Paralle IPython

parallel

{
 "metadata": {
  "name": "t12aparallel",
  "signature": "sha256:592681c554e9742e6e0d910855060e314fd9070ccc64be9e0a6d2e7f4e314130"
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Parallel IPython"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Suppose that we need to vary the parameter, `x` of some defintion `f(x)` Instead of runs sequentially, we can run each value in one specific thread of the multicore machine. 30 threads in this case"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Load parallel modules"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the IPython notebook main page go to the `Clusters` tab and start the corresponding profile. In this case `\"myserver\"`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.parallel import Client\n",
      "c = Client(profile=\"default\")\n",
      "lview = c.load_balanced_view()\n",
      "dview = c.direct_view()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "here = os.getcwd()\n",
      "dview.execute('import os; os.chdir(\"%s\")' % here)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<AsyncResult: execute>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Prepare `f(x)`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SPhenoPATH='../SPhenoT12AM'\n",
      "LesHouchesInputFile='LesHouches.in.T12AM_low_LFV'\n",
      "\n",
      "def SPhenoPar(y, SPhenoPATH, LesHouchesInputFile):\n",
      "    import t12aLFV as t; reload(t)\n",
      "    t.di['eta_R'] ={1:0.1,2:0.1,3:0.}\n",
      "    t.di['eta_I'] ={1:0.0,2:0.1,3:0.} #Eta1 is real for neutrino physics \n",
      "    t.di=t.benchmarks(t.di,'fermion')     #Benchmark is the point\n",
      "    t.di['eta_R'][1]=y\n",
      "    x = t.runSPhenoparallel(t.di,SPhenoPATH,LesHouchesInputFile)\n",
      "    return x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Load output modules"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import time\n",
      "n=30"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Run in parallel for each `x` and print execution time"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "start = time.time()\n",
      "amr = [lview.apply(SPhenoPar, y, SPhenoPATH, LesHouchesInputFile) for y in np.logspace(-4, 0., n)]\n",
      "df=pd.DataFrame(columns=['mH']) #intialize pandas with any column\n",
      "for r in amr:\n",
      "    df=df.append(r.get()[0],ignore_index=True)\n",
      "    \n",
      "end = time.time()\n",
      "print end - start"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "12.4079530239\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Run for each `x` and print execution time"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "start = time.time()\n",
      "df=pd.DataFrame(columns=['mH']) #intialize pandas with any column\n",
      "for y in np.logspace(-4, 0., n):\n",
      "    df=df.append(SPhenoPar(y, SPhenoPATH, LesHouchesInputFile)[0],ignore_index=True)\n",
      "    \n",
      "end = time.time()\n",
      "print end - start"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120.674180984\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Store output in stantard format"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.to_csv('output.csv')\n",
      "#pd.read_csv('output.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}