Performance modeling.ipynb

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   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from time import time"
   ]
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   "source": [
    "## inner product"
   ]
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   "source": [
    "m = 1000000\n",
    "x = np.random.rand(m)\n",
    "y = np.random.rand(m)"
   ]
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   "source": [
    "r_peak = 2.9e9 * 32  # 32 flops/cycle\n",
    "B_peak = 2.133e9 * 2 # 2 words/cycle"
   ]
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   "source": [
    "F = 2*m\n",
    "M = 2*m\n",
    "print(F/r_peak)\n",
    "print(M/B_peak)"
   ]
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   "source": [
    "%%timeit\n",
    "np.dot(x,y)"
   ]
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    "x.dtype"
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   "source": [
    "## GEMV"
   ]
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    "m = 10000\n",
    "A = np.random.rand(m,m)\n",
    "x = np.random.rand(m)"
   ]
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    "F = 2*m**2\n",
    "M = m**2\n",
    "print(F/r_peak)\n",
    "print(M/B_peak)"
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   "cell_type": "code",
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   "source": [
    "%%timeit\n",
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   "metadata": {},
   "source": [
    "## GEMM"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "m = 1000\n",
    "A = np.random.rand(m,m)\n",
    "B = np.random.rand(m,m)"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "F = 2*m**3\n",
    "M = 3*m**2\n",
    "print(F/r_peak)\n",
    "print(M/B_peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%timeit\n",
    "C = np.dot(A,B)"
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