measurement.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:15328e5c8f829fac798082ee1e1f7d70f95e0bbe45e4252d0de67847330cb9bd"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import subprocess\n",
      "import re\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "TIME_REGEX = re.compile(r'^\\s+(?P<real>[0-9.]+) real\\s+(?P<user>[0-9.]+) user\\s+(?P<sys>[0-9.]+) sys$', re.MULTILINE)\n",
      "SPACE_REGEX = re.compile(r'(?P<space_in_bytes>\\d+)\\s+maximum resident set size', re.MULTILINE)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = {}\n",
      "\n",
      "dict_implementations = (\n",
      "    ('chaining', './soln_3_chaining_hashmap'),\n",
      "    ('open addressing', './soln_4_open_addressing_hashmap'),\n",
      ")\n",
      "\n",
      "for implementation_name, exe in dict_implementations:\n",
      "    \n",
      "    data[implementation_name] = []\n",
      "    \n",
      "    for dict_capacity in map(int, (1.2e8, 1.4e8, 2e8, 4e8)):\n",
      "        \n",
      "#         if (implementation_name == 'chaining' and dict_capacity > 2e8):\n",
      "            # in this case, space used > 6GB and machine starts thrashing{}\n",
      "#             next\n",
      "        \n",
      "        out = subprocess.check_output(['/usr/bin/time', '-l',\n",
      "                                       exe, 'usernames_100M.txt', str(dict_capacity)],\n",
      "                                      stderr=subprocess.STDOUT)\n",
      "        time_match = TIME_REGEX.search(out)\n",
      "        space_match = SPACE_REGEX.search(out)\n",
      "        row = {\n",
      "            'implementation': implementation_name,\n",
      "            'dict_capacity': dict_capacity,\n",
      "            'time_real': float(time_match.group('real')),\n",
      "            'time_user': float(time_match.group('user')),\n",
      "            'time_sys': float(time_match.group('sys')),\n",
      "            'space': int(space_match.group('space_in_bytes')),\n",
      "        }\n",
      "        data[implementation_name].append(row)\n",
      "    \n",
      "    data[implementation_name] = pd.DataFrame(data[implementation_name])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "{'chaining':    dict_capacity implementation       space  time_real  time_sys  time_user\n",
        " 0      120000000       chaining  4518617088     112.09      2.62     108.95\n",
        " 1      140000000       chaining  4834074624     109.11      2.56     106.20\n",
        " 2      200000000       chaining  5785096192     104.97      3.18     100.98\n",
        " 3      400000000       chaining  7257128960     478.89    201.00     149.80,\n",
