesSubplot at 0x7f265a38bfd0>"
 493       ]
 494      },
 495      "execution_count": 81,
 496      "metadata": {},
 497      "output_type": "execute_result"
 498     },
 499     {
 500      "data": {
 501       "image/png": "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\n",
 502       "text/plain": [
 503        "<Figure size 432x288 with 1 Axes>"
 504       ]
 505      },
 506      "metadata": {
 507       "needs_background": "light"
 508      },
 509      "output_type": "display_data"
 510     }
 511    ],
 512    "source": [
 513     "performance.total_time.plot.bar()"
 514    ]
 515   },
 516   {
 517    "cell_type": "code",
 518    "execution_count": 82,
 519    "metadata": {
 520     "Collapsed": "false"
 521    },
 522    "outputs": [
 523     {
 524      "data": {
 525       "text/plain": [
 526        "<matplotlib.axes._subplots.AxesSubplot at 0x7f265a35bf10>"
 527       ]
 528      },
 529      "execution_count": 82,
 530      "metadata": {},
 531      "output_type": "execute_result"
 532     },
 533     {
 534      "data": {
 535       "image/png": 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\n",
 536       "text/plain": [
 537        "<Figure size 432x288 with 1 Axes>"
 538       ]
 539      },
 540      "metadata": {
 541       "needs_background": "light"
 542      },
 543      "output_type": "display_data"
 544     }
 545    ],
 546    "source": [
 547     "performance.total_time.plot.bar(logy=True)"
 548    ]
 549   },
 550   {
 551    "cell_type": "code",
 552    "execution_count": 83,
 553    "metadata": {
 554     "Collapsed": "false"
 555    },
 556    "outputs": [
 557     {
 558      "data": {
 559       "text/plain": [
 560        "<matplotlib.axes._subplots.AxesSubplot at 0x7f265a49e510>"
 561       ]
 562      },
 563      "execution_count": 83,
 564      "metadata": {},
 565      "output_type": "execute_result"
 566     },
 567     {
 568      "data": {
 569       "image/png": 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\n",
 570       "text/plain": [
 571        "<Figure size 432x288 with 1 Axes>"
 572       ]
 573      },
 574      "metadata": {
 575       "needs_background": "light"
 576      },
 577      "output_type": "display_data"
 578     }
 579    ],
 580    "source": [
 581     "performance.total_alloc.plot.bar()"
 582    ]
 583   },
 584   {
 585    "cell_type": "code",
 586    "execution_count": 84,
 587    "metadata": {
 588     "Collapsed": "false"
 589    },
 590    "outputs": [
 591     {
 592      "data": {
 593       "text/plain": [
 594        "<matplotlib.axes._subplots.AxesSubplot at 0x7f265ab71650>"
 595       ]
 596      },
 597      "execution_count": 84,
 598      "metadata": {},
 599      "output_type": "execute_result"
 600     },
 601     {
 602      "data": {
 603       "image/png": 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qiNjWXK8p5iv5+98D+AUwGbgmb0cRcVpBzNnAIuBG4PeBO4HHgXcCH4qIG5rq/xMwF7gU6M+LZwLvBe6NiHOG6mNB+zt9/nn5J4EFwO15//4pIj6bv9bqGPgc8DvAJGA7sBuwKn9fDxf1TdJdzUX5+7sHICIOK4h5vn1J/xd4I/BV4A+A/oj4syHe713AmRHx/fz5AuBzLdr5SUTMyx9fAfwIuIrsmD41It7aVH9geZLpwBXAZRFxR6u+5DGrgXXAVOAV+eMrgbeSHZsnFsQsB/YDvgu8A9gM/BT4EPDJiLiqIKbWx00nx8xORvuT225uZKOYom0d8NuC+uuAyfnj2cBasoQPcEeLNtYBE8gS3XZgal6+O3BXi5jbgX8D3gQcnf/5s/zx0S1i7mh4vAaYnj/eE1jXImZjY5tNr93Zqh2yb2zHAV8CtgHfAk4DprT6e87/nAg8DEzIn2uIv4N1DfX2AG7IH88q+rsGftpiPyL7R1v02vYW25PAjiH6NTF/vBewGvj/wx0D+Z+7Aj8HJjX8fbT6bFbmx8ChwAH58fZg/viAERwDtwN7NrRb2E5D/ZtHUpaX39Pw+LbhjpuBfgEHA38DbADuBs4F5rZo486Gz++hER6b6xoeTxzoP7A3sL5FTK2Pm06OmeYt9Wmc/cj+B19UsP28oP6EiPglQERsIUvCx0v6NNnBUWRHRDwbEb8G/jsitufxvwGeaxHTB9wGfAR4IrJR7G8i4saIuLFFzC6S9pa0D9loeVvezq+AHS1i1uuF6aofS+oDkDQXeKZFTETEcxFxXUQsAV4GfA5YCNw3RN8mAVPIEvfAVMxuZAdVKxMb6k3JG3+gRcxTkuYXlB8FPNVi/78ADo6IqU3bFLL/XAv7FBE78r78guxYmSrpKrIRWJGB+s8AayLi6fz5DuDZooDIvr39O9mPW16VH2/PRMT9EdFqEb/dJR2RT79MyD/7gXYL22lwq6R/kfQmSUfno8obJB0pqXnUeYOkT0jaPX/8DsimUICiOwNF3o97I+JvI+J3gXcDLyJLekV2yadr9gcmD0x95Md3q7/n5waml8iOywl5u4/T+t9n3Y+bTo6ZnRpPdiMbmS5o8dpXC8quBw5vKptI9hXw2Rb7+S9gj/zxLg3l02gaTRfEziT7enwh8MAwdbeQJdvN+Z8vycsn03okNA24mGxK5r/IEvx9ZFMnr2oRUzgKyV/bvUX5n+X7vR84m+xr9hfIRjvntog5h+xb1gqyUeDpefl04KaC+kfm7+EnwHX5tjEve3WLNv4OmN/itX9oUf4NCr5d5ft6rkXMf5J/I2wqfwlw6zCf657Ap8lGbP3D1P1e0/bSvHwfhlnYqiC2cbu+qe6uwMeAB/LtObJR7VeBWaM5Zoboz2Kyb4EPA38IfIdsGvQhYGmLmD/Oj7Hr8n6d0HDM7PTv2cdNZ8dM89azc/ZF8pNPOyLifwpee0NE3FxQvltE/LagfF+yv9h1I2j3BOANEfHXbfR5D2C/iNg8RJ0pwEFk/3H1R8TDQ9SdGxE/baMfLwOIiK2S9iKb330gIm4dIuZ3yeZr10fE3SNs5yXADLKRXH/RZ9WJfDRLZN/Mml+bEREPjWJfe5J9bX5kBHVfBbwuIv55NP3NYycAu0X27bJUyk6YT4yIom/CA3UmR/6NeJT7nkD2LXWHpIlk58keiohWo2fykf1BwKbIRtAjbcvHzeC4UR8zySf7/IzzfLIPOoCtZP9rFnZ8tPUd092YFvs5dKT/WdQ1Jk/a5wK/lxfdCHwiIkZ1w+bR9i2h979rZFMXjWX7RkTLdeDHU0w7bexkNF8Dur2RnWTcRPZ16Yv59q287LhO6zumuzFDfM5DToE5JiCb5/042aj4ILLEf03VfRvr9092lVs/2YUG1wGzG14rnGYdTzHttNFqGzjBlqrzgWMjO4nxPEkHkp04ekWH9R3TpRhJnynYB2Rfy/cqfMExjV4eEX/Y8Pzjku4so53E3/8/Am+LiA2S3gV8W9J7IuJHtD6pO55i2mmjUOrJfiIvXFvb6CGKr/gYbX3HdC/mdODPgZ3Oj5Cd7CvimBf8RtKCiPgBZOeggJ3ml9tsJ+X3PykiNgBExNWSNgLXSFpGfhXROI9pp41CqSf7i4A1ki4nuxYVsku9Ts5f67S+Y7oXs4bsRO4Pm1+Q9LEWbTjmBR8ELsnn7gU8RvbbiTLaSfn9PyPpJZGfkM1HuG8hu3rm5TWIaaeNQr1wgvYVwIk0nIkHVkbET8qo75juxCi7CuOpGM3VA44p2sdUgMh/D1JGOym/f2VLeGyLiB83lU8DzoriX7iPm5h22mhpNBP8Y7WR/wp2uLJ26zumezGp9qsHYqaRXZe9Nt8+BUzzZ1OPmHba2Kn+aCqP1UbxWeqhfjw0qvqO6V5Mqv3qgZhRX43jz2b8xLTTRvOW9Jy9pMXAKcCBGrz42BQKlksYbX3HdC8m1X6lHtNgNFfj+LMZJzEdHjODJJ3sgR+SrWWxL9nX1gFPkv1Uv9P6juleTKr9Sj1mwGiuxvFnM35iOjlmBkn+BK2ZgaTDgUvI5u4HrsZ5XzSduDNraTRzPmO1AScB95Kt2DewVOn2suo7pnsxqfYr9ZiG2Knky3D73019Yjo5Zp7fx2gqj9VG9hP8V1RV3zHdi0m1X6nGAB8eavNnU4+Ydtpo3lKfsx/wcERsrLC+Y7oXk2q/Uo3p5H6z/mzGT0w7bQzSE3P2ks4nWyP6P2j4qXVEXFNGfcd0LybVfqUe0w5/NuMnpoxjpldG9lPJ7r/aeHPuILtPahn1HdO9mFT7lWSMmpaxlfQnZMtKrwe+EEOP1vzZjJ+YdtoYpCdG9mZ1pTJvOG21lvo9aAGQNFfSdyWtz58flh/4pdR3TPdiUu1XwjGNy9ieBJwUEZeQ/dDm2DL7luj7d0ybbeykk7O73drI7sozn8F3Wi+8G3079R3TvZhU+5VqDNn9fY8AXg38uOm1wnsX+7MZfzHttNG89cqc/R4Rcas0aK3+HSXWd0z3YlLtV6oxPyNbAA3gMUkvjYifSdqngr6l+P4d034bg/RKsn9U0svJF+tXdseWljc1bqO+Y7oXk2q/koyJiGNa7OMXvHA/2rL6ltz7d0xHbQw2mq8BY7WRrfL3HbKz0Q8BPwAOKKu+Y7oXk2q/Uo9psZ9D/dnUI6aMY6YnrsaRNCEinpW0J7BLRDxZZn3HdC8m1X6lHtNiPw9ExKyy2kn5/dc9poxjpieuxgE2S1oBvBb4ZQX1HdO9mFT7lWSMpM+02D7L8Dcp92czfmLaaWOw0XwNGKsN2B14N9kPCLYAFwALyqrvmO7FpNqvVGPIFrxaSna/2ebtUX829Yhpp42d9jGayilswN7ApcCzVdR3TPdiUu1XSjHA9cDrW7y22Z9N/WLaaSMiemYaB0lHS/occDvwIrL/5Uqr75juxaTar0Rj3gUU3pEqIg4su28Jvn/HdNDGIKP5n2GsNmAz8DVgMbBn2fUd072YVPvVAzHt3NTan804iWmnjeatV67GmRoR26uq75juxaTarx6IeX6NnIayOyLiiLLaSfz91zqmnTZ22kfKyT6/4qBlByPi7E7qO6Z7Man2qwdiBm44vQD4fsNLU8jmbHdaH8efzfiJaaeNVlKfs18L3EY2P3Uk2W257gUOB54tob5juheTar9Sj/kh2Y2m787/HNj+HFhYUjspv/+6x7TTRrF25n66vQHfA3ZteL4r8L2y6jumezGp9iv1mHY2fzbjJ6aMYyb1kf2AlzH49myT87Ky6jumezGp9ivpGEknSbpX0hOStkt6UtJwc7j+bMZPTDttDNIrC6EtB+6Q9L38+dHAx0qs75juxaTar9Rj/hFYFKO7D6k/m/ET004bgyR9graRpJcB7wE2AnsAWyPiprLqO6Z7Man2K+UYSTdHxBuG2mdJ7ST5/h3TXhuDjGbOZ6w24P3AOuBxsrmr3wDXl1XfMd2LSbVfPRBzPnAF2XXWJ/HCXav82dQgpp02dtrHaCqP1Za/yReR35kHOBS4oqz6juleTKr96oGYLxdsF/mzqUdMO200b70yZ/9URDwlCUm7RcTdkg4psb5juheTar+SjomI04fZZxntJPv+HdNWG4P0SrLvl7QX8B/AtyU9Dmwtsb5juheTar+SjpE0F/g8sF9EvFLSYcDbI+LvSmwn2ffvmLbaGKRnTtAOkHQ0MA34VkQ8XXZ9x3QvJtV+pRgj6UbgL4F/iXyJBEnrI+KVVfQttffvmM7agB5M9mZ1JGlNRBylhvVwJN0ZEYePdd+sN/TKj6rM6q7zG05brXlkb9YDJB0ErABeT3