plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cycles</th>\n",
       "      <th>branchless</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0</td>\n",
       "      <td>1.63205</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>1.62193</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2</td>\n",
       "      <td>1.64844</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>3</td>\n",
       "      <td>1.62628</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>4</td>\n",
       "      <td>1.62825</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>96</td>\n",
       "      <td>1.62944</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>97</td>\n",
       "      <td>1.64676</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>98</td>\n",
       "      <td>1.63615</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>99</td>\n",
       "      <td>1.63444</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>100</td>\n",
       "      <td>1.63533</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     probability   cycles  branchless\n",
       "101            0  1.63205           1\n",
       "102            1  1.62193           1\n",
       "103            2  1.64844           1\n",
       "104            3  1.62628           1\n",
       "105            4  1.62825           1\n",
       "..           ...      ...         ...\n",
       "197           96  1.62944           1\n",
       "198           97  1.64676           1\n",
       "199           98  1.63615           1\n",
       "200           99  1.63444           1\n",
       "201          100  1.63533           1\n",
       "\n",
       "[101 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch = pd.read_csv('data/branch.csv')\n",
    "branchless = branch[branch['branchless'] == 1]\n",
    "branch = branch[branch['branchless'] == 0]\n",
    "branchless"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = list(branch['probability'])\n",
    "y = list(branch['cycles'])\n",
    "# plt.xticks(x)\n",
    "plt.plot(x, y)\n",
    "plt.xlabel('Branching probability')\n",
    "plt.ylabel('Clock cycles per iteration')\n",
    "plt.title(\"Performance penalty of branching on CPUs\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = list(branch['probability'])\n",
    "y = list(branch['cycles'])\n",
    "# plt.xticks(x)\n",
    "plt.plot(x, y, label='Branching')\n",
    "\n",
    "y = list(branchless['cycles'])\n",
    "plt.plot(x, y, label='Branchless')\n",
    "plt.xlabel('Branching probability')\n",
    "plt.ylabel('Clock cycles per iteration')\n",
    "plt.title(\"Branching vs. branchless performance on CPU\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('branchless.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>cpu_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>memory_latency_list/size KB:1</td>\n",
       "      <td>1.88726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>memory_latency_list/size KB:2</td>\n",
       "      <td>1.88480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>memory_latency_list/size KB:4</td>\n",
       "      <td>1.88496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>memory_latency_list/size KB:8</td>\n",
       "      <td>3.80355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>memory_latency_list/size KB:16</td>\n",
       "      <td>3.80349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>memory_latency_list/size KB:32</td>\n",
       "      <td>3.79045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>memory_latency_list/size KB:64</td>\n",
       "      <td>3.79570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>memory_latency_list/size KB:128</td>\n",
       "      <td>6.19455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>memory_latency_list/size KB:256</td>\n",
       "      <td>11.89620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>memory_latency_list/size KB:512</td>\n",
       "      <td>17.64150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>memory_latency_list/size KB:1024</td>\n",
       "      <td>21.65930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>memory_latency_list/size KB:2048</td>\n",
       "      <td>25.12390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>memory_latency_list/size KB:4096</td>\n",
       "      <td>27.47280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>memory_latency_list/size KB:8192</td>\n",
       "      <td>28.97490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>memory_latency_list/size KB:16384</td>\n",
       "      <td>32.40930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>memory_latency_list/size KB:32768</td>\n",
       "      <td>67.69970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>memory_latency_list/size KB:65536</td>\n",
       "      <td>90.05500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>memory_latency_list/size KB:131072</td>\n",
       "      <td>100.47100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>memory_latency_list/size KB:262144</td>\n",
       "      <td>148.46800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>memory_latency_list/size KB:524288</td>\n",
       "      <td>173.66900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>memory_latency_list/size KB:1048576</td>\n",
       "      <td>192.43400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>memory_latency_list/size KB:2097152</td>\n",
       "      <td>201.02300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>memory_latency_list/size KB:4194304</td>\n",
