Timing_Results/plot_timing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_omp</th>\n",
       "      <th>Total</th>\n",
       "      <th>E-step</th>\n",
       "      <th>M-step</th>\n",
       "      <th>IO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2539.128065</td>\n",
       "      <td>2293.212175</td>\n",
       "      <td>245.102882</td>\n",
       "      <td>2.501011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1731.789112</td>\n",
       "      <td>1426.280260</td>\n",
       "      <td>304.410219</td>\n",
       "      <td>1.926899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1336.784840</td>\n",
       "      <td>934.716463</td>\n",
       "      <td>399.653912</td>\n",
       "      <td>2.602816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>900.095940</td>\n",
       "      <td>490.777731</td>\n",
       "      <td>407.839060</td>\n",
       "      <td>2.654076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>741.780996</td>\n",
       "      <td>309.553146</td>\n",
       "      <td>430.645466</td>\n",
       "      <td>2.640963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32</td>\n",
       "      <td>782.614946</td>\n",
       "      <td>210.109472</td>\n",
       "      <td>570.574045</td>\n",
       "      <td>2.243996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64</td>\n",
       "      <td>1440.789938</td>\n",
       "      <td>1023.807049</td>\n",
       "      <td>415.407658</td>\n",
       "      <td>2.650023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_omp        Total       E-step      M-step        IO\n",
       "0      1  2539.128065  2293.212175  245.102882  2.501011\n",
       "1      2  1731.789112  1426.280260  304.410219  1.926899\n",
       "2      4  1336.784840   934.716463  399.653912  2.602816\n",
       "3      8   900.095940   490.777731  407.839060  2.654076\n",
       "4     16   741.780996   309.553146  430.645466  2.640963\n",
       "5     32   782.614946   210.109472  570.574045  2.243996\n",
       "6     64  1440.789938  1023.807049  415.407658  2.650023"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = \"Blobs_smp20000_fea30_cls8.xlsx\"\n",
    "df_omp = pd.read_excel(file,sheetname=\"OpenMP\")\n",
    "df_omp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ovNB5QmNqagpkVeScMmUK06ZNY9GiRUj+n7Kampry9OlTUlJSsLCwyHHc06dP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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10744cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "\n",
    "fontsize=10 # fontsize for all text. 10 for \n",
    "\n",
    "mpl.rcParams['xtick.labelsize'] = fontsize\n",
    "mpl.rcParams['ytick.labelsize'] = fontsize\n",
    "\n",
    "data_omp = [df_omp[\"Total\"].values,\n",
    "             df_omp[\"E-step\"].values,\n",
    "             df_omp[\"M-step\"].values,\n",
    "             df_omp[\"Total\"].values-df_omp[\"E-step\"].values-df_omp[\"M-step\"].values]\n",
    "\n",
    "label_list = [\"Total\",\"E-step\",\"M-step\",\"others\"]\n",
    "color_list = [\"Black\",\"blue\",\"red\",\"grey\"]\n",
    "\n",
    "fig, axarr = plt.subplots(1,2,figsize=[8,3])\n",
    "#fig.subplots_adjust(hspace=0.05)\n",
    "\n",
    "def stackedbar(ax,n_list,data_list,xlabel):\n",
    "    ind = np.arange(1,len(n_list)+1) \n",
    "    bottom = np.zeros_like(data_list[0])\n",
    "    for data,label,color in zip(data_list,label_list[1:],color_list[1:]):\n",
    "        # assume the first data is total and skip\n",
    "        ax.bar(ind,data,bottom=bottom,color=color,label=label)\n",
    "        bottom = bottom + data\n",
    "    #ax.set_ylim([0,50000])\n",
    "    #ax.set_xlim([0,7])\n",
    "    ax.set_xticks(ind+0.4)\n",
    "    ax.set_xticklabels(n_list)\n",
    "    ax.xaxis.grid(False) \n",
    "    ax.set_xlabel(xlabel,fontsize=fontsize)\n",
    "    ax.set_ylabel(\"time use (ms)\",fontsize=fontsize)\n",
    "        \n",
    "stackedbar(axarr[0],df_omp[\"n_omp\"],data_omp[1:],\"OpenMP threads\")\n",
    "\n",
    "axarr[0].legend(loc=1,fontsize=fontsize)\n",
    "axarr[0].set_title(\"Kmean OpenMP timing\",fontsize=fontsize,fontweight=\"bold\")\n",
    "\n",
    "def scaling_curve(ax,n,data_list,xlabel):\n",
    "    for data,label,color in zip(data_list,label_list,color_list):   \n",
    "        ax.plot(n,data[0]/data,\n",
    "                marker='D',label=label,color=color,linewidth=1)\n",
    "        \n",
    "    ax.set_xscale(\"log\")\n",
    "\n",
    "    ax.set_xticks(n)\n",
    "    ax.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "    ax.set_xlim([0.8,80])\n",
    "    \n",
    "    ax.set_yscale(\"log\")\n",