b53Wbg1Ii4f0w7Zj3Dyd6sB6ikm5RbfXkax6w3dH7Daas1J3uz3nAI8B3gTLLEf4GkBWPcJ+shnsYx6zGS9iZbPuHUiJgw1v2x3uCRvVmPUKc3nLZa88jerAdI2gzcCVwJrIyIX41xl6zHONmb9QCVcMNpqzcne7OEqcQbTlu9ec7eLG3l3XDaas0je7MeoOx2dMdFxDP5812B6yLimLHtmfUKj+zNekPHN5y2euuV9ezN6q7jG05bvXkax6xHqNMbTluteWRv1gMkvR84B5hJdr39a4FbgDePZb+sd3jO3qw3nAMcBdyfn5Q9Atg2tl2yXuJkb9YbnoqIp4DnbzhNtjia2Yh4GsesN3R8w2mrN5+gNesx7d5w2urNyd7MrAY8Z29mVgNO9mZmNeBkb2ZWA072VnuSOrq1nyRf1WbJc7K3cU3SbEl3S7pE0l2Srpa0h6Qtkj4q6QfAH0k6XNKP8jpfy+/ziqSj8rJbJJ0naX1e/j5JV0laBVwnabKk70q6XdI6SSc2tf9FSeslfUXSsZJulnSvpPlj97djdeJkb3VwCLAiIg4DtgMfysufiogFEXE5cCnwf/I664Bz8zpfBj4QEa9j5/XjXwecFhFvBp4C3hkRRwLHAJ+SpLzeHLIbhB8GHAqcAiwA/gL469LfrVkBJ3urgwcj4ub88b+RJVqAKwAkTQP2iogb8/JLgN/Lf8Q0JSJ+mJd/tWm/346Ix/LHAj4p6S7gO8AMYL/8tc0RsS4ingM2AN+N7JrndcDsst6k2VA812h10PxjkoHnw920W8O83hh/KjAdeHVEPCNpC9ndpQB+21DvuYbnz+F/g9YlHtlbHcyS9Lr88WLgB40vRsQTwOOS3pgXvQe4MSIeB56U9Nq8/OQh2pgGPJIn+mOAA8rrvlnnnOytDjYCp+VTLC8GPl9Q5zTgvLzO4cAn8vIlwApJt5CN9J9o0cZXgD5Ja8lG+XeX2H+zjnm5BBvXJM0GvhERr2wzfnJE/DJ/vAx4aUScU14PzbrD84VmQztB0l+R/Vu5H3jf2HbHrD0e2ZuZ1YDn7M3MasDJ3sysBpzszcxqwMnezKwGnOzNzGrAyd7MrAb+F6AxmOlFWZEzAAAAAElFTkSuQmCC\n",
 604       "text/plain": [
 605        "<Figure size 432x288 with 1 Axes>"
 606       ]
 607      },
 608      "metadata": {
 609       "needs_background": "light"
 610      },
 611      "output_type": "display_data"
 612     }
 613    ],
 614    "source": [
 615     "performance.total_alloc.plot.bar(logy=True)"
 616    ]
 617   },
 618   {
 619    "cell_type": "code",
 620    "execution_count": 85,
 621    "metadata": {
 622     "Collapsed": "false"
 623    },
 624    "outputs": [
 625     {
 626      "data": {
 627       "text/plain": [
 628        "<matplotlib.axes._subplots.AxesSubplot at 0x7f2659fb2b10>"
 629       ]
 630      },
 631      "execution_count": 85,
 632      "metadata": {},
 633      "output_type": "execute_result"
 634     },
 635     {
 636      "data": {
 637       "image/png": 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\n",
 638       "text/plain": [
 639        "<Figure size 432x288 with 1 Axes>"
 640       ]
 641      },
 642      "metadata": {
 643       "needs_background": "light"
 644      },
 645      "output_type": "display_data"
 646     }
 647    ],
 648    "source": [
 649     "performance[['total_time', 'total_alloc']].plot.bar(logy=True)"
 650    ]
 651   },
 652   {
 653    "cell_type": "code",
 654    "execution_count": 86,
 655    "metadata": {
 656     "Collapsed": "false"
 657    },
 658    "outputs": [
 659     {
 660      "data": {
 661       "text/html": [
 662        "<div>\n",
 663        "<style scoped>\n",
 664        "    .dataframe tbody tr th:only-of-type {\n",
 665        "        vertical-align: middle;\n",
 666        "    }\n",
 667        "\n",
 668        "    .dataframe tbody tr th {\n",
 669        "        vertical-align: top;\n",
 670        "    }\n",
 671        "\n",
 672        "    .dataframe thead th {\n",
 673        "        text-align: right;\n",
 674        "    }\n",
 675        "</style>\n",
 676        "<table border=\"1\" class=\"dataframe\">\n",
 677        "  <thead>\n",
 678        "    <tr style=\"text-align: right;\">\n",
 679        "      <th></th>\n",
 680        "      <th>Elapsed time (s)</th>\n",
 681        "      <th>Max memory</th>\n",
 682        "    </tr>\n",
 683        "    <tr>\n",
 684        "      <th>Program</th>\n",
 685        "      <th></th>\n",
 686        "      <th></th>\n",
 687        "    </tr>\n",
 688        "  </thead>\n",
 689        "  <tbody>\n",
 690        "    <tr>\n",
 691        "      <td>advent01</td>\n",
 692        "      <td>0.39</td>\n",
 693        "      <td>72440</td>\n",
 694        "    </tr>\n",
 695        "    <tr>\n",
 696        "      <td>advent02</td>\n",
 697        "      <td>0.41</td>\n",
 698        "      <td>72440</td>\n",
 699        "    </tr>\n",
 700        "    <tr>\n",
 701        "      <td>advent03</td>\n",
 702        "      <td>0.37</td>\n",
 703        "      <td>72504</td>\n",
 704        "    </tr>\n",
 705        "    <tr>\n",
 706        "      <td>advent04</td>\n",
 707        "      <td>0.43</td>\n",
 708        "      <td>72504</td>\n",
 709        "    </tr>\n",
 710        "    <tr>\n",
 711        "      <td>advent05</td>\n",
 712        "      <td>0.43</td>\n",
 713        "      <td>72440</td>\n",
 714        "    </tr>\n",
 715        "    <tr>\n",
 716        "      <td>advent06</td>\n",
 717        "      <td>0.41</td>\n",
 718        "      <td>72504</td>\n",
 719        "    </tr>\n",
 720        "    <tr>\n",
 721        "      <td>advent07</td>\n",
 722        "      <td>0.39</td>\n",
 723        "      <td>72444</td>\n",
 724        "    </tr>\n",
 725        "    <tr>\n",
 726        "      <td>advent08</td>\n",
 727        "      <td>0.44</td>\n",
 728        "      <td>72508</td>\n",
 729        "    </tr>\n",
 730        "    <tr>\n",
 731        "      <td>advent09</td>\n",
 732        "      <td>0.66</td>\n",
 733        "      <td>72508</td>\n",
 734        "    </tr>\n",
 735        "    <tr>\n",
 736        "      <td>advent10</td>\n",
 737        "      <td>0.36</td>\n",
 738        "      <td>72444</td>\n",
 739        "    </tr>\n",
 740        "    <tr>\n",
 741        "      <td>advent11</td>\n",
 742        "      <td>16.75</td>\n",
 743        "      <td>72504</td>\n",
 744        "    </tr>\n",
 745        "    <tr>\n",
 746        "      <td>advent12</td>\n",
 747        "      <td>0.38</td>\n",
 748        "      <td>72508</td>\n",
 749        "    </tr>\n",
 750        "    <tr>\n",
 751        "      <td>advent13</td>\n",
 752        "      <td>0.36</td>\n",
 753        "      <td>72436</td>\n",
 754        "    </tr>\n",
 755        "    <tr>\n",
 756        "      <td>advent14</td>\n",
 757        "      <td>0.61</td>\n",
 758        "      <td>72508</td>\n",
 759        "    </tr>\n",
 760        "    <tr>\n",
 761        "      <td>advent15</td>\n",
 762        "      <td>2.15</td>\n",
 763        "      <td>240372</td>\n",
 764        "    </tr>\n",
 765        "    <tr>\n",
 766        "      <td>advent15loop</td>\n",
 767        "      <td>1.88</td>\n",
 768        "      <td>240444</td>\n",
 769        "    </tr>\n",
 770        "    <tr>\n",
 771        "      <td>advent16</td>\n",
 772        "      <td>0.38</td>\n",
 773        "      <td>72444</td>\n",
 774        "    </tr>\n",
 775        "    <tr>\n",
 776        "      <td>advent17</td>\n",
 777        "      <td>1.77</td>\n",
 778        "      <td>72444</td>\n",
 779        "    </tr>\n",
 780        "    <tr>\n",
 781        "      <td>advent18</td>\n",
 782        "      <td>0.39</td>\n",
 783        "      <td>72444</td>\n",
 784        "    </tr>\n",
 785        "    <tr>\n",
 786        "      <td>advent19</td>\n",
 787        "      <td>0.46</td>\n",
 788        "      <td>72504</td>\n",
 789        "    </tr>\n",
 790        "    <tr>\n",
 791        "      <td>advent20</td>\n",
 792        "      <td>4.12</td>\n",
 793        "      <td>72436</td>\n",
 794        "    </tr>\n",
 795        "    <tr>\n",
 796        "      <td>advent21</td>\n",
 797        "      <td>0.39</td>\n",
