{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Speed up by parallel processing\n",
    "\n",
    "* This [blogpost on processes and threads](https://medium.com/@bfortuner/python-multithreading-vs-multiprocessing-73072ce5600b) gives a good introduction and has a nice notebook with some simple experiments that you can run on your own machine.\n",
    "    * **Your turn** In [the notebook connected to the post](https://github.com/bfortuner/ml-study/blob/master/multitasking_python.ipynb) experiments are done with 1,2 and 4 threads and processes. Now write a function which executes an experiment with i processes or threads for i in range(1,N) and which outputs for each i the runtime. Plot these runtimes and see when adding threads or processes actually starts to slow things down (on your computer). \n",
    "    * Run this for the various tasks mentioned in the blogpost and possibly for a task you added yourself and plot all the runtimes of the different tasks together in one graph (each task a separate line). Pandas plotting seems well suited for this.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}