ipython_nbs/day4_2_cython_numba_parakeet.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
      "last updated: 05/28/2014\n",
      "\n",
      "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb?create=1)  \n",
      "- [Link to this IPython notebook on Github](https://github.com/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)  \n",
      "- [Link to the GitHub Repository One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr>\n",
      "I would be happy to hear your comments and suggestions.  \n",
      "Please feel free to drop me a note via\n",
      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).\n",
      "<hr>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Day 4.2 - One Python Benchmark per Day"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Comparing (C)Python compilers - Cython vs. Numba vs. Parakeet on Bubblesort"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "**[-> skip to the results](#Results)**\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "I made some significant changes to the previous [Day 4 benchmark](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_python_cython_numba.ipynb?create=1), thus I decided to make it a separate article:  \n",
      "- added the [parakeet](http://www.parakeetpython.com) compiler  \n",
      "- improved the Bubblesort algorithm to avoid comparing already-sorted pairs  \n",
      "- improved the Cython implementation (avoiding redundant conversion from memory view to array object)  \n",
      "- focussed on only the optimized (\"best\") Cython and Numba implementations of Bubblesort  \n",
      "- used Python 2.7.6 instead of Python 3.4.0 (because of parakeet)  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Quick note about Bubblesort"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I don't want to get into the details about sorting algorithms here, but there is a great report  \n",
      "[\"Sorting in the Presence of Branch Prediction and Caches - Fast Sorting on Modern Computers\"](https://www.cs.tcd.ie/publications/tech-reports/reports.05/TCD-CS-2005-57.pdf) written by Paul Biggar and David Gregg, where they describe and analyze elementary sorting algorithms in very nice detail (see chapter 4).   \n",
      "\n",
      "And for a quick reference, [this website](http://www.sorting-algorithms.com/bubble-sort) has a nice animation of this algorithm."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A long story short: The \"worst-case\" complexity of the Bubblesort algorithm (i.e., \"Big-O\")  \n",
      " $\\Rightarrow \\pmb O(n^2)$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Bubble sort implemented in (C)Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def python_bubblesort(a_list):\n",
      "    \"\"\" Bubblesort in Python for list objects. \"\"\"\n",
      "    length = len(a_list)\n",
      "    swapped = 1\n",
      "    for i in xrange(0, length):\n",
      "        if swapped: \n",
      "            swapped = 0\n",
      "            for ele in xrange(0, length-i-1):\n",
      "                if a_list[ele] > a_list[ele + 1]:\n",
      "                    temp = a_list[ele + 1]\n",
      "                    a_list[ele + 1] = a_list[ele]\n",
      "                    a_list[ele] = temp\n",
      "                    swapped = 1\n",
      "    return a_list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def python_bubblesort_ary(np_ary):\n",
      "    \"\"\" Bubblesort in Python for NumPy arrays. \"\"\"\n",
      "    length = np_ary.shape[0]\n",
      "    swapped = 1\n",
      "    for i in xrange(0, length):\n",
      "        if swapped: \n",
      "            swapped = 0\n",
      "            for ele in xrange(0, length-i-1):\n",
      "                if np_ary[ele] > np_ary[ele + 1]:\n",
      "                    temp = np_ary[ele + 1]\n",
      "                    np_ary[ele + 1] = np_ary[ele]\n",
      "                    np_ary[ele] = temp\n",
      "                    swapped = 1\n",
      "    return np_ary"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Bubble sort implemented in Cython"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
      "Since we are working in an IPython notebook here, we can make use of the very convenient *IPython magic*: It will take care of the conversion to C code, the compilation, and eventually the loading of the function.  \n",
      "\n",
      "Note that the static type declarations that we add via `cdef` are note required for Cython to work, but it will speed things up tremendously."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "import numpy as np\n",
      "cimport numpy as np\n",
      "cimport cython\n",
      "@cython.boundscheck(False) \n",
      "@cython.wraparound(False)\n",
      "cpdef cython_bubblesort(inp_ary):\n",
      "    \"\"\" The Cython implementation of Bubblesort with NumPy memoryview.\"\"\"\n",
      "    cdef unsigned long length, i, swapped, ele, temp\n",
      "    cdef long[:] np_ary = inp_ary\n",
      "    length = np_ary.shape[0]\n",
      "    swapped = 1\n",
      "    for i in xrange(0, length):\n",
      "        if swapped: \n",
      "            swapped = 0\n",
      "            for ele in xrange(0, length-i-1):\n",