 798        "      <td>72508</td>\n",
 799        "    </tr>\n",
 800        "    <tr>\n",
 801        "      <td>advent22</td>\n",
 802        "      <td>2.02</td>\n",
 803        "      <td>72500</td>\n",
 804        "    </tr>\n",
 805        "    <tr>\n",
 806        "      <td>advent23</td>\n",
 807        "      <td>1.91</td>\n",
 808        "      <td>95500</td>\n",
 809        "    </tr>\n",
 810        "    <tr>\n",
 811        "      <td>advent24</td>\n",
 812        "      <td>3.48</td>\n",
 813        "      <td>72504</td>\n",
 814        "    </tr>\n",
 815        "    <tr>\n",
 816        "      <td>advent25</td>\n",
 817        "      <td>0.41</td>\n",
 818        "      <td>72504</td>\n",
 819        "    </tr>\n",
 820        "  </tbody>\n",
 821        "</table>\n",
 822        "</div>"
 823       ],
 824       "text/plain": [
 825        "              Elapsed time (s)  Max memory\n",
 826        "Program                                   \n",
 827        "advent01                  0.39       72440\n",
 828        "advent02                  0.41       72440\n",
 829        "advent03                  0.37       72504\n",
 830        "advent04                  0.43       72504\n",
 831        "advent05                  0.43       72440\n",
 832        "advent06                  0.41       72504\n",
 833        "advent07                  0.39       72444\n",
 834        "advent08                  0.44       72508\n",
 835        "advent09                  0.66       72508\n",
 836        "advent10                  0.36       72444\n",
 837        "advent11                 16.75       72504\n",
 838        "advent12                  0.38       72508\n",
 839        "advent13                  0.36       72436\n",
 840        "advent14                  0.61       72508\n",
 841        "advent15                  2.15      240372\n",
 842        "advent15loop              1.88      240444\n",
 843        "advent16                  0.38       72444\n",
 844        "advent17                  1.77       72444\n",
 845        "advent18                  0.39       72444\n",
 846        "advent19                  0.46       72504\n",
 847        "advent20                  4.12       72436\n",
 848        "advent21                  0.39       72508\n",
 849        "advent22                  2.02       72500\n",
 850        "advent23                  1.91       95500\n",
 851        "advent24                  3.48       72504\n",
 852        "advent25                  0.41       72504"
 853       ]
 854      },
 855      "execution_count": 86,
 856      "metadata": {},
 857      "output_type": "execute_result"
 858     }
 859    ],
 860    "source": [
 861     "times = pd.read_csv('time-results.csv').set_index('Program').sort_index()\n",
 862     "times"
 863    ]
 864   },
 865   {
 866    "cell_type": "code",
 867    "execution_count": 87,
 868    "metadata": {
 869     "Collapsed": "false"
 870    },
 871    "outputs": [
 872     {
 873      "data": {
 874       "text/html": [
 875        "<div>\n",
 876        "<style scoped>\n",
 877        "    .dataframe tbody tr th:only-of-type {\n",
 878        "        vertical-align: middle;\n",
 879        "    }\n",
 880        "\n",
 881        "    .dataframe tbody tr th {\n",
 882        "        vertical-align: top;\n",
 883        "    }\n",
 884        "\n",
 885        "    .dataframe thead th {\n",
 886        "        text-align: right;\n",
 887        "    }\n",
 888        "</style>\n",
 889        "<table border=\"1\" class=\"dataframe\">\n",
 890        "  <thead>\n",
 891        "    <tr style=\"text-align: right;\">\n",
 892        "      <th></th>\n",
 893        "      <th>Elapsed time (s)</th>\n",
 894        "      <th>Max memory</th>\n",
 895        "    </tr>\n",
 896        "  </thead>\n",
 897        "  <tbody>\n",
 898        "    <tr>\n",
 899        "      <td>count</td>\n",
 900        "      <td>26.000000</td>\n",
 901        "      <td>26.000000</td>\n",
 902        "    </tr>\n",
 903        "    <tr>\n",
 904        "      <td>mean</td>\n",
 905        "      <td>1.605769</td>\n",
 906        "      <td>86280.615385</td>\n",
 907        "    </tr>\n",
 908        "    <tr>\n",
 909        "      <td>std</td>\n",
 910        "      <td>3.255795</td>\n",
 911        "      <td>45597.232922</td>\n",
 912        "    </tr>\n",
 913        "    <tr>\n",
 914        "      <td>min</td>\n",
 915        "      <td>0.360000</td>\n",
 916        "      <td>72436.000000</td>\n",
 917        "    </tr>\n",
 918        "    <tr>\n",
 919        "      <td>25%</td>\n",
 920        "      <td>0.390000</td>\n",
 921        "      <td>72444.000000</td>\n",
 922        "    </tr>\n",
 923        "    <tr>\n",
 924        "      <td>50%</td>\n",
 925        "      <td>0.430000</td>\n",
 926        "      <td>72504.000000</td>\n",
 927        "    </tr>\n",
 928        "    <tr>\n",
 929        "      <td>75%</td>\n",
 930        "      <td>1.852500</td>\n",
 931        "      <td>72508.000000</td>\n",
 932        "    </tr>\n",
 933        "    <tr>\n",
 934        "      <td>max</td>\n",
 935        "      <td>16.750000</td>\n",
 936        "      <td>240444.000000</td>\n",
 937        "    </tr>\n",
 938        "  </tbody>\n",
 939        "</table>\n",
 940        "</div>"
 941       ],
 942       "text/plain": [
 943        "       Elapsed time (s)     Max memory\n",
 944        "count         26.000000      26.000000\n",
 945        "mean           1.605769   86280.615385\n",
 946        "std            3.255795   45597.232922\n",
 947        "min            0.360000   72436.000000\n",
 948        "25%            0.390000   72444.000000\n",
 949        "50%            0.430000   72504.000000\n",
 950        "75%            1.852500   72508.000000\n",
 951        "max           16.750000  240444.000000"
 952       ]
 953      },
 954      "execution_count": 87,
 955      "metadata": {},
 956      "output_type": "execute_result"
 957     }
 958    ],
 959    "source": [
 960     "times.describe()"
 961    ]
 962   },
 963   {
 964    "cell_type": "code",
 965    "execution_count": 88,
 966    "metadata": {
 967     "Collapsed": "false"
 968    },
 969    "outputs": [
 970     {
 971      "data": {
 972       "text/html": [
 973        "<div>\n",
 974        "<style scoped>\n",
 975        "    .dataframe tbody tr th:only-of-type {\n",
 976        "        vertical-align: middle;\n",
 977        "    }\n",
 978        "\n",
 979        "    .dataframe tbody tr th {\n",
 980        "        vertical-align: top;\n",
 981        "    }\n",
 982        "\n",
 983        "    .dataframe thead th {\n",
 984        "        text-align: right;\n",
 985        "    }\n",
 986        "</style>\n",
 987        "<table border=\"1\" class=\"dataframe\">\n",
 988        "  <thead>\n",
 989        "    <tr style=\"text-align: right;\">\n",
 990        "      <th></th>\n",
 991        "      <th>total_time</th>\n",
 992        "      <th>total_alloc</th>\n",
 993        "      <th>total_ticks</th>\n",
 994        "      <th>initial_capabilities</th>\n",
 995        "      <th>Elapsed time (s)</th>\n",
