FiniteVolumeGPU/OpenCL to CUDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets have matplotlib \"inline\"\n",
    "%matplotlib inline\n",
    "\n",
    "#Import packages we need\n",
    "import numpy as np\n",
    "from matplotlib import animation, rc\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import subprocess\n",
    "import os\n",
    "import gc\n",
    "import datetime\n",
    "import importlib\n",
    "\n",
    "import pycuda.autoinit\n",
    "import pycuda.driver\n",
    "\n",
    "try:\n",
    "    from StringIO import StringIO\n",
    "except ImportError:\n",
    "    from io import StringIO\n",
    "\n",
    "#Set large figure sizes\n",
    "#Note, this prevents nice figures for articles...\n",
    "rc('figure', figsize=(16.0, 12.0))\n",
    "rc('animation', html='html5')\n",
    "\n",
    "#Finally, import our simulator\n",
    "#from SWESimulators import FBL, CTCS, LxF, FORCE, HLL, HLL2, KP07, KP07_dimsplit, WAF, CDKLM16, DataOutput, PlotHelper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_test_data(nx, ny, num_ghost_cells):\n",
    "    width = 100.0\n",
    "    height = width\n",
    "    dx = width / float(nx)\n",
    "    dy = height / float(ny)\n",
    "\n",
    "    h = np.zeros((ny+2*num_ghost_cells, nx+2*num_ghost_cells), dtype=np.float32); \n",
    "    hu = np.zeros((ny+2*num_ghost_cells, nx+2*num_ghost_cells), dtype=np.float32);\n",
    "    hv = np.zeros((ny+2*num_ghost_cells, nx+2*num_ghost_cells), dtype=np.float32);\n",
    "\n",
    "    #Create a gaussian \"dam break\" that will not form shocks\n",
    "    x_center = dx*nx/2.0\n",
    "    y_center = dy*ny/2.0\n",
    "    size = width \n",
    "    for j in range(-num_ghost_cells, ny+num_ghost_cells):\n",
    "        y = dy*(j+0.5) - y_center\n",
    "        for i in range(-num_ghost_cells, nx+num_ghost_cells):\n",
    "            x = dx*(i+0.5) - x_center\n",
    "            \n",
    "            h[j+num_ghost_cells, i+num_ghost_cells] = 0.5 + 0.1*np.exp(-(x**2/size + y**2/size))\n",
    "            #hu[j+num_ghost_cells, i+num_ghost_cells] = 0.01*np.sin(x)*np.exp(-(x**2/size))\n",
    "            hu[j+num_ghost_cells, i+num_ghost_cells] = 0.1*np.exp(-(x**2/size + y**2/size))\n",
    "            hv[j+num_ghost_cells, i+num_ghost_cells] = 0.1*np.exp(-(x**2/size + y**2/size))\n",
    "    \n",
    "    return h, hu, hv, dx, dy, nx, ny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of absolute difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from SWESimulators import Common\n",
    "importlib.reload(Common)\n",
    "\n",
    "nx = 10\n",
    "ny = 15\n",
    "nx_halo = 2\n",
    "ny_halo = 3\n",
    "a = np.random.rand(ny+2*ny_halo, nx+2*nx_halo).astype(np.float32)\n",
    "\n",
    "a_gpu = Common.CUDAArray2D(nx, ny, nx_halo, ny_halo, a)\n",
    "b = a_gpu.download(async=True)\n",
    "pycuda.driver.Context.synchronize()\n",
    "print(\"Sum of absolute difference: \", np.sum(np.abs(a-b)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0xef53611a20>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from SWESimulators import LxF\n",
    "importlib.reload(LxF)\n",
    "\n",
    "nx = 10\n",
    "ny = 15\n",
    "num_ghost_cells = 1\n",
    "dt = 0.01\n",
    "g = 9.81\n",
    "\n",
    "h0, hu0, hv0, dx, dy, nx, ny = gen_test_data(nx, ny, num_ghost_cells)\n",
    "plt.figure()\n",
    "plt.subplot(121)\n",
    "plt.imshow(h0)\n",
    "plt.colorbar()\n",
    "\n",
    "sim = LxF.LxF(h0, hu0, hv0, \\\n",
    "                nx, ny, \\\n",
    "                dx, dy, dt, \\\n",
    "                g)\n",
    "\n",
    "t = sim.step(0.02)\n",
    "h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.imshow(h1)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0xef53931160>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from SWESimulators import FORCE\n",
    "importlib.reload(FORCE)\n",
    "\n",
    "nx = 10\n",
    "ny = 15\n",
    "num_ghost_cells = 1\n",
    "dt = 0.01\n",
    "g = 9.81\n",
    "\n",
    "h0, hu0, hv0, dx, dy, nx, ny = gen_test_data(nx, ny, num_ghost_cells)\n",
    "plt.figure()\n",
    "plt.subplot(121)\n",
    "plt.imshow(h0)\n",
    "plt.colorbar()\n",
    "\n",
    "sim = FORCE.FORCE(h0, hu0, hv0, \\\n",
    "                nx, ny, \\\n",
    "                dx, dy, dt, \\\n",
    "                g)\n",
    "\n",
    "t = sim.step(0.02)\n",
    "h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.imshow(h1)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}