{ "metadata": { "name": "", "signature": "sha256:238dbef6f3b00f719aba8df1bf4becdb9a2ef44aa22b675d26c0ced39c7e618e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Sebastian Raschka](http://sebastianraschka.com) \n", "last updated: 05/17/2014\n", "\n", "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_python_cython_numba.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_python_cython_numba.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": [ "
\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", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Day 4 - One Python Benchmark per Day" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (C)Python vs. Cython vs. Numba" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "**-> [skip to the results](#Results)**\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quick note about bubble sort" ] }, { "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 bubble sort algorithm (i.e., \"Big-O\") \n", " $\\Rightarrow \\pmb O(n^2)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bubble sort implemented in (C)Python" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def python_bubblesort(a_list):\n", " \"\"\" Plain Python implementation of bubble sort (sorts in place) \"\"\"\n", " count = len(a_list)\n", " for i in range(count):\n", " for j in range(1, count):\n", " if a_list[j] < a_list[j-1]:\n", " a_list[j-1], a_list[j] = a_list[j], a_list[j-1]\n", " return a_list" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import random\n", "random.seed(12345)\n", "\n", "l = [random.randint(1,100) for num in range(1, 100)]\n", "assert(sorted(l) == python_bubblesort(l))\n", "print('bubble sort works correctly')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "bubble sort works correctly\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
" ] }, { "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", "\n", "**If you want to know how to use Cython without the IPython magic, I have a short outline of the procedure [here](http://sebastianraschka.com/IPython_htmls/cython_least_squares.html#cython_bonus).**\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", "cimport cython\n", "cpdef cython_bubblesort_notypes(a_list):\n", " \"\"\" \n", " The Python implementation of bubble sort compiled\n", " via Cython without C type definitions.\n", " \n", " \"\"\"\n", " count = len(a_list)\n", " for i in range(count):\n", " for j in range(1, count):\n", " if a_list[j] < a_list[j-1]:\n", " a_list[j-1], a_list[j] = a_list[j], a_list[j-1]\n", " return a_list" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "cimport cython\n", "cpdef cython_bubblesort_ctypes(a_list):\n", " \"\"\" \n", " Cython implementation of bubble sort with static\n", " type declarations.\n", " \n", " \"\"\"\n", " cdef int count, i, j # static type declarations\n", " count = len(a_list)\n", " for i in range(count):\n", " for j in range(1, count):\n", " if a_list[j] < a_list[j-1]:\n", " a_list[j-1], a_list[j] = a_list[j], a_list[j-1]\n", " return a_list" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "cimport cython\n", "from libc.stdlib cimport malloc, free\n", "\n", "cpdef cython_bubblesort_clist(a_list):\n", " \"\"\" \n", " The Cython implementation of bubble sort with internal\n", " conversion between Python list objects and C arrays.\n", " \n", " \"\"\"\n", " cdef int *c_list\n", " c_list = malloc(len(a_list)*cython.sizeof(int))\n", " cdef int count, i, j # static type declarations\n", " count = len(a_list)\n", " \n", " # convert Python list to C array\n", " for i in range(count):\n", " c_list[i] = a_list[i]\n", "\n", " for i in range(count):\n", " for j in range(1, count):\n", " if c_list[j] < c_list[j-1]:\n", " c_list[j-1], c_list[j] = c_list[j], c_list[j-1]\n", "\n", " # convert C array back to Python list\n", " for i in range(count):\n", " a_list[i] = c_list[i]\n", " \n", " free(c_list)\n", " return a_list" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "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_numpy(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 range(0, length):\n", " if swapped: \n", " swapped = 0\n", " for ele in range(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": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
" ] }, { "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\n", "@jit\n", "def numba_bubblesort(a_list):\n", " \"\"\" Numba implementation of bubble sort (sorts in place) \"\"\"\n", " count = len(a_list)\n", " for i in range(count):\n", " for j in range(1, count):\n", " if a_list[j] < a_list[j-1]:\n", " a_list[j-1], a_list[j] = a_list[j], a_list[j-1]\n", " return a_list" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "from numba import jit\n", "@jit\n", "def numba_bubblesort_numpy(np_ary):\n", " \"\"\" \n", " Numba implementation of bubble sort (sorts in place),\n", " It is identical to numba_bubble_sort(), but we will feed\n", " a numpy array type to this function.\n", " \n", " \"\"\"\n", " count = np_ary.shape[0]\n", " for i in range(count):\n", " for j in range(1, count):\n", " if np_ary[j] < np_ary[j-1]:\n", " np_ary[j-1], np_ary[j] = np_ary[j], np_ary[j-1]\n", " return np_ary" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