 996        "      <th>Max memory</th>\n",
 997        "    </tr>\n",
 998        "    <tr>\n",
 999        "      <th>program</th>\n",
1000        "      <th></th>\n",
1001        "      <th></th>\n",
1002        "      <th></th>\n",
1003        "      <th></th>\n",
1004        "      <th></th>\n",
1005        "      <th></th>\n",
1006        "    </tr>\n",
1007        "  </thead>\n",
1008        "  <tbody>\n",
1009        "    <tr>\n",
1010        "      <td>advent01</td>\n",
1011        "      <td>0.48</td>\n",
1012        "      <td>107029176</td>\n",
1013        "      <td>1644</td>\n",
1014        "      <td>12</td>\n",
1015        "      <td>0.39</td>\n",
1016        "      <td>72440</td>\n",
1017        "    </tr>\n",
1018        "    <tr>\n",
1019        "      <td>advent02</td>\n",
1020        "      <td>0.26</td>\n",
1021        "      <td>35370072</td>\n",
1022        "      <td>876</td>\n",
1023        "      <td>12</td>\n",
1024        "      <td>0.41</td>\n",
1025        "      <td>72440</td>\n",
1026        "    </tr>\n",
1027        "    <tr>\n",
1028        "      <td>advent03</td>\n",
1029        "      <td>0.06</td>\n",
1030        "      <td>4017640</td>\n",
1031        "      <td>192</td>\n",
1032        "      <td>12</td>\n",
1033        "      <td>0.37</td>\n",
1034        "      <td>72504</td>\n",
1035        "    </tr>\n",
1036        "    <tr>\n",
1037        "      <td>advent04</td>\n",
1038        "      <td>0.44</td>\n",
1039        "      <td>60820368</td>\n",
1040        "      <td>1512</td>\n",
1041        "      <td>12</td>\n",
1042        "      <td>0.43</td>\n",
1043        "      <td>72504</td>\n",
1044        "    </tr>\n",
1045        "    <tr>\n",
1046        "      <td>advent05</td>\n",
1047        "      <td>0.09</td>\n",
1048        "      <td>27810256</td>\n",
1049        "      <td>312</td>\n",
1050        "      <td>12</td>\n",
1051        "      <td>0.43</td>\n",
1052        "      <td>72440</td>\n",
1053        "    </tr>\n",
1054        "    <tr>\n",
1055        "      <td>advent06</td>\n",
1056        "      <td>0.11</td>\n",
1057        "      <td>11624856</td>\n",
1058        "      <td>372</td>\n",
1059        "      <td>12</td>\n",
1060        "      <td>0.41</td>\n",
1061        "      <td>72504</td>\n",
1062        "    </tr>\n",
1063        "    <tr>\n",
1064        "      <td>advent07</td>\n",
1065        "      <td>0.33</td>\n",
1066        "      <td>21605440</td>\n",
1067        "      <td>1128</td>\n",
1068        "      <td>12</td>\n",
1069        "      <td>0.39</td>\n",
1070        "      <td>72444</td>\n",
1071        "    </tr>\n",
1072        "    <tr>\n",
1073        "      <td>advent08</td>\n",
1074        "      <td>0.40</td>\n",
1075        "      <td>74894192</td>\n",
1076        "      <td>1356</td>\n",
1077        "      <td>12</td>\n",
1078        "      <td>0.44</td>\n",
1079        "      <td>72508</td>\n",
1080        "    </tr>\n",
1081        "    <tr>\n",
1082        "      <td>advent09</td>\n",
1083        "      <td>3.50</td>\n",
1084        "      <td>793279616</td>\n",
1085        "      <td>11928</td>\n",
1086        "      <td>12</td>\n",
1087        "      <td>0.66</td>\n",
1088        "      <td>72508</td>\n",
1089        "    </tr>\n",
1090        "    <tr>\n",
1091        "      <td>advent10</td>\n",
1092        "      <td>0.01</td>\n",
1093        "      <td>924456</td>\n",
1094        "      <td>36</td>\n",
1095        "      <td>12</td>\n",
1096        "      <td>0.36</td>\n",
1097        "      <td>72444</td>\n",
1098        "    </tr>\n",
1099        "    <tr>\n",
1100        "      <td>advent11</td>\n",
1101        "      <td>112.58</td>\n",
1102        "      <td>35282262592</td>\n",
1103        "      <td>383916</td>\n",
1104        "      <td>12</td>\n",
1105        "      <td>16.75</td>\n",
1106        "      <td>72504</td>\n",
1107        "    </tr>\n",
1108        "    <tr>\n",
1109        "      <td>advent12</td>\n",
1110        "      <td>0.03</td>\n",
1111        "      <td>2206400</td>\n",
1112        "      <td>108</td>\n",
1113        "      <td>12</td>\n",
1114        "      <td>0.38</td>\n",
1115        "      <td>72508</td>\n",
1116        "    </tr>\n",
1117        "    <tr>\n",
1118        "      <td>advent13</td>\n",
1119        "      <td>0.01</td>\n",
1120        "      <td>542152</td>\n",
1121        "      <td>36</td>\n",
1122        "      <td>12</td>\n",
1123        "      <td>0.36</td>\n",
1124        "      <td>72436</td>\n",
1125        "    </tr>\n",
1126        "    <tr>\n",
1127        "      <td>advent14</td>\n",
1128        "      <td>1.66</td>\n",
1129        "      <td>259113488</td>\n",
1130        "      <td>5676</td>\n",
1131        "      <td>12</td>\n",
1132        "      <td>0.61</td>\n",
1133        "      <td>72508</td>\n",
1134        "    </tr>\n",
1135        "    <tr>\n",
1136        "      <td>advent15</td>\n",
1137        "      <td>43.83</td>\n",
1138        "      <td>6662932672</td>\n",
1139        "      <td>149460</td>\n",
1140        "      <td>12</td>\n",
1141        "      <td>2.15</td>\n",
1142        "      <td>240372</td>\n",
1143        "    </tr>\n",
1144        "    <tr>\n",
1145        "      <td>advent16</td>\n",
1146        "      <td>0.15</td>\n",
1147        "      <td>17242880</td>\n",
1148        "      <td>528</td>\n",
1149        "      <td>12</td>\n",
1150        "      <td>0.38</td>\n",
1151        "      <td>72444</td>\n",
1152        "    </tr>\n",
1153        "    <tr>\n",
1154        "      <td>advent17</td>\n",
1155        "      <td>21.33</td>\n",
1156        "      <td>4808712520</td>\n",
1157        "      <td>72744</td>\n",
1158        "      <td>12</td>\n",
1159        "      <td>1.77</td>\n",
1160        "      <td>72444</td>\n",
1161        "    </tr>\n",
1162        "    <tr>\n",
1163        "      <td>advent18</td>\n",
1164        "      <td>0.51</td>\n",
1165        "      <td>21509984</td>\n",
1166        "      <td>1728</td>\n",
1167        "      <td>12</td>\n",
1168        "      <td>0.39</td>\n",
1169        "      <td>72444</td>\n",
1170        "    </tr>\n",
1171        "    <tr>\n",
1172        "      <td>advent19</td>\n",
1173        "      <td>0.24</td>\n",
1174        "      <td>44456496</td>\n",
1175        "      <td>816</td>\n",
1176        "      <td>12</td>\n",
1177        "      <td>0.46</td>\n",
1178        "      <td>72504</td>\n",
1179        "    </tr>\n",
1180        "    <tr>\n",
1181        "      <td>advent20</td>\n",
1182        "      <td>34.74</td>\n",
1183        "      <td>3860804096</td>\n",
1184        "      <td>118476</td>\n",
1185        "      <td>12</td>\n",
1186        "      <td>4.12</td>\n",
1187        "      <td>72436</td>\n",
1188        "    </tr>\n",
1189        "    <tr>\n",
1190        "      <td>advent21</td>\n",
1191        "      <td>0.15</td>\n",
1192        "      <td>9561880</td>\n",
1193        "      <td>528</td>\n",
1194        "      <td>12</td>\n",
1195        "      <td>0.39</td>\n",
1196        "      <td>72508</td>\n",
1197        "    </tr>\n",
1198        "    <tr>\n",
1199        "      <td>advent22</td>\n",
