python-vs-numpy.ipynb

python3-vs-numpy.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def python_matrix_mult(A, B, C):\n    for i in range(size):\n        for j in range(size):\n            for k in range(size):\n                C[i][j] += A[i][k] * B[k][j]\n    return C",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def numpy_matrix_mult(A, B):\n    return np.dot(A, B)",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "size = 1024\nA = np.random.rand(size, size)\nB = np.random.rand(size, size)\nC = np.zeros((size, size))",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "python_time = %timeit -o python_matrix_mult(A.tolist(), B.tolist(), C.tolist())",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": "3min 35s ± 9.31 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "numpy_time = %timeit -o numpy_matrix_mult(A, B)",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": "24.5 ms ± 966 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(\"numpy's absolute speedup: {:.0f}\".format(python_time.average / numpy_time.average))",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": "numpy's absolute speedup: 8788\n",
          "name": "stdout"
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.2",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "4a2cd4cf5090b1c1801e64624bb760e9",
      "data": {
        "description": "python-vs-numpy.ipynb",
        "public": true
      }
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/4a2cd4cf5090b1c1801e64624bb760e9"
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  },
  "nbformat": 4,
  "nbformat_minor": 2
}