    "    #ax.set_ylim([-0.5,32])\n",
    "    ax.set_yticks(np.exp2(np.arange(-1,5)))\n",
    "    ax.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "    \n",
    "    ax.set_xlabel(xlabel,fontsize=fontsize)\n",
    "    ax.set_ylabel(\"speed-up\",fontsize=fontsize)\n",
    "    ax.xaxis.label.set_size(fontsize)\n",
    "        \n",
    "scaling_curve(axarr[1],df_omp[\"n_omp\"],data_omp[:-1],\"OpenMP threads\")\n",
    "axarr[1].legend(loc=2,fontsize=fontsize)\n",
    "axarr[1].set_title(\"scaling curve\",fontsize=fontsize,fontweight=\"bold\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"plots/OpenMP_scaling.jpg\",dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N_MPI</th>\n",
       "      <th>Total</th>\n",
       "      <th>E-step</th>\n",
       "      <th>M-step</th>\n",
       "      <th>IO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2327.527416</td>\n",
       "      <td>2080.869320</td>\n",
       "      <td>245.803846</td>\n",
       "      <td>3.746011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1370.546596</td>\n",
       "      <td>1208.027011</td>\n",
       "      <td>161.206449</td>\n",
       "      <td>3.857213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>889.274717</td>\n",
       "      <td>783.437196</td>\n",
       "      <td>103.108949</td>\n",
       "      <td>4.113028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>490.620752</td>\n",
       "      <td>421.552152</td>\n",
       "      <td>63.779867</td>\n",
       "      <td>4.099924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>298.665461</td>\n",
       "      <td>221.786738</td>\n",
       "      <td>67.226194</td>\n",
       "      <td>4.935520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32</td>\n",
       "      <td>280.026070</td>\n",
       "      <td>183.410053</td>\n",
       "      <td>76.902479</td>\n",
       "      <td>9.008225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64</td>\n",
       "      <td>985.778767</td>\n",
       "      <td>106.266378</td>\n",
       "      <td>605.020996</td>\n",
       "      <td>19.194771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   N_MPI        Total       E-step      M-step         IO\n",
       "0      1  2327.527416  2080.869320  245.803846   3.746011\n",
       "1      2  1370.546596  1208.027011  161.206449   3.857213\n",
       "2      4   889.274717   783.437196  103.108949   4.113028\n",
       "3      8   490.620752   421.552152   63.779867   4.099924\n",
       "4     16   298.665461   221.786738   67.226194   4.935520\n",
       "5     32   280.026070   183.410053   76.902479   9.008225\n",
       "6     64   985.778767   106.266378  605.020996  19.194771"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mpi = pd.read_excel(file,sheetname=\"MPI\")\n",
    "df_mpi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1Y9y4cRgaGmrbHioUCjQaDQqFgmnTptGnTx8gdVQCLy8v7t69i7OzM7NmzaJ8+fLafa9b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\nESEiIiIiIl5RESEiIiIiIl75H35ica/AuGE1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f9760b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_mpi = [df_mpi[\"Total\"].values,\n",
    "             df_mpi[\"E-step\"].values,\n",
    "             df_mpi[\"M-step\"].values,\n",
    "             df_mpi[\"Total\"].values-df_mpi[\"E-step\"].values-df_mpi[\"M-step\"].values]\n",
    "\n",
    "fig, axarr = plt.subplots(1,2,figsize=[8,3])\n",
    "\n",
    "stackedbar(axarr[0],df_mpi[\"N_MPI\"],data_mpi[1:],\"MPI processes\")\n",
    "\n",
    "axarr[0].legend(loc=1,fontsize=fontsize)\n",
    "axarr[0].set_title(\"Kmean MPI timing\",fontsize=fontsize,fontweight=\"bold\")\n",
    "\n",
    "scaling_curve(axarr[1],df_mpi[\"N_MPI\"],data_mpi[:-1],\"MPI processes\")\n",
    "axarr[1].legend(loc=2,fontsize=fontsize)\n",
    "axarr[1].set_title(\"scaling curve\",fontsize=fontsize,fontweight=\"bold\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"plots/MPI_scaling.jpg\",dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t_cuda</th>\n",
       "      <th>Total</th>\n",
       "      <th>E-step</th>\n",
       "      <th>M-step</th>\n",
       "      <th>Cuda IO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>783.192</td>\n",
       "      <td>356.6850</td>\n",
       "      <td>119.579</td>\n",
       "      <td>392.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>636.066</td>\n",
       "      <td>202.4170</td>\n",
       "      <td>132.818</td>\n",
       "      <td>294.903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>547.731</td>\n",
       "      <td>108.1010</td>\n",
       "      <td>138.312</td>\n",