1200        "      <td>22.64</td>\n",
1201        "      <td>3242847728</td>\n",
1202        "      <td>77208</td>\n",
1203        "      <td>12</td>\n",
1204        "      <td>2.02</td>\n",
1205        "      <td>72500</td>\n",
1206        "    </tr>\n",
1207        "    <tr>\n",
1208        "      <td>advent23</td>\n",
1209        "      <td>72.61</td>\n",
1210        "      <td>10263690000</td>\n",
1211        "      <td>247608</td>\n",
1212        "      <td>12</td>\n",
1213        "      <td>1.91</td>\n",
1214        "      <td>95500</td>\n",
1215        "    </tr>\n",
1216        "    <tr>\n",
1217        "      <td>advent24</td>\n",
1218        "      <td>26.45</td>\n",
1219        "      <td>4352105528</td>\n",
1220        "      <td>90180</td>\n",
1221        "      <td>12</td>\n",
1222        "      <td>3.48</td>\n",
1223        "      <td>72504</td>\n",
1224        "    </tr>\n",
1225        "    <tr>\n",
1226        "      <td>advent25</td>\n",
1227        "      <td>0.35</td>\n",
1228        "      <td>39231576</td>\n",
1229        "      <td>1200</td>\n",
1230        "      <td>12</td>\n",
1231        "      <td>0.41</td>\n",
1232        "      <td>72504</td>\n",
1233        "    </tr>\n",
1234        "  </tbody>\n",
1235        "</table>\n",
1236        "</div>"
1237       ],
1238       "text/plain": [
1239        "          total_time  total_alloc  total_ticks  initial_capabilities  \\\n",
1240        "program                                                                \n",
1241        "advent01        0.48    107029176         1644                    12   \n",
1242        "advent02        0.26     35370072          876                    12   \n",
1243        "advent03        0.06      4017640          192                    12   \n",
1244        "advent04        0.44     60820368         1512                    12   \n",
1245        "advent05        0.09     27810256          312                    12   \n",
1246        "advent06        0.11     11624856          372                    12   \n",
1247        "advent07        0.33     21605440         1128                    12   \n",
1248        "advent08        0.40     74894192         1356                    12   \n",
1249        "advent09        3.50    793279616        11928                    12   \n",
1250        "advent10        0.01       924456           36                    12   \n",
1251        "advent11      112.58  35282262592       383916                    12   \n",
1252        "advent12        0.03      2206400          108                    12   \n",
1253        "advent13        0.01       542152           36                    12   \n",
1254        "advent14        1.66    259113488         5676                    12   \n",
1255        "advent15       43.83   6662932672       149460                    12   \n",
1256        "advent16        0.15     17242880          528                    12   \n",
1257        "advent17       21.33   4808712520        72744                    12   \n",
1258        "advent18        0.51     21509984         1728                    12   \n",
1259        "advent19        0.24     44456496          816                    12   \n",
1260        "advent20       34.74   3860804096       118476                    12   \n",
1261        "advent21        0.15      9561880          528                    12   \n",
1262        "advent22       22.64   3242847728        77208                    12   \n",
1263        "advent23       72.61  10263690000       247608                    12   \n",
1264        "advent24       26.45   4352105528        90180                    12   \n",
1265        "advent25        0.35     39231576         1200                    12   \n",
1266        "\n",
1267        "          Elapsed time (s)  Max memory  \n",
1268        "program                                 \n",
1269        "advent01              0.39       72440  \n",
1270        "advent02              0.41       72440  \n",
1271        "advent03              0.37       72504  \n",
1272        "advent04              0.43       72504  \n",
1273        "advent05              0.43       72440  \n",
1274        "advent06              0.41       72504  \n",
1275        "advent07              0.39       72444  \n",
1276        "advent08              0.44       72508  \n",
1277        "advent09              0.66       72508  \n",
1278        "advent10              0.36       72444  \n",
1279        "advent11             16.75       72504  \n",
1280        "advent12              0.38       72508  \n",
1281        "advent13              0.36       72436  \n",
1282        "advent14              0.61       72508  \n",
1283        "advent15              2.15      240372  \n",
1284        "advent16              0.38       72444  \n",
1285        "advent17              1.77       72444  \n",
1286        "advent18              0.39       72444  \n",
1287        "advent19              0.46       72504  \n",
1288        "advent20              4.12       72436  \n",
1289        "advent21              0.39       72508  \n",
1290        "advent22              2.02       72500  \n",
1291        "advent23              1.91       95500  \n",
1292        "advent24              3.48       72504  \n",
1293        "advent25              0.41       72504  "
1294       ]
1295      },
1296      "execution_count": 88,
1297      "metadata": {},
1298      "output_type": "execute_result"
1299     }
1300    ],
1301    "source": [
1302     "performance = performance.merge(times, left_index=True, right_index=True)\n",
1303     "performance.drop(index='advent15loop', inplace=True)\n",
1304     "performance"
1305    ]
1306   },
1307   {
1308    "cell_type": "code",
1309    "execution_count": 89,
1310    "metadata": {
1311     "Collapsed": "false"
1312    },
1313    "outputs": [
1314     {
1315      "data": {
1316       "text/plain": [
1317        "Index(['total_time', 'total_alloc', 'total_ticks', 'initial_capabilities',\n",
1318        "       'Elapsed time (s)', 'Max memory'],\n",
1319        "      dtype='object')"
1320       ]
1321      },
1322      "execution_count": 89,
1323      "metadata": {},
1324      "output_type": "execute_result"
1325     }
1326    ],
1327    "source": [
1328     "performance.columns"
1329    ]
1330   },
1331   {
1332    "cell_type": "code",
1333    "execution_count": 90,
1334    "metadata": {
1335     "Collapsed": "false"
1336    },
1337    "outputs": [
1338     {
1339      "data": {
1340       "text/plain": [
1341        "<matplotlib.axes._subplots.AxesSubplot at 0x7f2659dff7d0>"
1342       ]
1343      },
1344      "execution_count": 90,
1345      "metadata": {},
1346      "output_type": "execute_result"
1347     },
1348     {
1349      "data": {
1350       "image/png": 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\n",
1351       "text/plain": [
1352        "<Figure size 432x288 with 1 Axes>"
1353       ]
1354      },
1355      "metadata": {
1356       "needs_background": "light"
1357      },
1358      "output_type": "display_data"
1359     }
1360    ],
1361    "source": [
1362     "performance[['total_time', 'Elapsed time (s)']].plot.bar(logy=True)"
1363    ]
