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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
      "\n",
      "last updated: 05/21/2014\n",
      "\n",
      "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_2_jit_numpy.ipynb?create=1)  \n",
      "\n",
      "- [Link to this IPython notebook on Github](https://github.com/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_2_jit_numpy.ipynb)  \n",
      "\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 7.2 - One Python Benchmark per Day"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Using just-in-time compilers for speeding up NumPy array expressions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is a follow-up to day 7, where I compared NumPy with Numexpr for various different operators. Now, I only want focus on one expression that was listed in the [Numexpr documentation](https://github.com/pydata/numexpr/blob/master/README.rst) (which supposedly show off its strengths), and compare the performance of Numexpr to the two other JIT (just-in-time) compilers Numba and parakeet."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr>\n",
      "**-> [skip to the results](#Results)**\n",
      "<hr>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**For this benchmark, we will compare different arithmetic operators for simple matrix calculations using NumPy arrays to their Numexpr equivalents.**\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Test function in NumPy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def numpy_regular(A, B):\n",
      "    return(A*B-4.1*A > 2.5*B)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Test function in Numexpr\n",
      "\n",
      "[Numexpr](https://pypi.python.org/pypi/numexpr/2.4) is a \"fast numerical expression evaluator for NumPy\". It has a internal Virtual Machine to rewrite code more efficiently by breaking down operations into smaller chunks for the CPU cache. By using a JIT (just-in-time compiler), compilation at runtime is not necessary.\n",
      "\n",
      "\n",
      "\n",
      "The usage is quite simple, we just have to provide the numerical expression that we want to evaluate as a string to the `evaluate` function, e.g., \n",
      "\n",
      "    import numexpr\n",
      "    import numpy\n",
      "    a = numpy.array([[1, 2]])\n",
      "    numexpr.evaluate('a ** 2')\n",
      "    >> array([[1, 4]], dtype=int64)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numexpr as ne"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def numexpr_evaluate(A, B):\n",
      "    return ne.evaluate('A*B-4.1*A > 2.5*B')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Test function in Numba\n",
      "\n",
      "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": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "Continuum Analytics [write on their website](http://numba.pydata.org/numba-doc/dev/arrays.html) that \"the NumPy array ufunc$^1$ support in nopython node is incomplete at this time. Most if not all ufuncs should work in object mode though.\"  \n",
      "\n",
      "\n",
      "$^1$ \"A universal function (or ufunc for short) is a function that operates on ndarrays in an element-by-element fashion   \n",
      "([http://docs.scipy.org/doc/numpy/reference/ufuncs.html](http://docs.scipy.org/doc/numpy/reference/ufuncs.html))"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numba import jit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@jit\n",
      "def numba_jit(A, B):\n",
      "    return A*B-4.1*A > 2.5*B"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numba import vectorize"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@vectorize(['u1(float64, float64)'])\n",
      "def numba_vectorized(A,B):\n",
      "    return A*B+4.1*A > 2.5*B"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Test function in parakeet\n",
      "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"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@para_jit\n",
      "def parakeet_jit(A, B):\n",
      "    return A*B-4.1*A > 2.5*B"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Test function in Cython\n",
      "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": 11
    },
    {
     "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_unrolled(double[:, :] A, double[:, :] B):\n",
      "    cdef long i, j, rows, cols\n",
      "    cdef double[:, :] C\n",
      "    rows = A.shape[0]\n",
      "    cols = A.shape[1]\n",
      "    C = np.empty((rows,cols,))\n",
      "    for i in xrange(rows):\n",
      "        for j in xrange(cols):\n",
      "            C[i,j] = A[i,j]*B[i,j] + 4.1*A[i,j] > 2.5*B[i,j]\n",
      "\n",
      "    return np.asarray(C)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Timing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import timeit\n",
      "\n",
      "orders_n = [10**n for n in range(1, 5)]\n",
      "\n",