       "      <td>295.276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>541.216</td>\n",
       "      <td>69.1309</td>\n",
       "      <td>169.700</td>\n",
       "      <td>296.227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>511.788</td>\n",
       "      <td>38.1258</td>\n",
       "      <td>171.490</td>\n",
       "      <td>295.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32</td>\n",
       "      <td>2496.990</td>\n",
       "      <td>245.1650</td>\n",
       "      <td>1894.401</td>\n",
       "      <td>322.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64</td>\n",
       "      <td>2437.490</td>\n",
       "      <td>162.1510</td>\n",
       "      <td>1919.949</td>\n",
       "      <td>321.023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   t_cuda     Total    E-step    M-step  Cuda IO\n",
       "0       1   783.192  356.6850   119.579  392.046\n",
       "1       2   636.066  202.4170   132.818  294.903\n",
       "2       4   547.731  108.1010   138.312  295.276\n",
       "3       8   541.216   69.1309   169.700  296.227\n",
       "4      16   511.788   38.1258   171.490  295.944\n",
       "5      32  2496.990  245.1650  1894.401  322.414\n",
       "6      64  2437.490  162.1510  1919.949  321.023"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cuda = pd.read_excel(file,sheetname=\"Cuda\")\n",
    "df_cuda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZfHwyKOMVFcrcxIlKLC01hIaWwz4MlZTe8zy5XA5AcnIyU6dOZfr06SxduhTJ\n8zWk5XI5z549Izk5GauXVjLKOq7RaHKdW5jq1S2QyaSFnicIlZ0hB9xWlsG+e/fuxdramu7du7N+\n/fpsjyUnJ1OlyovXIZfLSU5OLrTM4l5j8n2vsj5Vy+nT1dC/sxcvt/A4tEkM7NkD+/eDvT0MHgxL\nl0KDBlKg9Nd4Q78fLytJLDt2wJtvyhg40JTu3Q0XR1nQRxxl0nCVkJDA5MmTGTp0KP3792f58uW6\nxxQKBVWrVsXS0hKFQpHteJUqVbKNl8k6tzCPHxe8mqYglJdaZVx+fjMByrregurOydAXyKioKCQS\nCb/88gt//PEHc+bMYe3atdSqVSvf605hinONyW/GhsWyxchDljyvN50UH78il1kShp7Bkn3LgbzH\nrmRkZI2J0XYnNWmiHRMTHa2iXr0XLTAPHpQ+HkO/Hy8raSxSKYSGShk61IxjxxSlXt24orwnFXaW\n08OHDxkzZgz+/v506dIFgFatWnHmzBmcnJyIiYmhc+fO2Nrasnz5cry8vLh37x5qtRpra+s8z9WH\n8PAVeikny6QSrtgaFxfLs2dPad/eUa9TIwVB0Nq2bZvu9vDhwwkMDKRWLW3K16RJE+Lj40lKSsLC\nwoJz587h5eVV5jG9nMwAutulSWouXDjH1KkTCAxcpNv8FmDkSA+aN2/BypWhBT7/6dMnnD79S5ls\nflvQ7uJZScz+/cYcOaJNYgYOVOLjk54tiRHy5uKSyfHjKmbNMuPLL9PKdQPNik7vCU1ERARPnz4l\nPDyc8PBwAD799FMWLlzIihUraNy4MX369EEqldKxY0fc3d1Rq9X4+/sDMGfOHObPn5/t3FfJ8ePR\n1KhRQ7esuCAIZe/gwYOkpKTg7u6Or68vXl5eaDQa3NzcqFOnTpnWnTOZyaKPpKZhQzuio7/XJTRx\ncbGkpqYW6bmxsdc5deqE3hOagnYXP3xYxt27RrokZs4ckcSUxPz56fTta8G2bcYMGya2Rsii94Rm\n3rx5zJs3L9fxb775Jtcxb29vvL29sx1r1KhRnudWRiqVisWLg7h79w6ZmZl8+OFgDh/+DzKZMc2b\na/dbCg1dols5ePHiECwsLPLc/Xb48CE0aNAQY2MZbm7uhIWtRCaTYWZmxsKFS7GwkBvypQpChbR1\n61aAbNO2nZ2dcXZ2Lpf680tmspQ2qWnatBm3bsWTnJyMpaUlR44c4r33+uXa/PbEiWN8881mZDIZ\nNWvWIij9+XCsAAAgAElEQVRoMVu2fEVs7HX2799L585dWbZsMenpaZiamuHj44darWb+fF9q1KjB\ngwf3cXLqmmsvqJwK23Lg6lUp48dnsGBBeoler6BlZgbr1qUxaJA5Tk6ZNGsmtkYAsbBemdq/Pwor\nKyv8/ReQkqJgzJhhdO3ajcaNm9CqVRsA/u//BtKuXXsWLQrk7NkzPH36JM/db1NTUxk1yovmzVuw\nZs0qnJ17M2TIUE6ejOHp02cioREEA6vewwnZn38AxRvTJA9ZkmfSo2rRkscxZwp9/jvvOHPixDFc\nXfvzxx9X+de/RuZKaI4ePcLQocPp2bM3hw//B4VCwYgRY9i/P4qBAz/E338uH33kTpcub3Pu3K9E\nRIQxbtwk7t27y4oVq5HLLZk0aSx//fVnqTe/rVJFtMjog729Gl/fDMaPN+Pw4RTEyAWR0JSpv//+\nm44dtVPQLSzk2Nk14s6dOzRu/OLbYosW2ouDtXUN0tPT8t39FrQbVAIMHz6aLVu+Ytq0idSqVVuX\nHAmCYDhZyUfOAY4FtdIoZvuWenCwi0tfQkOXYGNTj3bt3gTg/v37DB8+nIwMFUOHjsDbewZbt24i\nKmo3DRva0aPHu9nKuHEjlq1bv2bbts0ASKXaj4YmTZpTtWo1AFq1asOtW3/nm9AoldCokZqaNdU8\nfJj3iiCVcWG7imzECCU//ihl4UJT0epFGaxDI7xgZ2fHpUu/AZCSoiAuLo42bdrqdtDWyj6iq2FD\nO3r37kNY2HpCQ7+gZ8/eupleWdPZv//+EK6u77N69ToaNWrMgQN7y+X1CIJQfCk+fihm++Y6ro9k\nBqBevfqkpqYSGbmT997rB0Dt2rXZunUrYWHr6dq1GwcO7MPLaxxhYevRaDTExBzHyMhIdy2ytbVj\n4kRvwsLW88knfvTs2QuA+PibpKWlkZmZybVrV7Cza5z79aXAl18a07mznO3bjQkLS2P27NwfriKZ\n0T+JBFasSOM//5Fx7JhYukS00JShAQM+ZOnShUyc6EV6ejpjxnxMtWpWhIevws6uUZ7PGTjww3x3\nv83SsmUblixZiLm5ORKJBB+fT8vj5QiCUEJZiYtu2raekpksvXq5cOTIIWxtG+rG5L2sZcvW+PhM\nx8JCjrm5OV27diMjI4MbN2LZvXs7kydPIzR0CRkZGaSnpzFt2mwAjI2NmT9/DomJibz7bi+aNWuu\nK/PxY/jqKxM2bjSmU6dM1q9PpUMH7VgOZ+dMoHJvOVBZWFtDWFgaEyaYER2dQu3ar2+XnthtWxD0\nSOy2bfh1aMqCvnbbtli2GCjdzCZ9xFEUCQl3CQjwy7Vf3J07EiIiTNi1y5h+/VRMnpxB8+Z5D0pd\ntszk+ZYDhr9GV5Q1V6BsYgkONuHiRSnbt6dS1O0PK8p7Ui7r0Gg0Go4fP86vv/5KUlIS1tbWdOnS\nhbffflvX/SEIgiAUTXkkMmXlv/81IizMhO++k+HhoeT4cQU2NgV/H/bxyaBWLVO9LIwnFGz27AwG\nDLBg/XpjJkyoPFO5tUkvTC54Al2R5JvQ/PLLL0RERNCqVSvs7e2pVasWT5484eTJk2zYsIHx48fT\ntWvX0kcgCIIgVDh169qwfv0mzp0z4osvTDh3TsrYsUrOnEmmenVDRyfkZGwMa9em0q+fBW+/nUnb\nthV/Knf21aRNSt0tmW9CEx8fz1dffYVUmn2gkaurK5mZmezatUskNIIgCK8gjQaOHZPyxRcm3Llj\nxMSJGUREpGFhYejIhILY2WlYuDCd8ePNOHo0BXkFXs2joNWkSyrfnjYPD49cyUxCQgIAUqmUoUOH\nlrhSQRAEoeJRqSAqSkbPnhZ89pkpI0YoOX1