mps-performance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 1: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot \n",
    "\n",
    "plt.rcParams[\"font.size\"] = \"15\"\n",
    "labels = ['1 Container', '5 Containers', '10 Containers']\n",
    "mps_means = [9544, 2267, 1178]\n",
    "nomps_means = [9514, 1774, 902]\n",
    "\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.25  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,10))\n",
    "fig.set\n",
    "\n",
    "rects1 = ax.bar(x - width/2, nomps_means, width, label='W/O MPS')\n",
    "rects2 = ax.bar(x + width/2, mps_means, width, label='W/ MPS')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('GFLOPs')\n",
    "ax.set_title('GPU Share Performance with and without MPS')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.legend(fontsize=15)\n",
    "ax.autoscale_view()\n",
    "\n",
    "\n",
    "def autolabel(rects):\n",
    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate('{}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 3),  # 3 points vertical offset\n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Task 5  27.89% \n",
    "# Task 10 30.59%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 2: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "from statistics import mean \n",
    "\n",
    "raw_data = [\n",
    "    [561.424, 562.109, 562.116,563.423,563.277],\n",
    "    [1115.657, 1116.113, 1120.452, 1113.281, 1113.515],\n",
    "    [2208.649, 2184.014, 2184.454, 2184.553, 2184.274],\n",
    "    [2228.590, 2300.825, 2224.736, 2322.735, 2252.484],\n",
    "    [2309.586, 2265.397, 2261.643, 2301.094, 2218.744],\n",
    "    [2259.235, 2303.627, 2253.201, 2211.359, 2309.130]\n",
    "]\n",
    "\n",
    "averages = [mean(subarray) for subarray in raw_data]\n",
    "\n",
    "labels = ['5%', '10%', '20%', '50%', '80%', '100%']\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.25  # the width of the bars\n",
    "\n",
    "# rects1 = ax.bar(x - width/2, nomps_means, width, label='W/O MPS')\n",
    "# rects2 = ax.bar(x + width/2, mps_means, width, label='W MPS')\n",
    "\n",
    "plt.bar(x, averages, align='center', alpha=0.5)\n",
    "plt.xticks(x, labels)\n",
    "plt.ylabel('GFLOPs')\n",
    "plt.title('Performance under different CUDA_')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Line plot\n",
    "plt.rcParams[\"font.size\"] = \"12\"\n",
    "\n",
    "x_list = ['5%', '10%', '20%', '50%', '80%', '100%']\n",
    "y_list = averages\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "# plt.figure('Line fig')\n",
    "# ax = plt.gca()\n",
    "# ax.set_xlabel('CUDA_')\n",
    "# ax.set_ylabel('GFLOPs')\n",
    "\n",
    "# ax.plot(x_list, y_list, color='b', linewidth=1, alpha=0.6)\n",
    "\n",
    "plt.plot(x_list, y_list, marker='o')\n",
    "plt.xlabel('CUDA_MPS_ACTIVE_THREAD_PERCENTAGE')\n",
    "plt.ylabel('GFLOPs')\n",
    "plt.title('5 Tasks Performance under \\n different threads percentage')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Experiment 3: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot\n",
    "# https://matplotlib.org/3.1.1/gallery/statistics/boxplot_demo.html\n",
    "# https://matplotlib.org/3.1.1/gallery/statistics/boxplot_color.html\n",
    "\n",
    "from matplotlib.patches import Polygon\n",
    "plt.rcParams[\"font.size\"] = \"12\"\n",
    "\n",
    "random_dists = ['Normal(1,1)', ' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)',\n",
    "                'Triangular(2,9,11)']\n",
    "data = [\n",
    "    [1162.205,1162.397,1139.097,1174.254,1175.631, 1174.566,1137.982,1202.010,1243.108,1214.942],\n",
    "    [1226.605,1163.133,1215.157,1150.168,1271.996,1188.389,1150.340,1225.715,1173.816,1149.981],\n",
    "    [1149.914,1243.154,1162.278,1213.827,1188.393,1163.586,1162.122,1214.712,1202.093,1161.795],\n",
    "    [1174.286,1271.781,1187.727,1163.446,1175.565,1162.410,1227.913,1162.315,1149.859,1215.246],\n",
    "    [1160.960,1160.577,1271.780,1213.204,1150.013,1201.833,1187.925,1149.171,1161.714,1199.196],\n",
    "    [1240.405,1256.981,1173.234,1199.389,1149.362,1174.776,1214.991,1150.777,1161.456,1188.261],\n",
    "    [1149.884,1200.699,1243.273,1214.929,1138.596,1149.582,1186.668,1175.493,1149.133,1173.329],\n",
    "    [1149.904,1149.352,1198.889,1212.634,1148.979,1172.886,1149.590,1243.080,1188.089,1215.209],\n",