1364   },
1365   {
1366    "cell_type": "code",
1367    "execution_count": 91,
1368    "metadata": {
1369     "Collapsed": "false"
1370    },
1371    "outputs": [
1372     {
1373      "data": {
1374       "text/html": [
1375        "<div>\n",
1376        "<style scoped>\n",
1377        "    .dataframe tbody tr th:only-of-type {\n",
1378        "        vertical-align: middle;\n",
1379        "    }\n",
1380        "\n",
1381        "    .dataframe tbody tr th {\n",
1382        "        vertical-align: top;\n",
1383        "    }\n",
1384        "\n",
1385        "    .dataframe thead th {\n",
1386        "        text-align: right;\n",
1387        "    }\n",
1388        "</style>\n",
1389        "<table border=\"1\" class=\"dataframe\">\n",
1390        "  <thead>\n",
1391        "    <tr style=\"text-align: right;\">\n",
1392        "      <th></th>\n",
1393        "      <th>total_time</th>\n",
1394        "      <th>total_alloc</th>\n",
1395        "      <th>total_ticks</th>\n",
1396        "      <th>initial_capabilities</th>\n",
1397        "      <th>Elapsed time (s)</th>\n",
1398        "      <th>Max memory</th>\n",
1399        "      <th>elapsed_adj</th>\n",
1400        "    </tr>\n",
1401        "    <tr>\n",
1402        "      <th>program</th>\n",
1403        "      <th></th>\n",
1404        "      <th></th>\n",
1405        "      <th></th>\n",
1406        "      <th></th>\n",
1407        "      <th></th>\n",
1408        "      <th></th>\n",
1409        "      <th></th>\n",
1410        "    </tr>\n",
1411        "  </thead>\n",
1412        "  <tbody>\n",
1413        "    <tr>\n",
1414        "      <td>advent01</td>\n",
1415        "      <td>0.48</td>\n",
1416        "      <td>107029176</td>\n",
1417        "      <td>1644</td>\n",
1418        "      <td>12</td>\n",
1419        "      <td>0.39</td>\n",
1420        "      <td>72440</td>\n",
1421        "      <td>0.04</td>\n",
1422        "    </tr>\n",
1423        "    <tr>\n",
1424        "      <td>advent02</td>\n",
1425        "      <td>0.26</td>\n",
1426        "      <td>35370072</td>\n",
1427        "      <td>876</td>\n",
1428        "      <td>12</td>\n",
1429        "      <td>0.41</td>\n",
1430        "      <td>72440</td>\n",
1431        "      <td>0.06</td>\n",
1432        "    </tr>\n",
1433        "    <tr>\n",
1434        "      <td>advent03</td>\n",
1435        "      <td>0.06</td>\n",
1436        "      <td>4017640</td>\n",
1437        "      <td>192</td>\n",
1438        "      <td>12</td>\n",
1439        "      <td>0.37</td>\n",
1440        "      <td>72504</td>\n",
1441        "      <td>0.02</td>\n",
1442        "    </tr>\n",
1443        "    <tr>\n",
1444        "      <td>advent04</td>\n",
1445        "      <td>0.44</td>\n",
1446        "      <td>60820368</td>\n",
1447        "      <td>1512</td>\n",
1448        "      <td>12</td>\n",
1449        "      <td>0.43</td>\n",
1450        "      <td>72504</td>\n",
1451        "      <td>0.08</td>\n",
1452        "    </tr>\n",
1453        "    <tr>\n",
1454        "      <td>advent05</td>\n",
1455        "      <td>0.09</td>\n",
1456        "      <td>27810256</td>\n",
1457        "      <td>312</td>\n",
1458        "      <td>12</td>\n",
1459        "      <td>0.43</td>\n",
1460        "      <td>72440</td>\n",
1461        "      <td>0.08</td>\n",
1462        "    </tr>\n",
1463        "    <tr>\n",
1464        "      <td>advent06</td>\n",
1465        "      <td>0.11</td>\n",
1466        "      <td>11624856</td>\n",
1467        "      <td>372</td>\n",
1468        "      <td>12</td>\n",
1469        "      <td>0.41</td>\n",
1470        "      <td>72504</td>\n",
1471        "      <td>0.06</td>\n",
1472        "    </tr>\n",
1473        "    <tr>\n",
1474        "      <td>advent07</td>\n",
1475        "      <td>0.33</td>\n",
1476        "      <td>21605440</td>\n",
1477        "      <td>1128</td>\n",
1478        "      <td>12</td>\n",
1479        "      <td>0.39</td>\n",
1480        "      <td>72444</td>\n",
1481        "      <td>0.04</td>\n",
1482        "    </tr>\n",
1483        "    <tr>\n",
1484        "      <td>advent08</td>\n",
1485        "      <td>0.40</td>\n",
1486        "      <td>74894192</td>\n",
1487        "      <td>1356</td>\n",
1488        "      <td>12</td>\n",
1489        "      <td>0.44</td>\n",
1490        "      <td>72508</td>\n",
1491        "      <td>0.09</td>\n",
1492        "    </tr>\n",
1493        "    <tr>\n",
1494        "      <td>advent09</td>\n",
1495        "      <td>3.50</td>\n",
1496        "      <td>793279616</td>\n",
1497        "      <td>11928</td>\n",
1498        "      <td>12</td>\n",
1499        "      <td>0.66</td>\n",
1500        "      <td>72508</td>\n",
1501        "      <td>0.31</td>\n",
1502        "    </tr>\n",
1503        "    <tr>\n",
1504        "      <td>advent10</td>\n",
1505        "      <td>0.01</td>\n",
1506        "      <td>924456</td>\n",
1507        "      <td>36</td>\n",
1508        "      <td>12</td>\n",
1509        "      <td>0.36</td>\n",
1510        "      <td>72444</td>\n",
1511        "      <td>0.01</td>\n",
1512        "    </tr>\n",
1513        "    <tr>\n",
1514        "      <td>advent11</td>\n",
1515        "      <td>112.58</td>\n",
1516        "      <td>35282262592</td>\n",
1517        "      <td>383916</td>\n",
1518        "      <td>12</td>\n",
1519        "      <td>16.75</td>\n",
1520        "      <td>72504</td>\n",
1521        "      <td>16.40</td>\n",
1522        "    </tr>\n",
1523        "    <tr>\n",
1524        "      <td>advent12</td>\n",
1525        "      <td>0.03</td>\n",
1526        "      <td>2206400</td>\n",
1527        "      <td>108</td>\n",
1528        "      <td>12</td>\n",
1529        "      <td>0.38</td>\n",
1530        "      <td>72508</td>\n",
1531        "      <td>0.03</td>\n",
1532        "    </tr>\n",
1533        "    <tr>\n",
1534        "      <td>advent13</td>\n",
1535        "      <td>0.01</td>\n",
1536        "      <td>542152</td>\n",
1537        "      <td>36</td>\n",
1538        "      <td>12</td>\n",
1539        "      <td>0.36</td>\n",
1540        "      <td>72436</td>\n",
1541        "      <td>0.01</td>\n",
1542        "    </tr>\n",
1543        "    <tr>\n",
1544        "      <td>advent14</td>\n",
1545        "      <td>1.66</td>\n",
1546        "      <td>259113488</td>\n",
1547        "      <td>5676</td>\n",
1548        "      <td>12</td>\n",
1549        "      <td>0.61</td>\n",
1550        "      <td>72508</td>\n",
1551        "      <td>0.26</td>\n",
1552        "    </tr>\n",
1553        "    <tr>\n",
1554        "      <td>advent15</td>\n",
1555        "      <td>43.83</td>\n",
1556        "      <td>6662932672</td>\n",
1557        "      <td>149460</td>\n",
1558        "      <td>12</td>\n",
1559        "      <td>2.15</td>\n",
1560        "      <td>240372</td>\n",
1561        "      <td>1.80</td>\n",
1562        "    </tr>\n",
1563        "    <tr>\n",
1564        "      <td>advent16</td>\n",
1565        "      <td>0.15</td>\n",
1566        "      <td>17242880</td>\n",
1567        "      <td>528</td>\n",
1568        "      <td>12</td>\n",
1569        "      <td>0.38</td>\n",