agZeXUiQzlYSbm4o331Qzf37FXZimoNWkly0r+W7i\nhc5y+vLLL6latSpPnz5l7969dO/enblz55a4QkEQBKFiSUmBHTuMWbvWhHr11Mybl06vXplin6BK\naunSNJyd5Rw8mEn//ipDh5NNYatJl6alptCx0N9//z2DBg0iJiaGQ4cO8ccffxS7EkEQhPKiVCr5\n5JNPGDp0KB999BHR0dHZHj927Bhubm64u7uze/duA0VZMSQlwYoVJrz1lpwTJ6SsXZvK/v2p9O4t\nkpnKzNISIiJSmTPHlNu3X59fZKEtNEZGRjx8+JCaNWsCkJaWVuZBCYIglNSBAwewsrJi+fLlJCUl\nMWjQIHr10i4Up1QqCQ4OJjIyEnNzczw9PXF2dtZd314Vhc0cSUjQTr3escOYvn1V7N2bir19xR9E\nKhSdo6OaCROUTJpkxr59qUgNvO7eo0cSjh6Vcu2aESYmGjIy8k60SrNmUaEtNE5OTgwfPpxhw4ax\nePFi3nnnnRJVJAiCUB769u3LtGnTAO3SEy+PBYyLi8PW1pZq1aphYmJChw4dOHv2rKFCLRNZTfpB\nQeQaj3D9uhHTp5vyzjty1Gr48UcFX3yRJpKZV9SUKRkYG8PKlSUfl1IaN25IWLvWmIEDzenUSc6R\nIzL69lVx8aKiTFaTLrSFZsaMGcyYMQOAtm3bYmxsXOLKBEEQypr8+dSO5ORkpk6dyvTp03WPJScn\nU6VKlWznJicnF1pm9eoWyGRF/4prqMUFAwMhJOTF/ZAQU+RyU1xdYckSOHkSpkyBL76AGjVMgPL5\noKsoiy1WlDig/GLZuRMcHWUMGGBKXhOT9RmHWg1nz8L+/dqfxETo3x8+/RR69QIzM2NAm0MsXw5y\nOQQFaZ8bEACBgaZAyQczF5rQ7Ny5k127dpGe/iKbOnToUIkrFARBKGsJCQlMnjyZoUOH0r9/f91x\nS0tLFAqF7r5CociW4OTn8eOUItdtqNVX8xtsGRQEK1ZomDs3nRUrlMjl2g+e8lrsrjKuRlvWyjMW\nY2NYvlyKp6cZ0dEKqlXTbxxpafDTT1K++07G99/LsLLS0KePipAQFY6Oat2qxc+eaX9eNnmydv2Z\nrNWki/J/ssQrBQNs2bKF9evXU+3ld0EQBKEM/PHHH9y8eZMmTZpgb29fojIePnzImDFj8Pf3p0uX\nLtkea9KkCfHx8SQlJWFhYcG5c+fw8vLSR+gGVdjMkWfPJCQmSir0uiSvi2XLFj//AJ9VbnX27ZvJ\njz+q+OQTM9atSyv1gO/ERDh6VMZ338mIiZHRunUmffuq2L8/hcaNi7ebkj5Xky40obG3t6du3bq5\n1qQRBEHQp88//5wzZ87g4ODAli1b6N27N2PHji12ORERETx9+pTw8HDCw8MBGDx4MKmpqbi7u+Pr\n64uXlxcajQY3Nzfq1Kmj75ciPGeID++KHkdI1galinR8ynErjMDAdPr2tWDXLhkeHqpibzlw86aE\n777TJjFXrkjp3l1F374qQkLSqVGjYmwJWWhC07lzZ3r37k2DBg3QaDRIJBK2bNlSHrEJgvAa+emn\nn4iMjMTIyIjMzEzc3d1LlNDMmzePefPm5fu4s7Mzzs7OpQm1wtBo4MwZKdevG2FqqiE9Xf8zR0rK\nkB/eFT0OQHe7vOIxN4eIiDQ+/NCcixelbNyoHT+V35YDajX8/ruRLol59EhCnz7azUi7d8/E3Lxc\nwi6WQhOaXbt2sXLlyiL1MwuCIJTUG2+8oRvTolKpXrmp1PqkUMDevcZs3GhMeroEL68MQkPTiIjI\n3fVk6GQGyv/Du6LHYah4WrZU0769WpfMaGN4sZBdWhqcPCnl8GHteJhq1TT07asiNDSNDh3URd7F\n21AKTWjq1KlD27ZtMaror0QQhErt/v379OnThxYtWhAbG4uxsTEeHh6AdnKCoG32//prE3bvltGp\nUyaBgen06JGp+6DJSlyyPqQqQjKTpbw/vPUVR2ZmJikpClJSUp//m0JqagopKSk5bitITdWeo1Ck\n6G5fvPgb16//N9/yy/N9WbbMhOjo3B/7ISGm7N8v4949I1q1Kvl4GEMrNKHJyMhg4MCBNGvWDMnz\nkUShoaGFFnzx4kVCQkLYunUr165dY/z48djZ2QHg6emJq6srYWFhHD9+HJlMhp+fHw4ODsTHx+Pr\n64tEIqFZs2YEBASIZEoQXgOrVq0ydAgVkna9GG0XwW+/GeHpqeTIkRQaNsz7w8bHJ4NTp37CxESG\nj0+Hco01vyQiS0jIEhITHzF69Meo1WoyMzPRaNS622q1GrVa8/zfrPs5z1Prjr98zstlHDp0kO++\ny382bkjIEr7//juaN7fPkYzkTlCUSiXm5hZYWFhgbm6BXP7itoVFzttyzM3NqVvX5vlxczIyMgpM\naMpLYQPHr1+XMmlSBoGBudeHqSwKTWjGjx9f7EI3bNjAgQMHMH/eyXb16lVGjx7NmDFjdOdcvXqV\nX3/9lT179pCQkIC3tzdRUVEEBwczffp0nJyc8Pf3Jzo6GhcXl2LHIAhC5bJv375cx6ZMmWKASCqG\nJ0+0+yt9/bUJlpYaxo7NYONGVaFjF5YtW8wvvyx5ftu3zL75Jyc/4+7du9y5c5uEBO2/R44cLvR5\n+/ZFcvJkDEZGRkgkRkilUoyMjDAykiCVSpFIjJ7ff/GY9rzsx7OfZ5StvP/973+FxlG7dh3eeacn\nFhZy5PLcSUnWbVNTU92X+ZJwdx/KsmX2+SZ6s2eX3e+ouCwsKleLTE75JjRbt27F09OTTp065XpM\npVKxfft2RowYkedzbW1tWb16NT4+PgBcuXKFmzdvEh0dTcOGDfHz8+P8+fN069YNiUSCjY0NmZmZ\nJCYmcvXqVV2dPXr04NSpUyKhEYTXQNaYGY1Gw7Vr11CrX8/Va69eNeKrr4w5cMCYXr1UhIWl0rGj\nukhTbfU1ZiQ5OZm7d+8U8HMXpTIDG5t6L/3YMGLEaH755SR790bmWW55fngX1FpU3klEVl0547G1\nbciMGZ+UUwzZuyNzMkT3pL7lm9C0bNmSsWPH0rRpU+zt7alZsyZPnz7l4sWLxMbGFvjNqU+fPty+\nfVt338HBgcGDB9OmTRvWrl3LmjVrqFKlClZWVrpz5HI5z549082kevlYYYq7iqcgVFaGXOm0rOvO\nGi+TpSQznCorpRIOH5axcaMxN28aMXKkkpMnFdSpU/RvzEUdM6JQKLIlJy+3sGj/vYNSmUHdujbU\nq1f/+b/1cHBoT79+/0fduvWoV68eVlbV82y5GDlyDI0bN80VS0VJIgzVIpIznlmz5nDp0u/4+s4i\nJGRVqVqBih5D3knNq5DMQAEJTceOHdm0aROnTp3i119/5fLly1hbW+Pi4sL8+fOL9ea7uLhQtWpV\n3e0FCxbQq1evPFfsfHm8jEKh0D2vIMVZxVMQylKtMi4/v1U9y7regurOqaSJz82bN1+q6wF3794t\nUTmVyT//SPjmG2O2bDHGzk6Nl5eSfv1UFHeHmaKMXdm0aSNKpZL09LQcLSv1aNu2HX369MPGpj42\nNjZUr25dqg/YnB/eFSWJMHT3TlbdWWviJCc/4/33+xARsYaJE8une7UiDBwvK4WOoXn77bd5++23\nS1WJl5cX8+fPx8HBgV9++YXWrVvj6OjI8uXL8fLy4t69e6jVaqytrWnVqhVnzpzBycmJmJgYOnfu\nXKq6BUGoHPz9/XW3zczMmDNnjgGjKTsaDZw7Z8TGjdoZJwMGKNm+PZXWrYvexZaens7NmzeIjb1O\nbOx/OXToYKHPcXV9Hz8//1InK0WV88PbUCpKHFl8fPx0Ww5YWlbhm2924eram0aNGtO3r2s5xaBN\nYLTvyauRzABINBpNmYwCun37NjNnzmT37t1cvXqVBQsWYGxsTM2aNVmwYAGWlpasXr2amJgY1Go1\nc+fOpWPHjty8eZP58+ejVCpp3LgxCxcuLHSV4oqyP4cg1KpdeItiaTy4/9Qg9RZUd06l7Zq6evUq\nrVu3LlUZ+lbUa8wBx8lIpUb839nVuR5LTYX9+2V8+aUJT59KGDMmAw8PJS/1vGej0Wh49OgRsbH/\nJTb2Otev/1d3++7dO9Sv34BmzZrTtGlzmjZtxpkzp9mxY2ueZRmyZaKi7KFUUeKA3LFcuHCOYcOG\nsGvXv2nb1sFgcRhKceIo6PpSZglNeaoIvxBBAJHQQOkTmhEjRuhlNfKXl4542bFjx1izZg0ymQw3\nNzeGDBlSaFlFucYccJyM121tXRvrD2fAhTUA3LolYfNmY3bsMKZ9ezVeXhn07Pli7RilUkl8/N+5\nkpbY2P+iVmto2rTZ88SlGU2bNqdZs+Y0bGiHiUnunbLz6noydDdLZfzQLGt5xXLgwD4CAj7l8OFo\n3nijrsHiMAR9JTSFdjllZmayd+9e7t69S+fOnWnWrBnW1tZFj1QQBKEY9PEdK+fSEVmUSiXBwcFE\nRkZibm6Op6cnzs7OpV6V+OVkBsDr9lbWtDLnPx3DOHNGxpAhSnbsuEdGxjXi4mJZtOi/XL/+X+Li\nrnPrVjxvvFFX19rSocNbeHj8i6ZNm1OzZs1idQ/puleylvqvQFOChYINGPABcXGxDB/uwb//fQi5\n2Em02ApNaPz9/alduzY///wzbdu2Zc6cOWzYsKE8YhME4TU0bNiwUpeRc+mILHFxcdja2lKtWjUA\nOnTowNmzZ+nXr1+J68qZzGSZ/HA9T4+l86jd/4iKusy2bWk0bdpU18oyeLA7TZs2p1GjxpiZmZW4\n/pwCgayPQgVgyCkTFssWg9wUKsDYlcpg+vTZxMXFMmXKeDZu3CIWlS2mQhOaW7dusWjRIs6fP4+z\nszPr168vj7gEQXhNhIWF5Tp2/fp1oOQL6+VcOiJLcnJytn3p5HI5ycnJJaoD8k9msszN2MyAS42o\nNexDzG1t0VjXQFOjBmpr7Y+mRg00pvmv3lpcFssW61pn4EVLTYoBWmlejsVCkW6QGCobiURCaOgX\nDB48kMWLP2PevEBDh1SpFKnLKTExEdBeDETGKAiCPmV19/zwww/Ur18fR0dHLl++TEJCgt7rsrS0\nzHO5iMLkt9aVVFr49fCJvA6tGzWAhw/g+h/w8GH2n/R0qFmzaD+1amn/zWu54MBAyGPqtjxkCXK5\nqfbx8pIjFoPEkAdDruOUU/6xVOHgwf107tyZ9u3bMHr0aAPFUb70EUehCc306dPx9PTkwYMHuLu7\n4+cnsmxBEPQna0G977//nsDnH3gDBgwokwt5kyZNiI+PJykpCQsLC86dO4eXl1ehz8tvrav/O7ua\njY7qfFtpsgYHPyio8LQ0jB4nInn0CKNE7Y/u9qWrSBIfYfQoUftv4iOMHj0EqfRFK4+1NUb37iH7\n81r+dQQFkXrrDikTvcHUFI2pKRpTMzA1BVmhHwPFkrOV6OUYFAZsqakoA2ChKLGYsmXLLgYO7Ie1\n9Rt07drNQHGUj3IbFNypUyeOHDlCYmIiVatWRabn//yCIAgASUlJ3Lp1C1tbW27cuFGkVcKL6uDB\ng6SkpODu7o6vry9eXl5oNBrc3NyoU6dOqcoecGENGx3JldS8PNOpQGZmqOvaQF0bMotSoUYDKSnZ\nkh/zLV8XnNAApvv3YRL9A2SkI0lPQ5KeDmlpIJGAqRkaM1M0JqbahMfMLPttU1MwMUVjZqo9N9tt\nEzRm2uTIOOY4pj98n28MhuwCq2yaNWtORMRGxo4dyX/+c4TGjZsaOqQKr9Bp2wcOHEAqlZKRkaFb\nCK8o32jKU0XIMAUBxLRtKHnT8blz5wgKCiIxMZE6deoQGBiIg0P5rclRkNJM2y4v+baMoJ3tlG8S\noVJBmjbBkWSkv7idngbpGc+TnzRIe/54ejqStOfHMjKe306HjHSMT/+C8cXfCoxT2daB9A8Gk9m0\nmfanoR3FXhq5mCyWLUYuN+VBBRmcXJwWiS1bviY8/AsOH46menX9zjB+7VpotmzZwoYNG5g5cybH\njx9nzJgxFS6hEQSh8uvYsSPbt2/nzp07NGjQoNJNW81qqZFKjRiQx8J6ZS0lx5TtLAUmM6DtcrK0\nRGNpiT4WJSsosUpzH4qyYyeksdcx/vknZLHXMUq4S2b9BtrkpkkzXaKjatIMTc2aFGlXziLGUxEG\nJxd35teIEaOJjb3OmDHD2bVrX57rDwlahSY0WdMJ5XI5JiYmqFSqMg9KEITXz5EjR1i7di2ZmZn0\n7dsXiUTCpEmTDB1WsQy4sMag33pzJjWFJjPlEEOWfGNJT0f6902ksdeRxl3H+NfTmG3fijTuOqg1\nZDZtSmbjptokJyvpadwEijDVvSLN+soZT3GSq4CABYwe/S98fGbw+edh5bJ1RWVUaELToEED3N3d\nmTt3LmFhYdjb25dHXIIgvGa+/vprdu/ejZeXF5MmTcLNza3SJTQVQcpLexelGKiLpViJlakpmfYt\nyLRvkeshyaNHSGOvI4u7jjT2OmZ7diGNu470VjzqOm+Q2aTpiyTnecuOuq4NSCT5thQZKqkpTXIl\nlUoJD/+SAQP6smbNF0yZMq3M4qzMCk1ogoODUSgUyOVy2rRpQ61a5bGvryAIrxupVIqJiQkSiQSJ\nRJJrlV+h6FJ8/JDXqgIGHB+hj8RKU6MGqho1UDnl2KRYpUJ6629tq87168iuXML031HIYq8jSUlB\nbWmJ9P4/+ZYrD1kCarU2Romk1N1ahdFHcmVpack33+yiX79eNG7cBFfX9/UeZ2VX6KDguXPn5joW\nHBxcZgGVREUY1CQIIAYFQ8kHBa9YsYLbt29z9epVnJycsLCwwNfXt0Rl6VtxrjGVcaDlqxSH5EkS\n8sB5mG8reD8wzfMkRqLRaG8bGWX/kRih0d2X5HP8xTGMJHkeN3r4AKMH9wuMpThdg7//fgFPTzd2\n7dqHg0P7or0p+aiM/0dKNSjY1VW7nblGo+HatWvcv1/wL0YQBKEkZs6cSUxMDK1ataJJkyb07NnT\n0CEJlZCmmhXJn4ehrmtTtFlfGo32R63O9SPRvHxfk8/xnOdnL8v86w2Yb/lab6+vfXtHli9fxYgR\nnhw+HE3dujZ6K7uyKzSh6d69u+52jx49GDNmTJkGJAjC6yk5OZnz589z//59bG1tiY+Pp2HDhoYO\nS6ikijw4OavLKY9V8PPrvijObLDkkFWoa9cp2ZT6fLz//gBu3Ihl2DB39u8/jKWlZbGe/6oqdN3u\nkydP6n727dvHw4cPyyMuQRBeM35+fjRo0ID4+Hhq1qzJp59+auiQhEouxccPxewX3ZaGmPWVVxy6\neGbNKXE83t4zaNOmLZMmfYxarS5tiK+EQltovv32W91tExMTFi9eXKYBCYLwekpKSuKjjz7iwIED\nODo6iou0oBcVYdZXtjiet9Rk1rXRLipYQhKJhOXLVzJkyCAWLAggIGCBPsKs1Io0y0kQBKE8xMXF\nAXDv3j2k0tybQQpCSVSEWV9ZcYA2uXraqRtVPx5F+oAPwMKiROWZmJjw1VdbcXXtTZMmTRk2bKQ+\nw610xMZMgiBUCPPmzcPPz4+4uDimTp1KQEBAicpRq9UEBgby119/YWJiwsKFC7ONxVm4cCEXLlzQ\nrUQcHh5epB23BUEfspIr1YNnKN9ywmLtalJmzSlxedbWNdi2bTf9+/elYUM7und/R4/RVi4ioREE\noUJo3rw5a9eu5datW9jZ2WFlZVWicn744QcyMjLYtWsXv//+O0uWLGHt2rW6x69evcqXX36JtbV+\n98URhOJSzAuk+nvvkDZsJOo6b5S4nCZNmrF+/deMGzeaAwe+o2nTZnqMsvIodFBwZmYme/bsYdWq\nVZw5c4bExMTyiEsQhNfMtm3b8PT0ZMOGDbi7u7N///4SlXP+/Hnd7Mz27dtz5coV3WNqtZr4+Hj8\n/f3x8PAgMjJSL7ELQkmoG9qR5jkci6WLSl1Wt249+PTTAP71r8EkJj7SQ3SVT6EtNP7+/tSuXZuf\nf/6Ztm3bMmfOHDZs2FBowRcvXiQkJIStW7cSHx+Pr68vEomEZs2aERAQgJGREWFhYRw/fhyZTIaf\nnx8ODg75nisIwqttz549HDhwAFNTU1JTUxk2bBgDBw4sdjnJycnZprFKpVJUKhUymYyUlBSGDRvG\n6NGjyczMZMSIEbRp04YWLXIvu/+y6tUtkMmKPqanpIsL6puII7uKEge8FMuiILC3x/ze39C2banK\nnD59MnfvxjNu3EiOHj1apI0sK8p7oo84Ck1obt26xaJFizh//jzOzs6sX7++0EI3bNjAgQMHdEuX\nBwcHM336dJycnPD39yc6OhobGxt+/fVX9uzZQ0JCAt7e3kRFReV5rouLS6lfqCAIFVuNGjV0A4HN\nzMxK3OVkaWmJQqHQ3Ver1chk2kudubk5I0aM0F2bOnfuzJ9//lloQvP4cUqR66+Mq6+KOMpX9lik\nmE2fjenU6TzZ/e9Slz1r1qeMHj2MkSPHsGpVeIEbWVaU90RfKwUXqcspq5spOTm5SK0ltra2rF69\nWnf/6tWrdOrUCdAuzvfzzz9z/vx5unXrhkQiwcbGRldPXucKgvDq02g0DBo0iPnz5zN48GAePnzI\nrFmzmDWreFNtHR0diYmJAeD333+nefPmusf+/vtvPD09yczMRKlUcuHCBVq3bq3X1yEIxZU20guj\n/93C+NjRUpdlZGREePgGrl69wurVn+shusqj0Baa6dOn4+npyYMHD3B3d8fPr/BFgPr06cPt27d1\n9zUajS5LlMvlPHv2jOTk5GzfwLKO53VuYYrbHCwIlZUhm4fLuu6BAweSnJyMVCrl559/Zvjw4bRq\n1arY5bi4uHDq1Ck8PDzQaDQsXryYr7/+GltbW3r16sXAgQMZMmQIxsbGDBw4kGbNXs8BlEIFYmyM\nwn8BloHzeNyjJ8hKN19HLpfrNrJs1KgJ/fsXv+u2Mir0XevUqRNHjhwhMTGxxLMCXm7VUSgUVK1a\nNVezsEKhoEqVKnmeW5jiNAcLQlkq673o82uWLet6C6o7p5ImPnv27GHKlCls376dmTNnsnPnTkaN\nGlXscoyMjPjss8+yHWvSpInu9tixYxk7dmyJYhSEspLR1xXzdWsw276VtBGjS11e3bo2bNmyA3f3\