4b_Runtime.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OUTDATED, the examples moved to the gallery\n",
    "## See https://empymod.github.io/emg3d-gallery\n",
    "\n",
    "----\n",
    "\n",
    "# Runtime\n",
    "\n",
    "Measure runtime usage of `emg3d` as a function of model size (using 1 thread)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import emg3d\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "plt.matplotlib.rcParams['figure.dpi'] = 300\n",
    "plt.matplotlib.rcParams['savefig.dpi'] = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  == 32^3 ~ 3.28e+04 cells ==\n",
      "\n",
      "  CPU:   1.3 s\n",
      "\n",
      "  == 48^3 ~ 1.11e+05 cells ==\n",
      "\n",
      "  CPU:   4.5 s\n",
      "\n",
      "  == 64^3 ~ 2.62e+05 cells ==\n",
      "\n",
      "  CPU:  10.6 s\n",
      "\n",
      "  == 96^3 ~ 8.85e+05 cells ==\n",
      "\n",
      "  CPU:  36.3 s\n",
      "\n",
      "  == 128^3 ~ 2.10e+06 cells ==\n",
      "\n",
      "  CPU:  90.6 s\n",
      "\n",
      "  == 192^3 ~ 7.08e+06 cells ==\n",
      "\n",
      "  CPU: 311.9 s\n",
      "\n",
      "  == 256^3 ~ 1.68e+07 cells ==\n",
      "\n",
      "  CPU: 758.7 s\n",
      "\n",
      "  == 384^3 ~ 5.66e+07 cells ==\n",
      "\n",
      "  CPU: 2727.8 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nsizes = np.array([32, 48, 64, 96, 128, 192, 256, 384])\n",
    "cpu_average = np.zeros(nsizes.shape)\n",
    "\n",
    "for i, nx in enumerate(nsizes):\n",
    "    \n",
    "    print(f\"  == {nx}^3 ~ {nx**3:4.2e} cells ==\\n\")\n",
    "\n",
    "    # Grid\n",
    "    hx = np.ones(nx)*50\n",
    "    x0 = -nx//2*50\n",
    "    grid = emg3d.utils.TensorMesh([hx, hx, hx], x0=(x0, x0, x0))\n",
    "\n",
    "    # Source location and frequency\n",
    "    src = [0, 0, 0, 0, 0] \n",
    "    freq = 1.0\n",
    "        \n",
    "    # Model and source field\n",
    "    model = emg3d.utils.Model(grid, 1., freq=freq)\n",
    "    sfield = emg3d.utils.get_source_field(grid, src, freq=freq, strength=0)\n",
    "    \n",
    "    if nx < 96:  # r = 1; n estimated by timeit\n",
    "        cpu = %timeit -r 1 -o -q emg3d.solver.solver(grid, model, sfield, verb=1)\n",
    "    else:        # r = 1; n = 1 (takes too long otherwise)\n",
    "        cpu = %timeit -r 1 -n 1 -o -q emg3d.solver.solver(grid, model, sfield, verb=1)\n",
    "\n",
    "    cpu_average[i] = cpu.average\n",
    "    \n",
    "    print(f\"  CPU: {cpu_average[i]:5.1f} s\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.loglog(nsizes**3/1e6, cpu_average, '.-', label='runtime')\n",
    "plt.legend()\n",
    "plt.xlabel('Number of cells (in millions)')\n",
    "plt.ylabel('Runtime (s)')\n",
    "plt.xticks([1e-1, 1e0, 1e1, 1e2], ('0.1', '1', '10', '100'))\n",
    "plt.yticks([1e0, 1e1, 1e2, 1e3, 1e4], ('1', '10', '100', '1000', '10000'))\n",
    "plt.savefig('runtime.svg', facecolor='none', edgecolor='none', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style='border: 3px solid #ddd;'>\n",
       "  <tr>\n",
       "     <td style='text-align: center; font-weight: bold; font-size: 1.2em; border: 2px solid #fff;' colspan='6'>Fri Jul 05 13:28:44 2019 CEST</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Linux</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>OS</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>4</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>CPU(s)</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>x86_64</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Machine</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>64bit</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Architecture</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>15.6 GB</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>RAM</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Jupyter</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Environment</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; border: 2px solid #fff;' colspan='6'>Python 3.7.3 (default, Mar 27 2019, 22:11:17) \n",
       "[GCC 7.3.0]</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.16.4</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>numpy</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.2.1</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>scipy</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.44.1</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>numba</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.7.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>emg3d</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>7.6.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>IPython</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>3.1.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>matplotlib</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; background-color: #ddd;border: 2px solid #fff;' colspan='6'>Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications</td>\n",
       "  </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "\n",
       "--------------------------------------------------------------------------------\n",
       "  Date: Fri Jul 05 13:28:44 2019 CEST\n",
       "\n",
       "             Linux : OS\n",
       "                 4 : CPU(s)\n",
       "            x86_64 : Machine\n",
       "             64bit : Architecture\n",
       "           15.6 GB : RAM\n",
       "           Jupyter : Environment\n",
       "\n",
       "  Python 3.7.3 (default, Mar 27 2019, 22:11:17)  [GCC 7.3.0]\n",
       "\n",
       "            1.16.4 : numpy\n",
       "             1.2.1 : scipy\n",
       "            0.44.1 : numba\n",
       "             0.7.0 : emg3d\n",
       "             7.6.0 : IPython\n",
       "             3.1.0 : matplotlib\n",
       "\n",
       "  Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for\n",
       "  Intel(R) 64 architecture applications\n",
       "--------------------------------------------------------------------------------"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emg3d.Report()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}