        " 'open addressing':    dict_capacity   implementation       space  time_real  time_sys  time_user\n",
        " 0      120000000  open addressing  1934000128     122.49      1.48     110.48\n",
        " 1      140000000  open addressing  2094002176      96.94      1.23      95.70\n",
        " 2      200000000  open addressing  2574000128      91.45      1.35      90.06\n",
        " 3      400000000  open addressing  4174008320      88.25      1.93      86.29}"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for implementation, df in data.items():\n",
      "    plt.plot(df.dict_capacity, df.time_real,\n",
      "             marker='o', label=implementation)\n",
      "axes = plt.gca()\n",
      "# axes.set_xlim(xmin=0, xmax=3.2e8)\n",
      "axes.set_ylim(ymin=0)#, ymax=10)\n",
      "plt.xlabel('Hashmap capacity')\n",
      "plt.ylabel('Real time (seconds)')\n",
      "plt.legend()\n",
      "plt.show()\n",
      "\n",
      "for implementation, df in data.items():\n",
      "    plt.plot(df.dict_capacity, df.space,\n",
      "             marker='o', label=implementation)\n",
      "axes = plt.gca()\n",
      "# axes.set_xlim(xmin=0, xmax=3.2e8)\n",
      "axes.set_ylim(ymin=0)#, ymax=6)\n",
      "plt.xlabel('Hashmap capacity')\n",
      "plt.ylabel('Maximum resident set size (bytes)')\n",
      "plt.legend()\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mM8sEXtcLg0RE0kNSG6krn5XdvRzIjLcgERFJL7EkiI/MrNDMWpjZAcFzER8m\nOzAREUmtWBLEJcC3gLXAJ4R6Xr0omUGJiEjqxXQXU1OhNggRkfgls6uNQ8zsRTNbHIwfaWbXJBKk\niIikj1guMd0HTARKg/F3gTOTFpGIiDQJsSSI1pWfeQiu8exJXkgiItIUxJIgNpnZwPCImZ0GrE9e\nSCIi0hTE8qBcLnAvcBywhdCb5c7WC4NERNJDUl8YFBTQNlh+W7yFNBQlCBGR+CXzLqYrgv6XdgB/\nMrM3zWxEIkGKiEj6iKUN4oKgo77hQCfgXOCWpEYlIiIpF0uCCFdLCoCH3f29JMYjIiJNRCwJ4g0z\nmwOcAjwfXG7SW+VERJq5WO5iygSOAla6+xYz6wz0dPd3GiPAarGokVpEJE4NfheTmeW6+8p9FLrP\nZRqSEoSISPySkSCeANoAM4DXCT0cZ0B34GjgVGCbu5+RaNDxUoIQEYlfUp6DCJ6gPoNQd999g8mr\ngVeAx929Ud8LoQQhIhK/pD8o15DMrANwP3A44MD5wArgCUKJaBUw2t23VFtPCUJEJE5JfeVoEtwB\nPOfug4AjgWXABGCuux8MvBiMi4hIijR6DcLM2gOL3H1AtenLgDx332hm3YBidz+02jKqQYiIxCmd\nahD9CfUQ+1DQbcd9ZtYG6OruG4NlNgJdUxCbiIgEYumLKcPMxpjZb4LxPmY2pB5lZgFfB/7s7l8n\n1MdTlctJQTVBVQURkRTKimGZPxN6cvo7wPXA9mDa0QmW+Qnwibu/Foz/HbgK2GBm3dx9g5l1Bz6N\ntnJRUVFkOD8/n/z8/ATDEBFpnoqLiykuLq73dmJ5knqRuw8O/wymve3uX0u4ULP5wI/dfbmZFQGt\ng1mfu/utZjYB6ODuE6qtpzYIEZE4JdoGEUsNojTobiNc0IHUvy+mccCjZnYAsJLQba6ZwHQzu5Dg\nNtd6liEiIvUQSw3iHEIn628A04DTgGvcfXryw6sRi2oQIiJxSuqDcmY2CDgpGH3R3ZfGW1BDUIIQ\nEYlfshNER6APoUtSDuDub8ZbWH0pQYiIxC9pbRBmdgNwHvAhVdsevh1vYSIikj5iaYNYDnzV3Usb\nJ6Q6Y1ENQkQkTsl8knox0DH+kEREJJ3FUoP4JvAM8B5QEkx2dz81ybFFi0U1CBGROCXzOYi/ArcQ\nShDhNgidpUVEmrlYEsR2d5+c9