nD58ca38AABWHSURBVGjQoAHt2zvqIcqKrdD+o507d/LBBx8wbNgwXF1ddZtVFkerVq04c+YMADEx\nMXTs2BFHR0dOnjyJWq3m7t27qNVqrK2t8zxXEIRXn0Qi4a233uLp06f83//9n5gMILxeJBIUQYuw\nWLYYSbJ+xrU4OLQnNHQ1I0cO5c6d24U/oZIrtIVmy5YtrF+/nmrVqpW4kjlz5jB//nxWrFhB48aN\n6dOnD1KplI4dO+Lu7o5arcbf3z/fcwVBePWpVCqWL19Ox44dOX36NEql0tAhCUK5UrV7E+U7PTFf\n/Tkpc/31Uqar6/vcuBHHsGHuHDx45JXeyFKi0WgK3Dh0xowZhISEVOhlyCvCKG1BAKhVu/Au0tJ4\ncP+pQeotqO6cStrl9Pfff3Pq1CkGDx7MDz/8QNu2bWnQoEGJytK34lxjKuPMERFH+SooFqM7t6nu\n/DaPj51CXa++XurTaDTMnOnNw4cP2LRpO1KplGXLFiOXmzLZgPtbAcWOo6DrS6EtNJ07d6Z37940\naNBAN2B3y5YtRY9WEAShCOzs7LCzswMoUde2ILwK1PXqkzrKC/miIJ6FF77mW1FIJBKWLl2Bh8eH\nBAXNx9LSkpDnm2QqFOn4GGAHctAmM/qMo9CEZteuXaxcuVLsdSIIgiAI5SDVewbVOzsi+/0CKj0N\n5jUxMWHjxi107vwmjx8/1h3PSijKO6l5OZnRVxyFJjR16tShbdu2YoCeIAiCIJQDjWUV7SaWAZ/y\n5N+HoIDF8Ypjw4aIbMlMlvJOanImM/qKo9CEJiMjQ7dWQ9b6MKGhoSWqTBAEQRCEwqUNHY75lxGY\nHP6WDNf3S11efklElpCQJZw5c5pu3bqjVCrJzFShVKpQqVSoVEqUStXzY8rnx1QvnadEpcpEpVJm\nOz/r9svnP3nyhNTU/JdaKU1SU2hCM378+GIXKgiCIAhCKchkJAcuxNLPh4ze70ER9mUqrbS0VFJT\nU5FKpZiamiGXy5DJjJHJpMhkxhgbGyOTyV760d43Nn5xO+t87bkvHpdKZRgbG7NuXThr164uPJgS\nyDeh+fHHH+nZsyc3btzItRdE1tYEgiAIgiCUDaWzC+qGdphv3kjqxxNLVVZWi0d+rTSzZ/uWS5dT\nUNAi5HJ5mcSRb0KTlJQEwMOHD0tUsCAIgiAIpZMcuAgrt/6kDfZAY1W9VGXll9SUVzJT1nHkm9B8\n8MEHgHYZ8UmTJumOi/EzgiAIglA+Mlu2Ir3f/2HxeQiKoEWlLi9nMlHeyUxZxpFvQrNnzx4iIyOJ\ni4vT7VyrVqtRKpXF3v22IgkPX1Gm5U+aNLNMyxcEQRBeLwqfT7F+x4nU0WNR2zUqdXlZiYOhF9bT\ndxz5JjQDBw6kS5curFu3jgkTJgDa1poaNWqUulJBEARBEIpGU6cOqeMmIV8YyLMvN+ulTB8fvwqx\nerI+48g3oTExMaF+/fosWLCg1JUIgiAIglByKROmYN21A7Jfz6Dq5GTocCoksVqeIAiCIFR0FhYo\n5s7HMmAuFLwF42tLJDSCIAiCUAmkD/YApQrTf0cZOpQKSSQ0giAIglAZGBmhCFqEfFEQpKUZOpoK\nRyQ0giAIglBJKN/ujqpVa8w3RBg6lApHJDSCIAiCUIko/BdgsWYlErHwbTaF7uUk6IdY/0YQBEHQ\nh/9v796Doqz7Po6/l0UQWBAs46k7NfDQ+MxURgdrwgkPFXZQ8zFFdM2k0sbW1CyEhPZWINIUJx0P\nkKWpecjKjndmh0dHKzQNO2j5lFmhhZpkchDYw/MHN6RbeWex18XC5zXDOHst8v3CDh+++9vfXpe7\nazdqbh9KxBOPUZGvk9020AqNiIhIgKmcmkHohhew/t8+s1tpNjTQiIiIBBjvOedQdf9kImZkmd1K\ns6GBRkREJABV3z2O4L17aLN1i9mtNAsaaERERAJR27ZUTncS8egj4PGY3Y3pDN0UfPvtt2Oz2QC4\n8MILGT58OLm5uVitVhITE7n//vvxeDw4nU6+/PJLQkJCyMnJoXPnzka22eKYtSFZG6FFRPyrZtAQ\nwpYsJHTdampSRprdjqkMG2hqamrwer2sWLGi8digQYOYP38+HTt25N5772XPnj2UlpZSW1vL2rVr\nKSkpIT8/n0WLFhnVpoiISOCwWKiYkUfUPWOoGXg7hIeb3ZFpDBtovvjiC6qrqxk7diwulwuHw0Ft\nbS2dOnUCIDExkffff58jR47Qu3dvAHr27Mlnn31mVIvSQvh7ZQi0OiQizYfrql7UXdWL8EXzqXow\n3ex2TGPYQNO2bVvS0tK44447OHDgAPfccw9RUVGN90dERPD9999TUVHR+LIUgNVqxeVyERz8x63G\nxIQTHGz1a/9/VocOka2qrpm19T23ntoicmaV053E3Hg9J0fdiSf2v8xuxxSGDTRxcXF07twZi8VC\nXFwckZGR/Pzzz433V1ZWEhUVxcmTJ6msrGw87vF4zjjMAJSXV/3pPh51Os+697NxxKRn7keOnDCl\nrpm1m+P33KGF1j1TbV8afESM5+l8ESdH2AnPz6GiYIHZ7ZjCsHc5rV+/nvz8fADKysqorq4mPDyc\n7777Dq/Xy9atW7nyyitJSEhgy5b6t6CVlJTQvXt3o1oUEREJWFWTpxK68V9YP2+dWzUMW6EZOnQo\nGRkZjBgxAovFQl5eHkFBQUydOhW3201iYiKXXXYZl1xyCdu2bSMlJQWv10teXp5RLYqIiAQsb7to\nKqc8hM35CMfXbQCLxeyWDGXYQBMSEsKcOb+95sS6detOux0UFMSMGTOMaktERKTFOHlnGmFLCwl5\ndxO1/W40ux1D6cR6IiIiLUWbNlRmzyTCOR1cLrO7MZQGGhERkRakNvlmPOecS9tVz5rdiqE00IiI\niLQkFguV/8wlfPZjWE78YnY3hml1A40Fr18/REREzOa67HLqru9D2Px5ZrdiGEOv5dSatdTz34iI\nSPNUmZlNTN/rOHnnWDz/uNDsdvyu1a3QiIiItAaef1xI9Zg0InL/aXYrhtBAIyIi0kJVOybTZsv/\nElyyy+xW/E4DjYiISAvltUVS9XAmEY8+At6Wvc9TA42IiEgLdjLVTlD5MUL+9brZrfiVNgW3AmZt\nSG6NG6H9/U63w5h3QU4RCVDBwVQ4c7FlTKW2/40QEmJ2R36hFRoREZEWrq5vfzwXxRG27CmzW/Eb\nrdAYRM/cjePvlSFonqtDIiJnUuHMJfp/buXksBGEFS6CiFCY8KCpPYXPymuyPjTQiIiItALuHv9N\nzYBbaTdiKG127gAgvLKGqoczTeknfFYeEU/kN1kfeslJRESklfDaIhuHGYCIJ/LrV0kMduow01R9\naIVGpAUw4rIbellTJLCFz8ojfNH83xxvGCyMWqnxHWaaqg8NNCIiIi3cHw0RDSKeyCd4dwm1N9+K\nNygIGj6sVggKwmu1QpD138fq7/M23v713/pjll//36mfY7US9nQhYUsLz9gH/LWhRgONiIiIEFT2\nA8E7irG43eDxgNsNXg+4PVgabnvq7/v1czz//hz3r8c8v95nOe22m6CjR/zWvwaaVkDvsBIRad0a\nVjz+aJWmcuo0019y+rt9aFOwiIhIK1D1cCaVU6f95riRw4w/+9AKjfiNVoZERJoX35Uao4cZf/bR\nLAcaj8eD0+nkyy+/JCQkhJycHDp37mx2WyIiIgGvcZiICKXKxBPrNXUfzXKgefvtt6mtrWXt2rWU\nlJSQn5/PokWLzG5LAoTewiwicmZVD2cS0SESjpibZU3ZR7PcQ7Nz50569+4NQM+ePfnss89M7khE\nRESas2a5QlNRUYHNZmu8bbVacblcBAf/frsdOkT+6a/t9fuT99/vxay6ZtZuuXXNrN38vufW4Gwy\n5q98vr+oj9M1lz6g+fTSkvpolis0NpuNysrKxtsej+cPhxkRERGRZjnQJCQksGXLFgBKSkro3r27\nyR2JiIhIc2bxeo1ZrD4bDe9y2rdvH16vl7y8PLp06WJ2WyIiItJMNcuBRkRERORsNMuXnERERETO\nhgYaERERCXgaaJrQ7t27sdvthtasq6vjoYceIjU1laFDh/LOO+8YWv+nn37i+uuv5+uvvzasZl1d\nHQ8++CApKSmkpqYaUvvUx/ann37ivvvuY+TIkaSkpPDdd9/5pabb7SYjI4OUlBRGjBjBvn372Lt3\nL6mpqdjtdtLS0jh69Khfai9ZsoThw4czZMgQnn/++cbjr776KsOHD/dLzZbAjAw4ldl54MuMfPBl\nRl74MiM/fJmZJ778lS96L3QTKSoq4pVXXiEsLMzQuq+88grR0dHMnj2bn3/+mcGDB9OvXz9DatfV\n1ZGdnU3btm0Nqddg8+bNuFwu1qxZw7Zt25g3bx7z58/3Wz3fx3b27Nncdttt3HzzzXz44Yfs37+f\nTp06NXnd9957D4A1a9ZQXFxMQUEBJ06cICsrix49erBmzRqKiorIyMho0rrFxcV8/PHHrF69murq\nap5++mkA9uzZw/r169G2u99nVgacysw88GVWPvgyOi98mZUfvszKE1/+zBet