1570        "      <td>72444</td>\n",
1571        "      <td>0.03</td>\n",
1572        "    </tr>\n",
1573        "    <tr>\n",
1574        "      <td>advent17</td>\n",
1575        "      <td>21.33</td>\n",
1576        "      <td>4808712520</td>\n",
1577        "      <td>72744</td>\n",
1578        "      <td>12</td>\n",
1579        "      <td>1.77</td>\n",
1580        "      <td>72444</td>\n",
1581        "      <td>1.42</td>\n",
1582        "    </tr>\n",
1583        "    <tr>\n",
1584        "      <td>advent18</td>\n",
1585        "      <td>0.51</td>\n",
1586        "      <td>21509984</td>\n",
1587        "      <td>1728</td>\n",
1588        "      <td>12</td>\n",
1589        "      <td>0.39</td>\n",
1590        "      <td>72444</td>\n",
1591        "      <td>0.04</td>\n",
1592        "    </tr>\n",
1593        "    <tr>\n",
1594        "      <td>advent19</td>\n",
1595        "      <td>0.24</td>\n",
1596        "      <td>44456496</td>\n",
1597        "      <td>816</td>\n",
1598        "      <td>12</td>\n",
1599        "      <td>0.46</td>\n",
1600        "      <td>72504</td>\n",
1601        "      <td>0.11</td>\n",
1602        "    </tr>\n",
1603        "    <tr>\n",
1604        "      <td>advent20</td>\n",
1605        "      <td>34.74</td>\n",
1606        "      <td>3860804096</td>\n",
1607        "      <td>118476</td>\n",
1608        "      <td>12</td>\n",
1609        "      <td>4.12</td>\n",
1610        "      <td>72436</td>\n",
1611        "      <td>3.77</td>\n",
1612        "    </tr>\n",
1613        "    <tr>\n",
1614        "      <td>advent21</td>\n",
1615        "      <td>0.15</td>\n",
1616        "      <td>9561880</td>\n",
1617        "      <td>528</td>\n",
1618        "      <td>12</td>\n",
1619        "      <td>0.39</td>\n",
1620        "      <td>72508</td>\n",
1621        "      <td>0.04</td>\n",
1622        "    </tr>\n",
1623        "    <tr>\n",
1624        "      <td>advent22</td>\n",
1625        "      <td>22.64</td>\n",
1626        "      <td>3242847728</td>\n",
1627        "      <td>77208</td>\n",
1628        "      <td>12</td>\n",
1629        "      <td>2.02</td>\n",
1630        "      <td>72500</td>\n",
1631        "      <td>1.67</td>\n",
1632        "    </tr>\n",
1633        "    <tr>\n",
1634        "      <td>advent23</td>\n",
1635        "      <td>72.61</td>\n",
1636        "      <td>10263690000</td>\n",
1637        "      <td>247608</td>\n",
1638        "      <td>12</td>\n",
1639        "      <td>1.91</td>\n",
1640        "      <td>95500</td>\n",
1641        "      <td>1.56</td>\n",
1642        "    </tr>\n",
1643        "    <tr>\n",
1644        "      <td>advent24</td>\n",
1645        "      <td>26.45</td>\n",
1646        "      <td>4352105528</td>\n",
1647        "      <td>90180</td>\n",
1648        "      <td>12</td>\n",
1649        "      <td>3.48</td>\n",
1650        "      <td>72504</td>\n",
1651        "      <td>3.13</td>\n",
1652        "    </tr>\n",
1653        "    <tr>\n",
1654        "      <td>advent25</td>\n",
1655        "      <td>0.35</td>\n",
1656        "      <td>39231576</td>\n",
1657        "      <td>1200</td>\n",
1658        "      <td>12</td>\n",
1659        "      <td>0.41</td>\n",
1660        "      <td>72504</td>\n",
1661        "      <td>0.06</td>\n",
1662        "    </tr>\n",
1663        "  </tbody>\n",
1664        "</table>\n",
1665        "</div>"
1666       ],
1667       "text/plain": [
1668        "          total_time  total_alloc  total_ticks  initial_capabilities  \\\n",
1669        "program                                                                \n",
1670        "advent01        0.48    107029176         1644                    12   \n",
1671        "advent02        0.26     35370072          876                    12   \n",
1672        "advent03        0.06      4017640          192                    12   \n",
1673        "advent04        0.44     60820368         1512                    12   \n",
1674        "advent05        0.09     27810256          312                    12   \n",
1675        "advent06        0.11     11624856          372                    12   \n",
1676        "advent07        0.33     21605440         1128                    12   \n",
1677        "advent08        0.40     74894192         1356                    12   \n",
1678        "advent09        3.50    793279616        11928                    12   \n",
1679        "advent10        0.01       924456           36                    12   \n",
1680        "advent11      112.58  35282262592       383916                    12   \n",
1681        "advent12        0.03      2206400          108                    12   \n",
1682        "advent13        0.01       542152           36                    12   \n",
1683        "advent14        1.66    259113488         5676                    12   \n",
1684        "advent15       43.83   6662932672       149460                    12   \n",
1685        "advent16        0.15     17242880          528                    12   \n",
1686        "advent17       21.33   4808712520        72744                    12   \n",
1687        "advent18        0.51     21509984         1728                    12   \n",
1688        "advent19        0.24     44456496          816                    12   \n",
1689        "advent20       34.74   3860804096       118476                    12   \n",
1690        "advent21        0.15      9561880          528                    12   \n",
1691        "advent22       22.64   3242847728        77208                    12   \n",
1692        "advent23       72.61  10263690000       247608                    12   \n",
1693        "advent24       26.45   4352105528        90180                    12   \n",
1694        "advent25        0.35     39231576         1200                    12   \n",
1695        "\n",
1696        "          Elapsed time (s)  Max memory  elapsed_adj  \n",
1697        "program                                              \n",
1698        "advent01              0.39       72440         0.04  \n",
1699        "advent02              0.41       72440         0.06  \n",
1700        "advent03              0.37       72504         0.02  \n",
1701        "advent04              0.43       72504         0.08  \n",
1702        "advent05              0.43       72440         0.08  \n",
1703        "advent06              0.41       72504         0.06  \n",
1704        "advent07              0.39       72444         0.04  \n",
1705        "advent08              0.44       72508         0.09  \n",
1706        "advent09              0.66       72508         0.31  \n",
1707        "advent10              0.36       72444         0.01  \n",
1708        "advent11             16.75       72504        16.40  \n",
1709        "advent12              0.38       72508         0.03  \n",
1710        "advent13              0.36       72436         0.01  \n",
1711        "advent14              0.61       72508         0.26  \n",
1712        "advent15              2.15      240372         1.80  \n",
1713        "advent16              0.38       72444         0.03  \n",
1714        "advent17              1.77       72444         1.42  \n",