    "    [1136.452,1196.986,1147.314,1186.196,1197.783,1241.950,1147.295,1213.614,1170.812,1147.462],\n",
    "    [1214.999,1150.174,1188.195,1213.549,1200.016,1151.112,1149.678,1174.083,1242.948,1150.042]\n",
    "]\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "fig.canvas.set_window_title('NBody Benchmark Results')\n",
    "fig.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)\n",
    "\n",
    "\n",
    "bp = ax1.boxplot(data, notch=0, sym='', vert=1, whis=1.5)\n",
    "\n",
    "plt.setp(bp['boxes'], color='black')\n",
    "plt.setp(bp['whiskers'], color='black')\n",
    "plt.setp(bp['fliers'], color='red', marker='+')\n",
    "\n",
    "# Add a horizontal grid to the plot, but make it very light in color\n",
    "# so we can use it for reading data values but not be distracting\n",
    "ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5)\n",
    "\n",
    "# Hide these grid behind plot objects\n",
    "ax1.set_axisbelow(True)\n",
    "ax1.set_title('10 concurrent tasks sharing one GPU with MPS')\n",
    "ax1.set_xlabel('Experiments(media/avg/min/max)')\n",
    "ax1.set_ylabel('GFLOPs')\n",
    "\n",
    "\n",
    "# Now fill the boxes with desired colors\n",
    "box_color = 'royalblue'\n",
    "num_boxes = len(data)\n",
    "medians = np.empty(num_boxes)\n",
    "for i in range(num_boxes):\n",
    "    box = bp['boxes'][i]\n",
    "    boxX = []\n",
    "    boxY = []\n",
    "    for j in range(5):\n",
    "        boxX.append(box.get_xdata()[j])\n",
    "        boxY.append(box.get_ydata()[j])\n",
    "    box_coords = np.column_stack([boxX, boxY])\n",
    "    # Alternate between Dark Khaki and Royal Blue\n",
    "    ax1.add_patch(Polygon(box_coords, facecolor=box_color))\n",
    "    # Now draw the median lines back over what we just filled in\n",
    "    med = bp['medians'][i]\n",
    "    medianX = []\n",
    "    medianY = []\n",
    "    for j in range(2):\n",
    "        medianX.append(med.get_xdata()[j])\n",
    "        medianY.append(med.get_ydata()[j])\n",
    "        ax1.plot(medianX, medianY, 'k')\n",
    "    medians[i] = medianY[0]\n",
    "    # Finally, overplot the sample averages, with horizontal alignment\n",
    "    # in the center of each box\n",
    "    ax1.plot(np.average(med.get_xdata()), np.average(data[i]),\n",
    "             color='w', marker='*', markeredgecolor='k')\n",
    "\n",
    "# Set the axes ranges and axes labels\n",
    "ax1.set_xlim(0.5, num_boxes + 0.5)\n",
    "top = 1300\n",
    "bottom = 1100\n",
    "ax1.set_ylim(bottom, top)\n",
    "# ax1.set_xticklabels(np.repeat(random_dists, 2),\n",
    "#                     rotation=45, fontsize=8)\n",
    "\n",
    "\n",
    "# Due to the Y-axis scale being different across samples, it can be\n",
    "# hard to compare differences in medians across the samples. Add upper\n",
    "# X-axis tick labels with the sample medians to aid in comparison\n",
    "# (just use two decimal places of precision)\n",
    "pos = np.arange(num_boxes) + 1\n",
    "upper_labels = [str(np.round(s, 2)) for s in medians]\n",
    "weights = ['bold', 'semibold']\n",
    "for tick, label in zip(range(num_boxes), ax1.get_xticklabels()):\n",
    "    k = tick % 2\n",
    "    ax1.text(pos[tick], .95, upper_labels[tick],\n",
    "             transform=ax1.get_xaxis_transform(),\n",
    "             horizontalalignment='center',\n",
    "             size='x-small',\n",
    "             weight=weights[k],\n",
    "             color=box_color)\n",
    "    \n",
    "    \n",
    "# Finally, add a basic legend\n",
    "fig.text(0.80, 0.15, '10 experiments', backgroundcolor='lightblue', color='black', weight='roman', size='x-small')\n",
    "fig.text(0.80, 0.12, 'Tesla V100 W/ MPS', backgroundcolor='lightgreen', color='black', weight='roman', size='x-small')\n",
    "fig.text(0.80, 0.09, '*', color='white', backgroundcolor='silver', weight='roman', size='x-small')\n",
    "fig.text(0.815, 0.09, 'Average Value', color='black', weight='roman', size='x-small')\n",
    "fig.text(0.80, 0.06, '--', color='red', backgroundcolor='silver', weight='roman', size='x-small')\n",
    "fig.text(0.815, 0.06, 'Median Value', color='black', weight='roman', size='x-small')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### We need performance comparision for real world use case. Like CV or NLP serving examples using GPU\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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