      "                if np_ary[ele] > np_ary[ele + 1]:\n",
      "                    temp = np_ary[ele + 1]\n",
      "                    np_ary[ele + 1] = np_ary[ele]\n",
      "                    np_ary[ele] = temp\n",
      "                    swapped = 1\n",
      "    return inp_ary"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Bubble sort implemented in Numba"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up the code. If you want to read more about Numba, please see refer to the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numba import jit as numba_jit\n",
      "@numba_jit\n",
      "def numba_bubblesort(np_ary):\n",
      "    \"\"\" The NumPy implementation of Bubblesort on NumPy arrays.\"\"\"\n",
      "    length = np_ary.shape[0]\n",
      "    swapped = 1\n",
      "    for i in xrange(0, length):\n",
      "        if swapped: \n",
      "            swapped = 0\n",
      "            for ele in xrange(0, length-i-1):\n",
      "                if np_ary[ele] > np_ary[ele + 1]:\n",
      "                    temp = np_ary[ele + 1]\n",
      "                    np_ary[ele + 1] = np_ary[ele]\n",
      "                    np_ary[ele] = temp\n",
      "                    swapped = 1\n",
      "    return np_ary"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Bubble sort implemented in  parakeet"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Similar to Numba,  [parakeet](http://www.parakeetpython.com) is a Python compiler that optimizes the runtime of numerical computations based on the NumPy data types, such as NumPy arrays.\n",
      "\n",
      "The usage is also similar to Numba where we just have to put the `jit` decorator on top of the function we want to optimize."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from parakeet import jit as para_jit\n",
      "@para_jit\n",
      "def parakeet_bubblesort(np_ary):\n",
      "    \"\"\" The parakeet implementation of Bubblesort on NumPy arrays.\"\"\"\n",
      "    length = np_ary.shape[0]\n",
      "    swapped = 1\n",
      "    for i in xrange(0, length):\n",
      "        if swapped: \n",
      "            swapped = 0\n",
      "            for ele in xrange(0, length-i-1):\n",
      "                if np_ary[ele] > np_ary[ele + 1]:\n",
      "                    temp = np_ary[ele + 1]\n",
      "                    np_ary[ele + 1] = np_ary[ele]\n",
      "                    np_ary[ele] = temp\n",
      "                    swapped = 1\n",
      "    return np_ary"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Verifying that all implementations work correctly"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import random\n",
      "import copy\n",
      "import numpy as np\n",
      "random.seed(4354353)\n",
      "\n",
      "l = np.asarray([random.randint(1,1000) for num in xrange(1, 1000)])\n",
      "l_sorted = np.sort(l)\n",
      "for f in [python_bubblesort, python_bubblesort_ary, cython_bubblesort, \n",
      "          numba_bubblesort, parakeet_bubblesort]:\n",
      "    assert(l_sorted.all() == f(copy.copy(l)).all())\n",
      "print('Bubblesort works correctly')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Bubblesort works correctly\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Timing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "import copy\n",
      "import numpy as np\n",
      "\n",
      "funcs = ['python_bubblesort',\n",
      "         'python_bubblesort_ary',\n",
      "         'cython_bubblesort',\n",
      "         'numba_bubblesort',\n",
      "         'parakeet_bubblesort'\n",
      "         ]\n",
      "\n",
      "orders_n = [10**n for n in range(1, 6)]\n",
      "timings = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    l = [np.random.randint(n) for num in range(n)]\n",
      "    for f in funcs:\n",
      "        l_copy = copy.deepcopy(l)\n",
      "        if f != 'python_bubblesort':\n",
      "            l_copy = np.asarray(l_copy)\n",
      "        timings[f].append(min(timeit.Timer('%s(l_copy)' %f, \n",
      "                      'from __main__ import %s, l_copy' %f)\n",
      "                              .repeat(repeat=3, number=10)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Setting up the plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import platform\n",
      "import multiprocessing\n",
      "from cython import __version__ as cython__version__\n",
      "from llvm import __version__ as llvm__version__\n",
      "from numba import __version__ as numba__version__\n",
      "from parakeet import __version__ as parakeet__version__\n",
      "\n",
      "def print_sysinfo():\n",
      "    \n",
      "    print '\\nPython version  :', platform.python_version()\n",
      "    print 'compiler        :', platform.python_compiler()\n",
      "    print 'Cython version  :', cython__version__\n",
      "    print 'NumPy version   :', np.__version__\n",
      "    print 'Numba version   :', numba__version__\n",
      "    print 'llvm version    :', llvm__version__\n",
      "    print 'parakeet version:', parakeet__version__\n",
      "    \n",
      "    print '\\nsystem     :', platform.system()\n",