" ] }, { "cell_type": "markdown", "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(12345)\n", "\n", "l = np.asarray([random.randint(1,1000) for num in range(1, 1000)])\n", "l_sorted = np.sort(l)\n", "for f in [python_bubblesort, cython_bubblesort_ctypes, \n", " cython_bubblesort_notypes, cython_bubblesort_clist, \n", " cython_bubblesort_numpy, numba_bubblesort, \n", " numba_bubblesort_numpy]:\n", " assert(l_sorted.all() == f(copy.copy(l)).all())\n", "print('Bubblesort works correctly')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Bubblesort works correctly\n" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Timing" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import timeit\n", "import random\n", "import copy\n", "import numpy as np\n", "random.seed(4567)\n", "\n", "def make_copy(l):\n", " return copy.deepcopy(l)\n", "\n", "funcs = ['python_bubblesort', 'numba_bubblesort', \n", " 'cython_bubblesort_notypes', 'cython_bubblesort_ctypes', \n", " 'cython_bubblesort_clist', 'cython_bubblesort_numpy', \n", " 'numba_bubblesort_numpy'\n", " ]\n", "\n", "orders_n = [10**n for n in range(1, 5)]\n", "timings = {f:[] for f in funcs}\n", "\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 = make_copy(l)\n", " if f in ['numba_bubblesort_numpy', 'cython_bubblesort_numpy']:\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=5, number=10)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Preparing plots" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import platform\n", "import multiprocessing\n", "from llvm import __version__ as llvm__version__\n", "from numba import __version__ as numba__version__\n", "from cython import __version__ as cython__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", " \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": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "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 for 10 loops in seconds')\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": 14 }, { "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 sec.',\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], 2), \n", " round(ranked_labels[0][0]/entry[0], 2)])\n", " print(fit_table)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "collapsed": false, "input": [ "title = 'Performance of bubblesort in Python, Cython, and Numba'\n", "\n", "labels = {'python_bubblesort':'(C)Python - Python lists', \n", " 'numba_bubblesort': 'Numba - Python lists', \n", " 'cython_bubblesort_ctypes': 'Cython - with C types, Python lists',\n", " 'cython_bubblesort_notypes': 'Cython - no type declarations, Python lists',\n", " 'cython_bubblesort_clist': 'Cython - Python lists with to-C-array-conversion',\n", " 'cython_bubblesort_numpy': 'Cython - NumPy arrays memoryview',\n", " 'numba_bubblesort_numpy': 'Numba - NumPy arrays'\n", " }\n", "\n", "ranked_by_time = sorted([(time[1][-1],time[0]) for time in timings.items()], reverse=True)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Results" ] }, { "cell_type": "code", "collapsed": false, "input": [ "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: 3.4.1\n", "compiler : GCC 4.2.1 (Apple Inc. build 5577)\n", "Cython version : 0.20.1\n", "NumPy version : 1.8.1\n", "Numba version : 0.13.1\n", "llvm version : 0.12.4\n", "\n", "system :" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " Darwin\n", "release : 13.1.0\n", "machine : x86_64\n", "processor: i386\n", "CPU count : 2\n", "interpreter:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 64bit\n", "\n", "\n", "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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KULjzYgNCwZyEuFd7AMNNyCnoWpcQ1vZmiOfJN4hnVWEwlr0L8Uwra3Ruiia/yxH1cgvx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\nQ21tDWpnmS0lriIiIiJSEFTjKiI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"text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "+---------+--------------------------------------------------+--------------+-----------------------+\n", "| n=10000 | bubblesort function | time in sec. | rel. performance gain |\n", "+---------+--------------------------------------------------+--------------+-----------------------+\n", "| | (C)Python - Python lists | 258.22 | 1.0 |\n", "| | Numba - Python lists | 207.95 | 1.24 |\n", "| | Cython - no type declarations, Python lists | 166.52 | 1.55 |\n", "| | Cython - with C types, Python lists | 51.9 | 4.98 |\n", "| | Numba - NumPy arrays | 2.56 | 101.03 |\n", "| | Cython - Python lists with to-C-array-conversion | 0.91 | 283.61 |\n", "| | Cython - NumPy arrays memoryview | 0.0 | 1106177.69 |\n", "+---------+--------------------------------------------------+--------------+-----------------------+\n" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the relative results also depend on what version of Python, Cython, and NumPy you are using. Also, the compiler choice for installing NumPy can account for differences in the results. " ] } ], "metadata": {} } ] }