       "      <td>204.96200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   name   cpu_time\n",
       "0         memory_latency_list/size KB:1    1.88726\n",
       "1         memory_latency_list/size KB:2    1.88480\n",
       "2         memory_latency_list/size KB:4    1.88496\n",
       "3         memory_latency_list/size KB:8    3.80355\n",
       "4        memory_latency_list/size KB:16    3.80349\n",
       "5        memory_latency_list/size KB:32    3.79045\n",
       "6        memory_latency_list/size KB:64    3.79570\n",
       "7       memory_latency_list/size KB:128    6.19455\n",
       "8       memory_latency_list/size KB:256   11.89620\n",
       "9       memory_latency_list/size KB:512   17.64150\n",
       "10     memory_latency_list/size KB:1024   21.65930\n",
       "11     memory_latency_list/size KB:2048   25.12390\n",
       "12     memory_latency_list/size KB:4096   27.47280\n",
       "13     memory_latency_list/size KB:8192   28.97490\n",
       "14    memory_latency_list/size KB:16384   32.40930\n",
       "15    memory_latency_list/size KB:32768   67.69970\n",
       "16    memory_latency_list/size KB:65536   90.05500\n",
       "17   memory_latency_list/size KB:131072  100.47100\n",
       "18   memory_latency_list/size KB:262144  148.46800\n",
       "19   memory_latency_list/size KB:524288  173.66900\n",
       "20  memory_latency_list/size KB:1048576  192.43400\n",
       "21  memory_latency_list/size KB:2097152  201.02300\n",
       "22  memory_latency_list/size KB:4194304  204.96200"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latency = pd.read_csv('data/latency.csv')\n",
    "latency = latency.drop(columns=['iterations', 'real_time', 'bytes_per_second', 'items_per_second',\n",
    "    'label', 'error_occurred', 'error_message', 'Nodes', 'Read Rate', 'Size', 'time_unit'])\n",
    "latency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nameToKB(name):\n",
    "    return int(name.split(':')[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpu_time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.88726</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.88480</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.88496</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.80355</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.80349</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.79045</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.79570</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.19455</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.89620</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17.64150</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21.65930</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25.12390</td>\n",
       "      <td>2048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27.47280</td>\n",
       "      <td>4096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>28.97490</td>\n",
       "      <td>8192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>32.40930</td>\n",
       "      <td>16384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>67.69970</td>\n",
       "      <td>32768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>90.05500</td>\n",
       "      <td>65536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100.47100</td>\n",
       "      <td>131072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>148.46800</td>\n",
       "      <td>262144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>173.66900</td>\n",
       "      <td>524288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>192.43400</td>\n",
       "      <td>1048576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>201.02300</td>\n",
       "      <td>2097152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>204.96200</td>\n",
       "      <td>4194304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cpu_time     size\n",
       "0     1.88726        1\n",
       "1     1.88480        2\n",
       "2     1.88496        4\n",
       "3     3.80355        8\n",
       "4     3.80349       16\n",
       "5     3.79045       32\n",
       "6     3.79570       64\n",
       "7     6.19455      128\n",
       "8    11.89620      256\n",
       "9    17.64150      512\n",
       "10   21.65930     1024\n",
       "11   25.12390     2048\n",
       "12   27.47280     4096\n",
       "13   28.97490     8192\n",
       "14   32.40930    16384\n",
       "15   67.69970    32768\n",
       "16   90.05500    65536\n",
       "17  100.47100   131072\n",
       "18  148.46800   262144\n",
       "19  173.66900   524288\n",
       "20  192.43400  1048576\n",
       "21  201.02300  2097152\n",
       "22  204.96200  4194304"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latency['size'] = list(map(nameToKB, latency['name']))\n",
    "latency = latency.drop(columns=['name'])\n",
    "latency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = list(latency['size']) \n",
    "y = list(latency['cpu_time'])\n",
    "plt.xticks(x)\n",
    "plt.plot(x, y, label='Read latency')\n",
    "plt.xlabel('Array size (KB)')\n",
    "plt.ylabel('Read latency (ns)')\n",
    "plt.xscale('log', base=2)\n",
    "plt.title(\"Stochastic read latency on CPUs\")\n",
    "plt.grid(True)\n",
    "# plt.legend()s\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "c261aea317cc0286b3b3261fbba9abdec21eaa57589985bb7a274bf54d6cc0a7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}