Fortran_Compiler_Comparison.ipynb

{
 "metadata": {
  "name": "Fortran_Compiler_Comparison"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import StringIO"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# from http://polyhedron.com/pb05-lin64-f90bench_SBhtml\n",
      "x='''\n",
      "  Test   Absoft   G95  GFortran Intel(AP) Intel Lahey     PGI     Sun\n",
      "AC        4.42    9.60    5.92    5.03    4.80    8.00    5.97    21.99\n",
      "AERMOD    11.26    25.99    18.99    11.62    10.59    11.27    10.56    10.30\n",
      "AIR    1.37    5.95    3.10    1.01    2.28    2.71    2.86    2.51\n",
      "CAPACITA    17.90    26.83    17.96    17.61    16.21    19.94    14.62    21.56\n",
      "CHANNEL2    84.53    272.72    84.26    85.77    83.96    122.19    103.49    84.83\n",
      "DODUC    18.66    24.05    17.51    13.67    13.70    17.45    15.97    14.15\n",
      "FATIGUE2    95.21    374.38    101.73    52.50    52.37    110.88    80.23    75.28\n",
      "GAS_DYN2    73.18    320.04    86.71    60.16    63.68    107.03    77.73    84.39\n",
      "INDUCT2    28.08    147.53    66.57    26.85    63.49    93.29    113.20    125.91\n",
      "LINPK    5.52    6.21    5.77    5.03    4.84    5.67    5.49    5.11\n",
      "MDBX    6.86    8.68    7.32    3.61    5.74    7.85    8.04    7.41\n",
      "MP_PROP_DESIGN    23.90    419.95    103.67    19.01    55.54    126.05    78.71    161.97\n",
      "NF    7.78    13.40    7.79    7.21    7.02    10.36    7.59    8.16\n",
      "PROTEIN    19.28    27.43    20.33    18.21    18.33    35.68    20.82    20.44\n",
      "RNFLOW    10.89    25.90    15.47    9.59    11.23    14.58    15.07    15.72\n",
      "TEST_FPU2    42.95    103.38    42.94    34.69    42.26    61.17    41.28    57.41\n",
      "TFFT2    53.47    60.04    54.16    53.49    52.54    55.74    53.73    55.07\n",
      "'''\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv(StringIO.StringIO(x.strip()), sep=\"\\s*\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Test</th>\n",
        "      <th>Absoft</th>\n",
        "      <th>G95</th>\n",
        "      <th>GFortran</th>\n",
        "      <th>Intel(AP)</th>\n",
        "      <th>Intel</th>\n",
        "      <th>Lahey</th>\n",
        "      <th>PGI</th>\n",
        "      <th>Sun</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>       AC</td>\n",
        "      <td>  4.42</td>\n",
        "      <td>   9.60</td>\n",
        "      <td>  5.92</td>\n",
        "      <td>  5.03</td>\n",
        "      <td>  4.80</td>\n",
        "      <td>   8.00</td>\n",
        "      <td>   5.97</td>\n",
        "      <td> 21.99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>   AERMOD</td>\n",
        "      <td> 11.26</td>\n",
        "      <td>  25.99</td>\n",
        "      <td> 18.99</td>\n",
        "      <td> 11.62</td>\n",
        "      <td> 10.59</td>\n",
        "      <td>  11.27</td>\n",
        "      <td>  10.56</td>\n",
        "      <td> 10.30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>      AIR</td>\n",
        "      <td>  1.37</td>\n",
        "      <td>   5.95</td>\n",
        "      <td>  3.10</td>\n",
        "      <td>  1.01</td>\n",
        "      <td>  2.28</td>\n",
        "      <td>   2.71</td>\n",
        "      <td>   2.86</td>\n",
        "      <td>  2.51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> CAPACITA</td>\n",
        "      <td> 17.90</td>\n",
        "      <td>  26.83</td>\n",
        "      <td> 17.96</td>\n",
        "      <td> 17.61</td>\n",
        "      <td> 16.21</td>\n",
        "      <td>  19.94</td>\n",
        "      <td>  14.62</td>\n",
        "      <td> 21.56</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> CHANNEL2</td>\n",
        "      <td> 84.53</td>\n",
        "      <td> 272.72</td>\n",
        "      <td> 84.26</td>\n",
        "      <td> 85.77</td>\n",
        "      <td> 83.96</td>\n",
        "      <td> 122.19</td>\n",
        "      <td> 103.49</td>\n",
        "      <td> 84.83</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "       Test  Absoft     G95  GFortran  Intel(AP)  Intel   Lahey     PGI    Sun\n",
        "0        AC    4.42    9.60      5.92       5.03   4.80    8.00    5.97  21.99\n",
        "1    AERMOD   11.26   25.99     18.99      11.62  10.59   11.27   10.56  10.30\n",
        "2       AIR    1.37    5.95      3.10       1.01   2.28    2.71    2.86   2.51\n",
        "3  CAPACITA   17.90   26.83     17.96      17.61  16.21   19.94   14.62  21.56\n",
        "4  CHANNEL2   84.53  272.72     84.26      85.77  83.96  122.19  103.49  84.83"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[['Intel','PGI']].plot(kind='bar',title='Fortran execution times',figsize=(12,4))\n",
      "xlabel('Test')\n",
      "ylabel('Seconds')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<matplotlib.text.Text at 0x63a6490>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GoX379mjfvj1eeuklzJw50xrZdMFeH/NJzQXIziYZ542sRfK6JjmbZFLnTWou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\nRGb4/8EhduAY+vcDAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x621aa10>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot("
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}