      "    print 'release    :', platform.release()\n",
      "    print 'machine    :', platform.machine()\n",
      "    print 'processor  :', platform.processor()\n",
      "    print 'CPU count  :', multiprocessing.cpu_count()\n",
      "    print 'interpreter:', platform.architecture()[0]\n",
      "    print '\\n\\n'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "def plot(timings, title, ranked_labels, labels, orders_n):\n",
      "    plt.rcParams.update({'font.size': 12})\n",
      "\n",
      "    fig = plt.figure(figsize=(11,10))\n",
      "    for lb in ranked_labels:\n",
      "        plt.plot(orders_n, timings[lb], alpha=0.5, label=labels[lb], \n",
      "                 marker='o', lw=3)\n",
      "    plt.xlabel('sample size n (items in the list)')\n",
      "    plt.ylabel('time per computation in milliseconds')\n",
      "    plt.xlim([min(orders_n) / 10, max(orders_n)* 10])\n",
      "    plt.legend(loc=2)\n",
      "    plt.grid()\n",
      "    plt.xscale('log')\n",
      "    plt.yscale('log')\n",
      "    plt.title(title)\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettytable\n",
      "\n",
      "def summary_table(ranked_labels):\n",
      "    fit_table = prettytable.PrettyTable(['n=%s' %orders_n[-1], \n",
      "                                         'bubblesort function' ,\n",
      "                                         'time in millisec.',\n",
      "                                         'rel. performance gain'])\n",
      "    fit_table.align['bubblesort function'] = 'l'\n",
      "    for entry in ranked_labels:\n",
      "        fit_table.add_row(['', labels[entry[1]], round(entry[0]*100, 3), \n",
      "                           round(ranked_labels[0][0]/entry[0], 2)])\n",
      "        # times 100 for converting from seconds to milliseconds: (time*1000 / 10-loops)\n",
      "    print(fit_table)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "title = 'Performance of Bubblesort in Python, Cython, parakeet, and Numba'\n",
      "\n",
      "labels = {'python_bubblesort':'(C)Python Bubblesort - Python lists', \n",
      "          'python_bubblesort_ary':'(C)Python Bubblesort - NumPy arrays',  \n",
      "          'cython_bubblesort': 'Cython Bubblesort - NumPy arrays',\n",
      "          'numba_bubblesort': 'Numba Bubblesort - NumPy arrays',\n",
      "          'parakeet_bubblesort': 'parakeet Bubblesort - NumPy arrays'\n",
      "          }\n",
      "\n",
      "ranked_by_time = sorted([(time[1][-1],time[0]) for time in timings.items()], reverse=True)\n",
      "\n",
      "print_sysinfo()\n",
      "plot(timings, title, [l for t,l in ranked_by_time], labels, orders_n)\n",
      "summary_table(ranked_by_time)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Python version  : 2.7.6\n",
        "compiler        : GCC 4.0.1 (Apple Inc. build 5493)\n",
        "Cython version  : 0.20.1\n",
        "NumPy version   : 1.8.0\n",
        "Numba version   : 0.12.1\n",
        "llvm version    : 0.12.3\n",
        "parakeet version: 0.23.2\n",
        "\n",
        "system     : Darwin\n",
        "release    : 13.2.0\n",
        "machine    : x86_64\n",
        "processor  : i386\n",
        "CPU count  : "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4\n",
        "interpreter: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "64bit\n",
        "\n",
        "\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3FdeSkhL8/fffePnyJUpKSvDixQuUlJRAT08P/fv3x6xZs/Ds2TMcOXIEu3bt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\naBEdOnQgOjqadevWlel7v8ywYcN48803USgULx2ZwsHBAQMDAxYsWMC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SQqGQVq5cKb+3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aiuiDMNZ7AqahGvBg1psWnEVsV6OTIhug+n/ryMHj2aX//619TX1wNQV1fHa6+9xmgvHXty/Phx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\nMqsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a59eb90>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+----------+-------------------------------------+-------------------+-----------------------+\n",
        "| n=100000 | bubblesort function                 | time in millisec. | rel. performance gain |\n",
        "+----------+-------------------------------------+-------------------+-----------------------+\n",
        "|          | (C)Python Bubblesort - NumPy arrays |       42.019      |          1.0          |\n",
        "|          | (C)Python Bubblesort - Python lists |       13.26       |          3.17         |\n",
        "|          | Numba Bubblesort - NumPy arrays     |       0.307       |         136.69        |\n",
        "|          | parakeet Bubblesort - NumPy arrays  |       0.277       |         151.63        |\n",
        "|          | Cython Bubblesort - NumPy arrays    |       0.141       |         297.2         |\n",
        "+----------+-------------------------------------+-------------------+-----------------------+\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that the relative results also depend on what version of Python, Cython, Numba, parakeet, and NumPy you are using. Also, the compiler choice for installing NumPy can account for differences in the results.  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}