COMPSs_Manuals/source/Sections/09_PyCOMPSs_Notebooks/demos/Mandelbrot_numba.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Accelerating parallel code with PyCOMPSs and Numba\n",
    "\n",
    "## Demo Supercomputing 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "What is mandelbrot?\n",
    "\n",
    "The mandelbrot set is a fractal, which is plotted on the complex plane. It shows how intrincate can be formed from a simple equation.\n",
    "\n",
    "It is generated using the algorithm:\n",
    "\n",
    "\\begin{align}\n",
    "Z_{n+1} & = z_{n}^2 + A \\\\\n",
    "\\end{align}\n",
    "\n",
    "Where *Z* and *A* are complex numbers, and *n* represents the number of iterations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, import time to measure the elapsed execution times and create an ordered dictionary to keep all measures -> we are going to measure and plot the performance with different conditions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from collections import OrderedDict\n",
    "times = OrderedDict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then, all required imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import NaN, arange, abs, array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mandelbrot set implementation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mandelbrot(a, max_iter):\n",
    "    z = 0\n",
    "    for n in range(1, max_iter):\n",
    "        z = z**2 + a\n",
    "        if abs(z) > 2:\n",
    "            return n\n",
    "    return NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mandelbrot_set(y, X, max_iter):\n",
    "    Z = [0 for _ in range(len(X))]\n",
    "    for ix, x in enumerate(X):\n",
    "        Z[ix] = mandelbrot(x + 1j * y, max_iter)\n",
    "    return Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Main function to generate the mandelbrot set. It splits the space in vertical chunks, and calculates the mandelbrot set of each one, generating the result *Z*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_mandelbrot(X, Y, max_iter):\n",
    "    st = time.time()\n",
    "    Z = [[] for _ in range(len(Y))]\n",
    "    for iy, y in enumerate(Y):\n",
    "        Z[iy] = mandelbrot_set(y, X, max_iter)\n",
    "    elapsed = time.time() - st\n",
    "    print(\"Elapsed time (s): {}\".format(elapsed))\n",
    "    return Z, elapsed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function plots the fractal inline (the coerced parameter *<Boolean\\>* is used to set *NaN* in coerced elements within *Z*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "def plot_fractal(Z, coerced):\n",
    "    if coerced:\n",
    "        Z = [[NaN if c == -2**63 else c for c in row] for row in Z]\n",
    "    import matplotlib.pyplot as plt\n",
    "    Z = array(Z)\n",
    "    plt.imshow(Z, cmap='plasma')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a benchmarking function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_fractal(coerced=False):\n",
    "    X = arange(-2, .5, .01)\n",
    "    Y = arange(-1.0,  1.0, .01)\n",
    "    max_iterations = 2000\n",
    "    Z, elapsed = run_mandelbrot(X, Y, max_iterations)\n",
    "    plot_fractal(Z, coerced)\n",
    "    return elapsed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the previous code **sequentially**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time (s): 61.640522480010986\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "times['Sequential'] = generate_fractal()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paralellization with PyCOMPSs\n",
    "\n",
    "After analysing the code, each mandelbrot set can be considered as a task, requiring only to decorate the ``mandelbrot_set`` function. It is interesting to observe that all sets are independent among them, so they can be computed completely independently, enabling to exploit multiple resources concurrently.\n",
    "\n",
    "In order to run this code with we need first to start the **COMPSs** runtime:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************\n",
      "**************** PyCOMPSs Interactive ******************\n",
      "********************************************************\n",
      "*          .-~~-.--.           ______        ______    *\n",
      "*         :         )         |____  \\      /  __  \\   *\n",
      "*   .~ ~ -.\\       /.- ~~ .      __) |      | |  | |   *\n",
      "*   >       `.   .'       <     |__  |      | |  | |   *\n",
      "*  (         .- -.         )   ____) |   _  | |__| |   *\n",
      "*   `- -.-~  `- -'  ~-.- -'   |______/  |_| \\______/   *\n",
      "*     (        :        )           _ _ .-:            *\n",
      "*      ~--.    :    .--~        .-~  .-~  }            *\n",
      "*          ~-.-^-.-~ \\_      .~  .-~   .~              *\n",
      "*                   \\ \\ '     \\ '_ _ -~                *\n",
      "*                    \\`.\\`.    //                      *\n",
      "*           . - ~ ~-.__\\`.\\`-.//                       *\n",
      "*       .-~   . - ~  }~ ~ ~-.~-.                       *\n",
      "*     .' .-~      .-~       :/~-.~-./:                 *\n",
      "*    /_~_ _ . - ~                 ~-.~-._              *\n",
      "*                                     ~-.<             *\n",
      "********************************************************\n",
