09ex_Numba.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1\n",
    "\n",
    "Create a vectorized version of log and exp math function for 1D array A = [2, 5, 10, 3, 8]\n",
    "\n",
    "Results should be: \n",
    "+ [0.6931472 1.609438  2.3025851 1.0986123 2.0794415]\n",
    "+ [7.3890562e+00 1.4841316e+02 2.2026465e+04 2.0085537e+01 2.9809580e+03]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6931472 1.609438  2.3025851 1.0986123 2.0794415]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from numba import vectorize, float32, cuda\n",
    "\n",
    "@vectorize([float32(float32)], target='parallel', fastmath=True) \n",
    "#to use cuda target must become cuda. Since emulator has a bug we use parallel\n",
    "def log(x):\n",
    "    c =  np.log(x)\n",
    "    return c\n",
    "\n",
    "A = np.float32([2,5,10,3,8])\n",
    "l = log(A)\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7.3890562e+00 1.4841316e+02 2.2026465e+04 2.0085537e+01 2.9809580e+03]\n"
     ]
    }
   ],
   "source": [
    "@vectorize([float32(float32)], target='parallel', fastmath=True) \n",
    "#to use cuda target must become cuda. Since emulator has a bug we use parallel\n",
    "def exp(x):\n",
    "    c =  np.exp(x)\n",
    "    return c\n",
    "\n",
    "e = exp(A)\n",
    "\n",
    "print(e)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exerice 2\n",
    "Compute the value of a Gaussian probability density function at $x$ with $mean = 1$, $\\sigma = 1$, lower and upper bound in $(-3, 3)$ and $size = 100000$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00013383 0.00013386 0.00013389 ... 0.05400392 0.05399745 0.05399097]\n"
     ]
    }
   ],
   "source": [
    "@vectorize([float32(float32)], target='parallel', fastmath=True)\n",
    "def gauss(x):\n",
    "    return 1/np.sqrt(2*np.pi)*np.exp(-(x - 1)**2/2)\n",
    "\n",
    "x = np.linspace(-3, 3, 100000, dtype = 'float32')\n",
    "\n",
    "g = gauss(x)\n",
    "\n",
    "print(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3\n",
    "\n",
    "Create a \"zero suppression\" function. A common operation when working with waveforms is to force all samples values below a certain absolute magnitude to be zero, as a way to eliminate low amplitude noise. \n",
    "Plot the data before and after the application of the zero_suppress function.\n",
    "\n",
    "$thresold = 15$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Data without zero suppression')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from numba import int16\n",
    "\n",
    "n = 100000\n",
    "noise = np.random.normal(size=n) * 3\n",
    "pulses = np.maximum(np.sin(np.arange(n) / (n / 23)) - 0.3, 0.0)\n",
    "data = ((pulses * 300) + noise).astype(np.int16)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(np.arange(0,n), data)\n",
    "plt.title('Data without zero suppression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Data with zero suppression')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "@vectorize([int16(int16)], target='parallel', fastmath=True)\n",
    "def zero_suppression(x):\n",
    "    if x <= 15:\n",
    "        return 0\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "data_filtered = zero_suppression(data)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(np.arange(0,n), data_filtered)\n",
    "plt.title('Data with zero suppression')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4\n",
    "\n",
    "Calculate the Sigmoid kernel between the matrix X and Y defined as below. The Sigmoid kernel is defined as:\n",
    "\n",
    "$k(x,y) = \\tanh(\\alpha x^T y + c) $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "CudaSupportError",
     "evalue": "Error at driver init: \n\nCUDA driver library cannot be found.\nIf you are sure that a CUDA driver is installed,\ntry setting environment variable NUMBA_CUDA_DRIVER\nwith the file path of the CUDA driver shared library.\n:",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mCudaSupportError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-58-99b08ea3e287>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[0mio_array\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtanh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mY\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m# do the computation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0msigmoid_kernel\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mio_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numba\\cuda\\compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    767\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    768\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 769\u001b[1;33m         return self.dispatcher.call(args, self.griddim, self.blockdim,\n\u001b[0m\u001b[0;32m    770\u001b[0m                                     self.stream, self.sharedmem)\n\u001b[0;32m    771\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numba\\cuda\\compiler.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, args, griddim, blockdim, stream, sharedmem)\u001b[0m\n\u001b[0;32m    859\u001b[0m         argtypes = tuple(\n\u001b[0;32m    860\u001b[0m             [self.typingctx.resolve_argument_type(a) for a in args])\n\u001b[1;32m--> 861\u001b[1;33m         \u001b[0mkernel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margtypes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    862\u001b[0m         \u001b[0mkernel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgriddim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblockdim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msharedmem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    863\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numba\\cuda\\compiler.py\u001b[0m in \u001b[0;36mcompile\u001b[1;34m(self, sig)\u001b[0m\n\u001b[0;32m    920\u001b[0m         \u001b[0margtypes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msigutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_signature\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    921\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mreturn_type\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mreturn_type\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mtypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 922\u001b[1;33m         \u001b[0mcc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_current_device\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_capability\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    