EhERKRJieUS022ELi3NYO8lJt3mKiKSJpL2HISZFRPlkpK7N/pt\nrkoQIiKCzCpfAAANt0lEQVTxS6tXjiZKCUJEJH4N3khtZmPc/WEz+wVVaxBG6C6m2xKIU0RE0kRd\njdThLrhz0F1LIiL7nVjaIE5w91f2Na0x6BKTiEj8kvkk9ZQo03Tbq4hIM1dXG8RxwPHAgWZ2JaG2\nBwhdcsqsbT0REWke6mqDOIC9ySCn0vSthF4aJCIizVgsbRD93H1V44RTN7VBiIjET89BiIhIVMls\npBYRkf2QEoSIiERV111M0W5vDXN3L0xCPCIi0kTUdRfTG+x9grr6tSs1BIiINHNqpBYRaeaS9kY5\nM/sK8GvgMKBVMNnd/TvxFiYiIukjlkbqR4FlwACgCFgFvJ68kEREpCmI5UG5N93962b2jrsfGUx7\n3d2PbpQIq8aiS0wiInFK5nMQpcHPDWb2fTP7OtAx3oKqM7NMM1tkZs8G453MbK6ZLTezOWbWob5l\niIhI4mJJEDcFJ+tfAL8E7gd+3gBlXw4sYe8dUROAue5+MPBiMC4iIimSkruYzKwXMBW4CbjS3Uea\n2TIgz903mlk3oNjdD622ni4xiYjEKWmXmMzsEDN70cwWB+NHmtk1iQRZye3Ar4CKStO6uvvGYHgj\n0LWeZYiISD3s8zZX4D5CJ/O/BOPvAo8DNyZSoJl9H/jU3ReZWX60ZdzdzSxqVaGoqCgynJ+fT35+\n1E2IiOy3iouLKS4urvd2YrmL6XV3P9rMFrn74GDaW+5+VEIFmv0WGAOUAS2BdsA/gG8C+e6+wcy6\nA/N0iUlEpP6SeRfTJjMbWKmg04D18RYU5u4T3b23u/cHzgBecvcxwAxgbLDYWODpRMsQEZH6i+US\n02XAvcAhZrYO+Ag4uwFjCFcJbgGmm9mFhB7GG92AZYiISJxivovJzNoS6rRvOzDa3Z9IZmC1xKBL\nTCIicWrwS0xm1tbMfmFmfzazS4GdwHeBxTRsDUJERJqgWmsQZvYPYCvwH2A40BvYDRS6+1uNFmHV\nmFSDEBGJU4O/k7pa30uZhBqm+7r7rnpFWg9KECIi8UvGXUzl4QF3LwfWpjI5iIhI46qrBlFOqN0h\nrBUQThDu7u2SHFu0mFSDEBGJU4O/MMjdM+sXkoiIpLNYHpQTEZH9kBKEiIhEpQQhIiJRKUGIiEhU\nShAiIhKVEoSIiESlBCEiIlEpQYiISFRKECIiEpUShIiIRKUEISIiUSlBiIhIVEoQIiISlRKEiIhE\npQQhIiJRKUGIiEhUShAiIhKVEoSIiESlBCEiIlEpQYiISFRKECIiElWjJwgz621m88xssZm9Z2aF\nwfROZjbXzJab2Rwz69DYsYmIyF7m7o1boFk3oJu7v2VmbYE3gB8A5wOfufvvzGw80NHdJ1Rb1xs7\nXhGRdGdmuLvFu16j1yDcfYO7vxUMbweWAj2BU4FpwWLTCCUNERFJkZS2QZhZP2AwsBDo6u4bg1kb\nga4pCktERICsVBUcXF56Crjc3beZ7a39uLubWdRrSUVFRZHh/Px88vPzkxuoiEiaKS4upri4uN7b\nafQ2CAAzawHMBGa7+5+CacuAfHffYGbdgXnufmi19dQGISISp7Rpg7BQVeEBYEk4OQRmAGOD4bHA\n040dm4iI7JWKu5hOAOYD7wDhwq8C/gdMB/oAq4DR7r6l2rqqQYiIxCnRGkRKLjElSglCRCR+aXOJ\nSURE0oMShIiIRKUEISIiUSlBiIhIVEoQIiISlRKEiIhEpQQhIiJRKUGIiEhUShAiIhKVEoSIiESl\nBCEiIlEpQYiISFRKECIiEpUShIiIRKUEISIiUSlBiIhIVEoQIiISlRKEiIhEpQQhIiJRKUGIiEhU\nShAiIhKVEoSIiESlBCEiIlEpQYiISFRKECIiEpUShIiIRNWkEoSZnWxmy8xshZmNT3U8IiL7syaT\nIMwsE7gTOBk4DDjTzAalNqrGVVxcnOoQkkr7l96a8/41532rjyaTIIAhwAfuvsrd9wB/A0alOKZG\n1dz/SLV/6a05719z3rf6aEoJoiewptL4J8E0ERFJgaaUIDzVAYiIyF7m3jTOy2Z2LFDk7icH41cB\nFe5+a6VlmkawIiJpxt0t3nWaUoLIAt4HTgLWAf8DznT3pSkNTERkP5WV6gDC3L3MzC4D/gVkAg8o\nOYiIpE6TqUGIiEjT0pQaqQEwswfNbKOZvVvHMpODh+neNrPBjRlffe1r/8ws38y+NLNFweeaxo6x\nPsyst5nNM7PFZvaemRXWslxaHsNY9i9dj6GZtTSzhWb2lpktMbOba1kuXY/dPvcvXY9dZWaWGcT+\nbC3zYz9+7t6kPsCJwGDg3VrmnwI8FwwfA/w31TE38P7lAzNSHWc99q8bcFQw3JZQu9Kg5nIMY9y/\ntD2GQOvgZxbwX+CE5nLsYty/tD12lfbhSuDRaPsR7/FrcjUId18AbK5jkVOBacGyC4EOZta1MWJr\nCDHsH0Dcdxs0Fe6+wd3fCoa3A0uBHtUWS9tjGOP+QZoeQ3ffGQweQKgt8Itqi6TtsYOY9g/S9NgB\nmFkvQkngfqLvR1zHr8kliBhEe6CuV4piSQYHjg+qf8+Z2WGpDihRZtaPUG1pYbVZzeIY1rF/aXsM\nzSzDzN4CNgLz3H1JtUXS+tjFsH9pe+wCtwO/AipqmR/X8UvHBAE1M2Nzaml/E+jt7l8DpgBPpzie\nhJhZW+DvwOXBN+0ai1QbT6tjuI/9S9tj6O4V7n4UoZPGUDPLj7JY2h67GPYvbY+dmX0f+NTdF1F3\nLSjm45eOCWIt0LvSeK9gWrPg7tvC1WB3nw20MLNOKQ4rLmbWAngKeMTdo/2DpfUx3Nf+NYdj6O5f\nArOAo6vNSutjF1bb/qX5sTseONXMPgIeB75jZn+ttkxcxy8dE8QM4FyIPH29xd03pjakhmNmXc3M\nguEhhG5FjnadtEkKYn8AWOLuf6plsbQ9hrHsX7oeQzPrYmYdguFWwDBgUbXF0vnY7XP/0vXYAbj7\nRHfv7e79gTOAl9z93GqLxXX8msyDcmFm9jiQB3QxszXAtUALAHe/x92fM7NTzOwDYAdwfuqijd++\n9g84DfipmZUBOwkd6HTyLeAc4B0zC//zTQT6QLM4hvvcP9L3GHYHpplZBqEvjw+7+4tmdjE0i2O3\nz/0jfY9dNA5Qn+OnB+VERCSqdLzEJCIijUAJQkREolKCEBGRqJQgREQkKiUIEZEmzGLowLTSsgPN\nbEHQWd/bZva9+pStBCEi0rQ9BJwc47LXEHqAczChW3T/XJ+ClSCkSTKz7dXGzzOzKQlsJ7+2bo+b\nKzPrYWZPBsNfq++3SEmtaB18mlmumc02s9fNbL6ZHRLMWg+0D4Y7UM+n3Jvcg3IigeoP6OiBnRi5\n+zrg/wWjg4FvALNTF5Ekwb3Axe7+gZkdQ6imcBJwM/AfMxsHtAmmJUw1CEkXkQ7GzGykmf3XzN40\ns7lm9pVgel6lF728GXSoB9DWzJ40s6Vm9kil7awys98Gy79uZl83szlm9kH46VMza2tmL5jZG2b2\njpmdGkzvZ2bLzOwRC7185smg+4aqQYeuCb9goZfUvGFm/c2sTbzbNLNJZvY/M3vXzO7Zx/b7Bcu1\nAK4HTg9+H6PNbLmZdQnWzbDQi2M6N/CxkiQK/q6PA54Mnub/C6H3lADcBtzv7r0Jdfv9SPStxCjV\nL7fQR59oH6CMUD854c9qYHIwr0Ol5X4M/CEYngEcFwy3JtTffz6whdA7Gwz4N3B8sMxHhL6Fhf+x\n3iH0rasLsCGYngnkBMNdgBXBcD9CXSqHy3sA+EWU/VgIjAqGDwBaJbJNoGOlbf4V+H4d2+9H8EIq\nYGz49xaM/4ZQD7QAw4EnU32s9Ynp/6HyMW0HrKtluSVAz0rjK4EuiZarGoQ0VbvcfXD4Q+jEFq5F\n9A6+6b8D/BII99n/KnB7UL3u6O7lwfT/ufs6D/3HvEXony1sRvDzXeA/7r7D3T8DSsysHaFa9s1m\n9jYwF+gRrrEAa9z9P8HwI8AJlXfAzHKAHu7+DIC7l7r7rgS3+Z2g1vQO8B3gsDq2XyWMSr83gAcJ\nOmsDLiDUACppxN23Ah+Z2WkQ6kDSzI4MZi8DvhtMHwS0DP6eE6IEIemi8kluCqFvxUcCFxP61oy7\n3wpcGIy/WqnhrqTSuuVUbXsLz6sASitNryDUieLZhL7lfz1IVJ8CLYNlKreLGLG3k8S1TTPLJnSN\n+UfBPt8XLB93u4y7fwJsNLPvAN9EbRNNnoU6+Pw3cIiZrTGz8wn9DV1ooZcfvUfoTXEQelnQ+cH0\nxwjVIBOmRmpJR+2AdcHweeGJZpbr7ouBxWb2TeAQ4MsYt1nbC1baEXoJS7mZfRvoW2leHzM71t3/\nC5wFLKi8ortvM7NPzGyUuz8TnOgzEthmOBl8Hlx//n/AdHffXsv2K9sK5FSbdj+h2sm0oFYlTZi7\nn1nLrBp3p7n7SkKXVRuEahDSVEW7iyk8rYhQA93rwKZK0y8PGmffJlQbmF1p3VjK8yjjjwJHB5d2\nxhB6B3XY+8DPzGwJoVsL746y3TFAYRDTq0DXeLfpoZfb3Efom+LzVH3FabTtV97neYQuRy0ys9HB\ntGcJtbXo8pLUSd19iyTAQu+jftbdj2jK26ylnKOBP7p7XjLLkfSnS0wiiUvGt6ukfmMzswnAJYQu\nX4nUSTUIERGJSm0QIiISlRKEiIhEpQQhIiJRKUGIiEhUShAiIhKVEoSIiET1/wFx/t+90/JXLAAA\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e9e9e50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ky8OrP7yURwFfJA0oDy/VIcpumS+5+4kVLasMBXyp65SHlyhEMeNVI6AxwYxX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\nAj8QXLh0cizwK+BfZhb/Zbo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       "text": [
        "<matplotlib.figure.Figure at 0x10a5c98d0>"
       ]
      }
     ],
     "prompt_number": 53
    }
   ],
   "metadata": {}
  }
 ]
}