      "* - Starting COMPSs runtime...                         *\n",
      "* - Log path : /home/user/.COMPSs/Interactive_23/\n",
      "* - PyCOMPSs Runtime started... Have fun!              *\n",
      "********************************************************\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pycompss.interactive as ipycompss\n",
    "if 'BINDER_SERVICE_HOST' in os.environ:\n",
    "    ipycompss.start(project_xml='../xml/project.xml',\n",
    "                    resources_xml='../xml/resources.xml')\n",
    "else:\n",
    "    ipycompss.start(graph=False, trace=True, monitor=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is necessary to decorate the ``mandelbrot_set`` function with the ``@task`` decorator.\n",
    "\n",
    "Note that the ``mandelbrot_set`` function returns a list of elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycompss.api.task import task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "@task(returns=list)\n",
    "def mandelbrot_set(y, X, max_iter):\n",
    "    Z = [0 for _ in range(len(X))]\n",
    "    for ix, x in enumerate(X):\n",
    "        Z[ix] = mandelbrot(x + 1j * y, max_iter)\n",
    "    return Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, include the synchronization of ``Z`` with ``compss_wait_on``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycompss.api.api import compss_wait_on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_mandelbrot(X, Y, max_iter):\n",
    "    st = time.time()\n",
    "    Z = [[] for _ in range(len(Y))]\n",
    "    for iy, y in enumerate(Y):\n",
    "        Z[iy] = mandelbrot_set(y, X, max_iter)\n",
    "    Z = compss_wait_on(Z)\n",
    "    elapsed = time.time() - st\n",
    "    print(\"Elapsed time (s): {}\".format(elapsed))\n",
    "    return Z, elapsed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the benchmark with **PyCOMPSs**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found task: mandelbrot_set\n",
      "Elapsed time (s): 29.189019441604614\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "times['PyCOMPSs'] = generate_fractal()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accelerating the tasks with Numba\n",
    "\n",
    "To this end, it is necessary to either use:\n",
    "1. the Numba's ``@jit`` decorator under the PyCOMPSs ``@task`` decorator\n",
    "2. or define the ``numba=True`` within the ``@task`` decorator.\n",
    "\n",
    "First, we decorate the inner function (``mandelbrot``) with ``@jit`` since it is also a target function to be optimized with Numba."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import jit\n",
    "\n",
    "@jit\n",
    "def mandelbrot(a, max_iter):\n",
    "    z = 0\n",
    "    for n in range(1, max_iter):\n",
    "        z = z**2 + a\n",
    "        if abs(z) > 2:\n",
    "            return n\n",
    "    return NaN # NaN is coerced by Numba"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Option 1* - Add the ``@jit`` decorator explicitly under ``@task`` decorator"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "@task(returns=list)\n",
    "@jit\n",
    "def mandelbrot_set(y, X, max_iter):\n",
    "    Z = [0 for _ in range(len(X))]\n",
    "    for ix, x in enumerate(X):\n",
    "        Z[ix] = mandelbrot(x + 1j * y, max_iter)\n",
    "    return Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Option 2* - Add the ``numba=True`` flag within ``@task`` decorator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "@task(returns=list, numba=True)\n",
    "def mandelbrot_set(y, X, max_iter):\n",
    "    Z = [0 for _ in range(len(X))]\n",
    "    for ix, x in enumerate(X):\n",
    "        Z[ix] = mandelbrot(x + 1j * y, max_iter)\n",
    "    return Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the benchmark with **Numba**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found task: mandelbrot_set\n",
      "Elapsed time (s): 12.679550170898438\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "times['PyCOMPSs + Numba'] = generate_fractal(coerced=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.bar(*zip(*times.items()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stop **COMPSs** runtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************\n",
      "*************** STOPPING PyCOMPSs ******************\n",
      "********************************************************\n",
      "Checking if any issue happened.\n",
      "Warning: some of the variables used with PyCOMPSs may\n",
      "         have not been brought to the master.\n",
      "********************************************************\n"
     ]
    }
   ],
   "source": [
    "ipycompss.stop()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  },
  "livereveal": {
   "auto_select": "code",
   "overlay": "<div class='myheader'><img height='100' align='right' src='http://www.bsc.es/actris/sites/default/files/logos/BSC-logo%201.jpg'></div>",
   "scroll": true,
   "theme": "simple",
   "transition": "slide"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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  "widgets": {
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    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
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 },
 "nbformat": 4,
 "nbformat_minor": 1
}