      "funcs = ['numpy_regular', 'numba_jit', 'numba_vectorized',\n",
      "         'parakeet_jit', 'cython_unrolled', 'numexpr_evaluate'\n",
      "        ]\n",
      "\n",
      "timings = {f:[] for f in funcs}\n",
      "\n",
      "for n in orders_n:\n",
      "    for f in funcs:\n",
      "        A = np.random.rand(n,n)\n",
      "        B = np.random.rand(n,n)\n",
      "        timings[f].append(min(timeit.Timer('%s(A, B)' %f, \n",
      "                      'from __main__ import A, B, %s' %f)\n",
      "                              .repeat(repeat=3, number=10)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Setting up plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import platform\n",
      "from llvm import __version__ as llvm__version__\n",
      "from parakeet import __version__ as parakeet__version__\n",
      "from numba import __version__ as numba__version__\n",
      "\n",
      "def print_sysinfo():\n",
      "    print '\\nsystem   :', platform.system()\n",
      "    print 'release  :', platform.release()\n",
      "    print 'machine  :', platform.machine()\n",
      "    print 'processor:', platform.processor()\n",
      "    print 'interpreter:', platform.architecture()[0]\n",
      "\n",
      "    print '\\nPython version', platform.python_version()\n",
      "    print 'compiler', platform.python_compiler()\n",
      "    print 'NumPy version', np.__version__\n",
      "    print 'Numexpr version', ne.__version__\n",
      "    print 'parakeet version', parakeet__version__\n",
      "    print 'Numba version', numba__version__\n",
      "    print 'llvm version', llvm__version__\n",
      "    print '\\n\\n'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettytable\n",
      "\n",
      "def summary_table(funcs):\n",
      "    fit_table = prettytable.PrettyTable(['n=%s' %orders_n[-1], \n",
      "                                         'function' ,\n",
      "                                         'rel. performance gain'])\n",
      "    fit_table.align['function'] = 'l'\n",
      "    for f in funcs:\n",
      "        fit_table.add_row(['', f, '{:.2f}x'.format(\n",
      "            (timings['numpy_regular'][-1]/timings[f][-1]))])\n",
      "    print(fit_table)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "def plot_figures(funcs): \n",
      "    fig = plt.figure(figsize=(8,8))\n",
      "    for f in funcs:\n",
      "        plt.plot([i**2 for i in orders_n], [i*100 for i in timings[f]], \n",
      "                            alpha=0.5, label='%s' %f, marker='o', lw=2)\n",
      "    plt.legend(loc='upper left')\n",
      "    plt.xscale('log')\n",
      "    plt.yscale('log')\n",
      "    plt.grid()\n",
      "    plt.xlabel('number of elements per matrix')\n",
      "    plt.ylabel('time in milliseconds')\n",
      "    plt.title('Approaches to evaluate the NumPy array expression \"A*B-4.1*A > 2.5*B\"')\n",
      "        \n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_sysinfo()\n",
      "summary_table(funcs)\n",
      "plot_figures(funcs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "system   : Darwin\n",
        "release  : 13.1.0\n",
        "machine  : x86_64\n",
        "processor: i386\n",
        "interpreter: 64bit\n",
        "\n",
        "Python version 2.7.6\n",
        "compiler GCC 4.0.1 (Apple Inc. build 5493)\n",
        "NumPy version 1.8.1\n",
        "Numexpr version 2.4\n",
        "parakeet version 0.24\n",
        "Numba version 0.13.1\n",
        "llvm version 0.12.4\n",
        "\n",
        "\n",
        "\n",
        "+---------+------------------+-----------------------+\n",
        "| n=10000 | function         | rel. performance gain |\n",
        "+---------+------------------+-----------------------+\n",
        "|         | numpy_regular    |         1.00x         |\n",
        "|         | numba_jit        |         1.02x         |\n",
        "|         | numba_vectorized |         4.23x         |\n",
        "|         | parakeet_jit     |         11.92x        |\n",
        "|         | cython_unrolled  |         4.04x         |\n",
        "|         | numexpr_evaluate |         9.17x         |\n",
        "+---------+------------------+-----------------------+\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Z+mAwGLWD/d2pBrOTajA71UxERETdtvH9w2sxx89gMBgMxutO6Xq2smfYvAps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\ndI1OQkKCUx4kG3WpPzIykgMHDpCXl4dWq+Xtt98mOjqaXbt2VWznW7J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TWV9kYSu14dHZ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1077de910>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import multiprocessing\n",
      "print('This benchmark was done on a machine with %s CPUs', multiprocessing.cpu_count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "This benchmark was done on a machine with 2 CPUs\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}