1715        "advent18              0.39       72444         0.04  \n",
1716        "advent19              0.46       72504         0.11  \n",
1717        "advent20              4.12       72436         3.77  \n",
1718        "advent21              0.39       72508         0.04  \n",
1719        "advent22              2.02       72500         1.67  \n",
1720        "advent23              1.91       95500         1.56  \n",
1721        "advent24              3.48       72504         3.13  \n",
1722        "advent25              0.41       72504         0.06  "
1723       ]
1724      },
1725      "execution_count": 91,
1726      "metadata": {},
1727      "output_type": "execute_result"
1728     }
1729    ],
1730    "source": [
1731     "performance['elapsed_adj'] = performance['Elapsed time (s)'] - 0.35\n",
1732     "performance"
1733    ]
1734   },
1735   {
1736    "cell_type": "code",
1737    "execution_count": 92,
1738    "metadata": {
1739     "Collapsed": "false"
1740    },
1741    "outputs": [
1742     {
1743      "data": {
1744       "text/plain": [
1745        "<matplotlib.axes._subplots.AxesSubplot at 0x7f2659b2a950>"
1746       ]
1747      },
1748      "execution_count": 92,
1749      "metadata": {},
1750      "output_type": "execute_result"
1751     },
1752     {
1753      "data": {
1754       "image/png": 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\n",
1755       "text/plain": [
1756        "<Figure size 432x288 with 1 Axes>"
1757       ]
1758      },
1759      "metadata": {
1760       "needs_background": "light"
1761      },
1762      "output_type": "display_data"
1763     }
1764    ],
1765    "source": [
1766     "performance[['total_time', 'elapsed_adj']].plot.bar(logy=True)"
1767    ]
1768   },
1769   {
1770    "cell_type": "code",
1771    "execution_count": 115,
1772    "metadata": {
1773     "Collapsed": "false"
1774    },
1775    "outputs": [
1776     {
1777      "data": {
1778       "image/png": 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\n",
1779       "text/plain": [
1780        "<Figure size 864x360 with 2 Axes>"
1781       ]
1782      },
1783      "metadata": {
1784       "needs_background": "light"
1785      },
1786      "output_type": "display_data"
1787     }
1788    ],
1789    "source": [
1790     "fig, ax = plt.subplots(ncols=2, figsize=(12,5))\n",
1791     "\n",
1792     "performance['elapsed_adj'].plot.bar(ax=ax[0],\n",
1793     "    logy=True, \n",
1794     "    title=\"Run times (wall clock), log scale\",\n",
1795     "#     figsize=(10,8)\n",
1796     "    )\n",
1797     "ax[0].set_xlabel('Program')\n",
1798     "\n",
1799     "performance['elapsed_adj'].plot.bar(ax=ax[1],\n",
1800     "    logy=False, \n",
1801     "    title=\"Run times (wall clock), linear scale\",\n",
1802     "#     figsize=(10,8)\n",
1803     "    )\n",
1804     "ax[1].set_xlabel('Program')\n",
1805     "\n",
1806     "plt.savefig('run_times_combined.png')"
1807    ]
1808   },
1809   {
1810    "cell_type": "code",
1811    "execution_count": 109,
1812    "metadata": {
1813     "Collapsed": "false"
1814    },
1815    "outputs": [
1816     {
1817      "data": {
1818       "image/png": 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\n",
1819       "text/plain": [
1820        "<Figure size 720x576 with 1 Axes>"
1821       ]
1822      },
1823      "metadata": {
1824       "needs_background": "light"
1825      },
1826      "output_type": "display_data"
1827     }
1828    ],
1829    "source": [
1830     "ax = performance['elapsed_adj'].plot.bar(logy=False, \n",
1831     "    title=\"Run times (wall clock), linear scale\",\n",
1832     "    figsize=(10,8))\n",
1833     "ax.set_xlabel('Program')\n",
1834     "plt.savefig('run_times_linear.png')"
1835    ]
1836   },
1837   {
1838    "cell_type": "code",
1839    "execution_count": 93,
1840    "metadata": {
1841     "Collapsed": "false"
1842    },
1843    "outputs": [
1844     {
1845      "data": {
1846       "text/plain": [
1847        "Index(['total_time', 'total_alloc', 'total_ticks', 'initial_capabilities',\n",
1848        "       'Elapsed time (s)', 'Max memory', 'elapsed_adj'],\n",
1849        "      dtype='object')"
1850       ]
1851      },
1852      "execution_count": 93,
1853      "metadata": {},
1854      "output_type": "execute_result"
1855     }
1856    ],
1857    "source": [
1858     "performance.columns"
1859    ]
1860   },
1861   {
1862    "cell_type": "code",
1863    "execution_count": 94,
1864    "metadata": {
1865     "Collapsed": "false"
1866    },
1867    "outputs": [
1868     {
1869      "data": {
1870       "text/plain": [
1871        "<matplotlib.axes._subplots.AxesSubplot at 0x7f2659b29ad0>"
1872       ]
1873      },
1874      "execution_count": 94,
1875      "metadata": {},
1876      "output_type": "execute_result"
1877     },
1878     {
1879      "data": {
1880       "image/png": 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\n",
1881       "text/plain": [
1882        "<Figure size 432x288 with 1 Axes>"
1883       ]
1884      },
1885      "metadata": {
1886       "needs_background": "light"
1887      },
1888      "output_type": "display_data"
1889     }
1890    ],
1891    "source": [
1892     "performance['Max memory'].plot.bar()"
1893    ]
1894   },
1895   {
1896    "cell_type": "code",
1897    "execution_count": 95,
1898    "metadata": {
1899     "Collapsed": "false"
1900    },
1901    "outputs": [
1902     {
1903      "data": {
1904       "text/plain": [
1905        "<matplotlib.axes._subplots.AxesSubplot at 0x7f2659a07890>"
1906       ]
1907      },
1908      "execution_count": 95,
1909      "metadata": {},
1910      "output_type": "execute_result"
1911     },
1912     {
1913      "data": {
1914       "image/png": 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\n",
1915       "text/plain": [
1916        "<Figure size 432x288 with 1 Axes>"
1917       ]
1918      },
1919      "metadata": {
1920       "needs_background": "light"
1921      },
1922      "output_type": "display_data"
1923     }
1924    ],
1925    "source": [
1926     "performance.plot.scatter('elapsed_adj', 'total_alloc', logx=True, logy=True)"
1927    ]
1928   },
1929   {
1930    "cell_type": "code",
1931    "execution_count": 100,
1932    "metadata": {
1933     "Collapsed": "false"
1934    },
1935    "outputs": [],
1936    "source": [
1937     "performance[['total_alloc', 'Max memory', 'elapsed_adj']].to_csv('performance.csv')"
1938    ]
1939   },
1940   {
1941    "cell_type": "code",
1942    "execution_count": null,
1943    "metadata": {
1944     "Collapsed": "false"
1945    },
1946    "outputs": [],
1947    "source": []
1948   }
1949  ],
1950  "metadata": {
1951   "kernelspec": {
1952    "display_name": "Python 3",
1953    "language": "python",
1954    "name": "python3"
1955   },
1956   "language_info": {
1957    "codemirror_mode": {
1958     "name": "ipython",
1959     "version": 3
1960    },
1961    "file_extension": ".py",
1962    "mimetype": "text/x-python",
1963    "name": "python",
1964    "nbconvert_exporter": "python",
1965    "pygments_lexer": "ipython3",
1966    "version": "3.7.4"
1967   }
1968  },
1969  "nbformat": 4,
1970  "nbformat_minor": 4
1971 }