923\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mspecialized\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    924\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdefinition\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numba\\cuda\\api.py\u001b[0m in \u001b[0;36mget_current_device\u001b[1;34m()\u001b[0m\n\u001b[0;32m    373\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_current_device\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    374\u001b[0m     \u001b[1;34m\"Get current device associated with the current thread\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 375\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mcurrent_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    376\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numba\\cuda\\cudadrv\\devices.py\u001b[0m in \u001b[0;36mget_context\u001b[1;34m(devnum)\u001b[0m\n\u001b[0;32m    210\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mCUDA\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m     \"\"\"\n\u001b[1;32m--> 212\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_runtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_or_create_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevnum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numba\\cuda\\cudadrv\\devices.py\u001b[0m in \u001b[0;36mget_or_create_context\u001b[1;34m(self, devnum)\u001b[0m\n\u001b[0;32m    136\u001b[0m             \u001b[0mattached_ctx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_attached_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mattached_ctx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_or_create_context_uncached\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevnum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mattached_ctx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numba\\cuda\\cudadrv\\devices.py\u001b[0m in \u001b[0;36m_get_or_create_context_uncached\u001b[1;34m(self, devnum)\u001b[0m\n\u001b[0;32m    149\u001b[0m             \u001b[1;31m# Try to get the active context in the CUDA stack or\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m             \u001b[1;31m# activate GPU-0 with the primary context\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m             \u001b[1;32mwith\u001b[0m \u001b[0mdriver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_active_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mac\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    152\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mac\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    153\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_activate_context_for\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numba\\cuda\\cudadrv\\driver.py\u001b[0m in \u001b[0;36m__enter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    393\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m             \u001b[0mhctx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdrvapi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcu_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 395\u001b[1;33m             \u001b[0mdriver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcuCtxGetCurrent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbyref\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhctx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    396\u001b[0m             \u001b[0mhctx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhctx\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    397\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numba\\cuda\\cudadrv\\driver.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, fname)\u001b[0m\n\u001b[0;32m    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitialization_error\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 285\u001b[1;33m             raise CudaSupportError(\"Error at driver init: \\n%s:\" %\n\u001b[0m\u001b[0;32m    286\u001b[0m                                    self.initialization_error)\n\u001b[0;32m    287\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mCudaSupportError\u001b[0m: Error at driver init: \n\nCUDA driver library cannot be found.\nIf you are sure that a CUDA driver is installed,\ntry setting environment variable NUMBA_CUDA_DRIVER\nwith the file path of the CUDA driver shared library.\n:"
     ]
    }
   ],
   "source": [
    "X = np.random.rand(3,3).astype('float32')\n",
    "Y = np.random.rand(3,3).astype('float32')\n",
    "io_array = np.zeros((3,3), dtype='float32')\n",
    "\n",
    "@cuda.jit\n",
    "def sigmoid_kernel(X, Y, alpha, c, io_array):\n",
    "    x, y = cuda.grid(2) # For 2D array\n",
    "    \n",
    "    if (x, y) < np.shape(io_array):\n",
    "        io_array[x,y] =  np.tanh(alpha*X.T*Y + c)# do the computation\n",
    "    \n",
    "sigmoid_kernel[(2,2), (3,3)](X, Y, 1, 1, io_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 5\n",
    "\n",
    "Create a kernel function similar to the ```double_kernel``` see during the lecture and create a new function that takes a 3 dimensional matrix as input calculating the $cos$ for each element and then returns the result. The shape of the matrix must be $256X256X256$. The matrix can be randomly generated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 6\n",
    "\n",
    "Create a matrix multiplication kernel function, called ```matmul``` that takes as input two 2D matrices:\n",
    "+ A of shape $24x12$\n",
    "+ B of shape $12x22$\n",
    "and that computes the multiplication and put the results into a third matrix C of shape $24x12$\n",
    "\n",
    "A and B must be randomly generated and only int values are allowed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "from __future__ import division\n",
    "from numba import cuda\n",
    "import numpy\n",
    "import math\n",
    "\n",
    "# complete the code\n",
    "@cuda.jit\n",
    "def matmul(A, B, C):\n",
    "    \"\"\"Perform matrix multiplication of C = A * B\n",
    "    \"\"\"\n",
    "    row, col = cuda.grid(2)\n",
    "    if row < C.shape[0] and col < C.shape[1]:\n",
    "        tmp = 0.\n",
    "        for k in range(A.shape[1]):\n",
    "            tmp += A[row, k] * B[k, col]\n",
    "        C[row, col] = tmp\n",
    "\n",
    "# Initialize the data arrays\n",
    "A = \n",
    "B = \n",
    "\n",
    "# Configure the blocks\n",
    "threadsperblock = \n",
    "blockspergrid_x = \n",
    "blockspergrid_y = \n",
    "blockspergrid = \n",
    "\n"
   ]
  }
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