0DSRTp06GfpL0iA5\nOZkHHngAAK/Xi9VqNaz2448/TkpKCuedd55hNQHi4uJwu914PB4qKir8fo4i38d2165dlJWVMWbM\nGF599VWuvvpqv9Tt378/M2fOBODQoUNERUUxd+5cevToAdQ/4woNDW3yulu3bqV79+5MmDCB8ePH\nk5SURHl5OXPnziUz0/iL2AUKszLgVGbmgS+z8sGX0Xnhy6z88GVWnvjyZ75ohaaJ3HTTTZSWlhpe\nNyIiAqg/u/LEiROZNGmSIXVffPFF2rdvT+/evSksLDSkZoPw8HAOHjzIgAEDKC8vZ/HixX6t5/vY\nHjx4kKioKJYtW8aCBQsoKipq/CPS1IKDg0lPT2fTpk08+eSTjX8cdu3axcqVK1m1alWT1ywvL+fQ\noUMsXryY0tJSxo8fT5cuXcjIyDAk8AKVWRlwKrPywJeZ+eDL6LzwZWZ++DIjT3z5M1+0QtMC/PDD\nD4wePZpBgwZx2223GVLzhRde4P3338dut7N3717S09M5cuSIIbWXLVtGYmIiGzdu5OWXX2batGnU\n1NQYUhsgOjqavn37AtC3b1+/X2vs8ccfZ+PGjWRlZVFVVcUbb7zBo48+SmFhIe3bt2/yetHR0SQm\nJhISEkJ8fDw//vgjBw4cwOl0MmXKFL766ityc3ObvK40DTPywJeZ+eDL7LzwZXR++DI6T3z5M1+0\nQhPgjh49ytixY8nOzubaa681rO6pk7zdbsfpdNKhQwdDakdFRdGmTRsA2rVrh8vlwu12G1Ib4Ior\nrmDz5s0MHjyYHTt20LVrV7/U2bBhA2VlZYwbN46wsDAsFgtvvfUW69atY8WKFURHR/ul7hVXXMGz\nzz7LXXfdxeHDh4mNjeW1117DarVSWlrKlClTeOSRR/xSW/4es/LAl5n54MvsvPBlVH74MitPfPkz\nXzTQBLjFixfzyy+/sHDhQhYuXAjUb0IzeyOeP40ZM4bMzExSU1Opq6tj8uTJhIeHG1Y/PT2d6dOn\ns2bNGmw2G3PmzPFLnRtvvJGMjAxGjhyJy+UiMzOTzMxMzj//fBwOBwBXXXUVEydObNK6ffr0YceO\nHQwdOhSv10t2drapezHkz2uNefCfmJ0XvozKD19m5Ykvf+aLzhQsIiIiAU97aERERCTgaaARERGR\ngKeBRkRERAKeBhoREREJeBpoREREJOBpoGlF8vPzsdvtJCcnk5SUhN1uZ+LEiRQXFzN58mS/1b3u\nuuv89rV9TZs2jS1btpx2rLS0lGHDhp3V1/H3z0SkpVG+/HnKF//QeWhakWnTpgH1pyXfv38/U6dO\nBep/uURE/g7li5hNA40A8O2333L33Xdz7Ngx+vTpg8PhwG630759e44fP05hYSFOp5Nvv/0Wj8fD\npEmT6NWrF2+++SarVq3C5XJhsVhYsGAB7dq1Iysri6+++oqOHTtSW1sLwFtvvUVRURHBwcGcd955\nFBQUEBT06yKh3W4nLi6Ob775Bq/XS0FBAR06dGDOnDl89NFHeDwexowZw4ABA07rbenSpaedmOm5\n555j6dKluN1ucnNzT7uv4Wq7oaGhREdHk5eXR2RkJDNnzuSTTz6hrq4Oh8NBZGQkANXV1TgcDgYO\nHMjAgQMNejREWhbli/LFCBpoBICamhoWLlyI2+0mKSmp8cyRt956KzfccAPPPfccMTEx5OXlUV5e\nzqhRo3j99dc5cOAAhYWFhIWFkZ2dzdatWwkJCaGmpoZ169Zx6NAhNm7cCMBrr71GWloaycnJbNiw\ngYqKCqKiok7rIyEhgRkzZrBq1SqWLFlC7969KS0tZfXq1dTU1DBs2LDGJeaG3nwlJCRw7733snnz\nZmbPnt34zNHr9ZKVlcXq1auJjY1l+fLlLFq0iISEBMrLy1m/fj3Hjx/nmWee4dprr6Wqqorx48cz\nevRo+vXr588fv0iLpnxRvhhBA40A0K1bN0JCQoD6K7I2iIuLA2Dfvn3s3LmTTz75BACXy8WxY8c4\n55xzSE9PJyIigv3799OzZ08OHTrEpZdeCsAFF1zA+eefD0BGRgZLlixh5cqVxMfH079//9/0cc01\n1wD1ofHuu+8SGxvL559/jt1ub6x78ODB03rzdeWVVwJw+eWXM2vWrMbj5eXl2Gw2YmNjgfrTfM+d\nO5eYmBh69uwJ1F/rZdKkSRQXF7N9+3YuvvjixmeAIvLXKF+UL0bQpmABwGKxnPF4fHw8t9xyCytW\nrKCoqIjk5GTatGnDk08+SUFBATk5OYSGhuL1eunatSslJSUAlJWVUVZWBsDatWtxOBysXLkSgE2b\nNv2mXsOVZ3ft2kXXrl2Jj4+nV69erFixguXLlzNgwAA6dux4xp4bQvGjjz6iW7dujcdjYmKoqKjg\n8OHDAGzfvp2LLrqI+Ph4Pv30UwBOnDhBWloaAElJSSxYsIB58+Y1fg8icvaUL8oXI2iFRv6UlJQU\npk+fzqhRo6ioqCA1NRWbzUZCQgLDhw8nODiYqKgoDh8+zJAhQ9i2bRt33HEHF1xwATExMQBceuml\njBs3joiICMLDw0lKSvpNnZdeeolly5YRFhbGrFmziI6OZvv27aSmplJVVUX//v2x2Wxn7HX37t2M\nHj0ai8VCXl4eDZcrs1gs5OTk4HA4sFgstGvXjscee4yYmBg++OADRowYgdvtZsKECY1f69xzz8Xh\ncJCZmclTTz31hyEnIn+d8kX50hR0cUppNux2O06nky5dupjdioi0MMqXlk8vOYmIiEjA0wqNiIiI\nBDyt0IiIiEjA00AjIiIiAU8DjYiIiAQ8DTQiIiIS8DTQiIiISMDTQCMiIiIB7/8BQQVf+BH1WfkA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1074ea198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_cuda = [df_cuda[\"Total\"].values,\n",
    "             df_cuda[\"E-step\"].values,\n",
    "             df_cuda[\"M-step\"].values,\n",
    "             df_cuda[\"Total\"].values-df_cuda[\"E-step\"].values-df_cuda[\"M-step\"].values]\n",
    "\n",
    "fig, axarr = plt.subplots(1,2,figsize=[8,3])\n",
    "\n",
    "stackedbar(axarr[0],df_cuda[\"t_cuda\"],data_cuda[1:],\"Threads per block\")\n",
    "\n",
    "axarr[0].legend(loc=0,fontsize=fontsize)\n",
    "axarr[0].set_title(\"Kmean Cuda timing\",fontsize=fontsize,fontweight=\"bold\")\n",
    "\n",
    "scaling_curve(axarr[1],df_cuda[\"t_cuda\"],data_cuda[:-1],\"Threads per block\")\n",
    "axarr[1].legend(loc=2,fontsize=fontsize)\n",
    "axarr[1].set_title(\"scaling curve\",fontsize=fontsize,fontweight=\"bold\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"plots/Cuda_scaling.jpg\",dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N_MPI</th>\n",
       "      <th>n_omp</th>\n",
       "      <th>Total</th>\n",
       "      <th>E-step</th>\n",
       "      <th>M-step</th>\n",
       "      <th>IO</th>\n",
       "      <th>n_hybrid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2363.860598</td>\n",
       "      <td>2112.615470</td>\n",
       "      <td>250.408402</td>\n",
       "      <td>4.006589</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1662.239609</td>\n",
       "      <td>1356.238149</td>\n",
       "      <td>304.833094</td>\n",
       "      <td>3.732907</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1189.350777</td>\n",
       "      <td>813.090273</td>\n",
       "      <td>375.009360</td>\n",
       "      <td>3.693638</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>931.707351</td>\n",
       "      <td>500.118240</td>\n",
       "      <td>429.142173</td>\n",
       "      <td>3.934515</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1358.942563</td>\n",
       "      <td>1212.020400</td>\n",
       "      <td>145.609492</td>\n",
       "      <td>3.800784</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   N_MPI  n_omp        Total       E-step      M-step        IO  n_hybrid\n",
       "0      1      1  2363.860598  2112.615470  250.408402  4.006589         1\n",
       "1      1      2  1662.239609  1356.238149  304.833094  3.732907         2\n",
       "2      1      4  1189.350777   813.090273  375.009360  3.693638         4\n",
       "3      1      8   931.707351   500.118240  429.142173  3.934515         8\n",
       "4      2      1  1358.942563  1212.020400  145.609492  3.800784         2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hyb = pd.read_excel(file,sheetname=\"hybrid\")\n",
    "df_hyb[\"n_hybrid\"] = df_hyb[\"N_MPI\"]*df_hyb[\"n_omp\"]\n",
    "df_hyb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gLy8PCoXCoPeSqLWwJ4molfz5r9+pU6fCxcUFV69eFSZxG1ugsKXH8+fPx7PPPouamhr8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b5ceb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[6,3])\n",
    "\n",
    "ax.scatter(df_hyb[\"n_hybrid\"],df_hyb[\"Total\"],alpha=0.5)\n",
    "\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_ylim([0,5000])\n",
    "\n",
    "ax.set_xticks(np.exp2(np.arange(7)))\n",
    "ax.set_xlim([0.8,80])\n",
    "ax.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "\n",
    "plt.title('Kmean hybrid MPI-OpenMP timing',fontsize=fontsize,fontweight=\"bold\")\n",
    "ax.set_xlabel(\"MPI_processes x OpenMP_threads\",fontsize=fontsize)\n",
    "ax.set_ylabel(\"time use (ms)\",fontsize=fontsize)\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"plots/hybrid_scaling.jpg\",dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_hybrid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2363.860598</td>\n",
       "      <td>2363.860598</td>\n",
       "      <td>2363.860598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1510.591086</td>\n",
       "      <td>1358.942563</td>\n",
       "      <td>1662.239609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1014.122154</td>\n",
       "      <td>909.529512</td>\n",
       "      <td>1189.350777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>668.929418</td>\n",
       "      <td>505.915032</td>\n",
       "      <td>931.707351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>415.931598</td>\n",
       "      <td>301.355469</td>\n",
       "      <td>663.490676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4</td>\n",
       "      <td>331.330188</td>\n",
       "      <td>297.959041</td>\n",
       "      <td>402.007910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>4</td>\n",
       "      <td>3724.246043</td>\n",
       "      <td>783.003317</td>\n",
       "      <td>5194.099651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>3</td>\n",
       "      <td>12033.302202</td>\n",
       "      <td>10385.388325</td>\n",
       "      <td>13332.237565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2</td>\n",
       "      <td>32050.996865</td>\n",
       "      <td>30551.306969</td>\n",
       "      <td>33550.686761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>1</td>\n",
       "      <td>69710.298024</td>\n",
       "      <td>69710.298024</td>\n",
       "      <td>69710.298024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count          mean           min           max\n",
       "n_hybrid                                                 \n",
       "1             1   2363.860598   2363.860598   2363.860598\n",
       "2             2   1510.591086   1358.942563   1662.239609\n",
       "4             3   1014.122154    909.529512   1189.350777\n",
       "8             4    668.929418    505.915032    931.707351\n",
       "16            4    415.931598    301.355469    663.490676\n",
       "32            4    331.330188    297.959041    402.007910\n",
       "64            4   3724.246043    783.003317   5194.099651\n",
       "128           3  12033.302202  10385.388325  13332.237565\n",
       "256           2  32050.996865  30551.306969  33550.686761\n",
       "512           1  69710.298024  69710.298024  69710.298024"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hyb.groupby('n_hybrid')[\"Total\"].aggregate(['count','mean','min','max'])"
   ]
  }
 ],
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