{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>a1.metal</th>\n",
       "      <th>a1.4xlarge</th>\n",
       "      <th>c6g.4xlarge</th>\n",
       "      <th>c6g.16xlarge</th>\n",
       "      <th>c5a.16xlarge</th>\n",
       "      <th>Measurement</th>\n",
       "      <th>units</th>\n",
       "      <th>better</th>\n",
       "      <th>tyoe</th>\n",
       "      <th>notes</th>\n",
       "      <th>URL</th>\n",
       "      <th>a1.metal normalised</th>\n",
       "      <th>a1.metal/a1.4xlarge</th>\n",
       "      <th>a1.4xlarge normalised</th>\n",
       "      <th>c6g.4xlarge/a1.4xlarge</th>\n",
       "      <th>c5a.16xlarge normalised</th>\n",
       "      <th>c6g.16xlarge/c5a.16xlarge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>STREAM</th>\n",
       "      <td>29710.500</td>\n",
       "      <td>29630.800</td>\n",
       "      <td>174045.400</td>\n",
       "      <td>172772.400</td>\n",
       "      <td>89837.200</td>\n",
       "      <td>Bandwidth</td>\n",
       "      <td>MB/s</td>\n",
       "      <td>higher</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>Memory Bandwidth, \"Triad\" number</td>\n",
       "      <td>https://www.cs.virginia.edu/stream/</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.997317</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.873800</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.923172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TeaLeaf</th>\n",
       "      <td>2948.400</td>\n",
       "      <td>2962.250</td>\n",
       "      <td>1147.370</td>\n",
       "      <td>468.100</td>\n",
       "      <td>2239.030</td>\n",
       "      <td>Walltime</td>\n",
       "      <td>s</td>\n",
       "      <td>lower</td>\n",
       "      <td>mini-app</td>\n",
       "      <td>heat conduction</td>\n",
       "      <td>https://uk-mac.github.io/TeaLeaf/</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.995324</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.581774</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.783230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CloverLeaf</th>\n",
       "      <td>5989.710</td>\n",
       "      <td>6037.430</td>\n",
       "      <td>2455.530</td>\n",
       "      <td>730.380</td>\n",
       "      <td>863.570</td>\n",
       "      <td>Walltime</td>\n",
       "      <td>s</td>\n",
       "      <td>lower</td>\n",
       "      <td>mini-app</td>\n",
       "      <td>Lagrangian-Eulerian hydrodynamics</td>\n",
       "      <td>http://uk-mac.github.io/CloverLeaf/</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.992096</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.458707</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.182357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GROMACS</th>\n",
       "      <td>8.004</td>\n",
       "      <td>7.967</td>\n",
       "      <td>18.194</td>\n",
       "      <td>58.727</td>\n",
       "      <td>60.805</td>\n",
       "      <td>Throughput</td>\n",
       "      <td>ns/day</td>\n",
       "      <td>higher</td>\n",
       "      <td>Application</td>\n",
       "      <td>Molecular dynamics, 2018.5</td>\n",
       "      <td>http://www.gromacs.org/</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.995377</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.283670</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.965825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             a1.metal  a1.4xlarge  c6g.4xlarge  c6g.16xlarge  c5a.16xlarge  \\\n",
       "STREAM      29710.500   29630.800   174045.400    172772.400     89837.200   \n",
       "TeaLeaf      2948.400    2962.250     1147.370       468.100      2239.030   \n",
       "CloverLeaf   5989.710    6037.430     2455.530       730.380       863.570   \n",
       "GROMACS         8.004       7.967       18.194        58.727        60.805   \n",
       "\n",
       "           Measurement    units  better         tyoe  \\\n",
       "STREAM        Bandwidth    MB/s  higher    synthetic   \n",
       "TeaLeaf        Walltime       s   lower     mini-app   \n",
       "CloverLeaf     Walltime       s   lower     mini-app   \n",
       "GROMACS      Throughput  ns/day  higher  Application   \n",
       "\n",
       "                                        notes  \\\n",
       "STREAM       Memory Bandwidth, \"Triad\" number   \n",
       "TeaLeaf                       heat conduction   \n",
       "CloverLeaf  Lagrangian-Eulerian hydrodynamics   \n",
       "GROMACS            Molecular dynamics, 2018.5   \n",
       "\n",
       "                                            URL  a1.metal normalised  \\\n",
       "STREAM      https://www.cs.virginia.edu/stream/                  1.0   \n",
       "TeaLeaf       https://uk-mac.github.io/TeaLeaf/                  1.0   \n",
       "CloverLeaf  http://uk-mac.github.io/CloverLeaf/                  1.0   \n",
       "GROMACS                 http://www.gromacs.org/                  1.0   \n",
       "\n",
       "            a1.metal/a1.4xlarge  a1.4xlarge normalised  \\\n",
       "STREAM                 0.997317                    1.0   \n",
       "TeaLeaf                0.995324                    1.0   \n",
       "CloverLeaf             0.992096                    1.0   \n",
       "GROMACS                0.995377                    1.0   \n",
       "\n",
       "            c6g.4xlarge/a1.4xlarge  c5a.16xlarge normalised  \\\n",
       "STREAM                    5.873800                      1.0   \n",
       "TeaLeaf                   2.581774                      1.0   \n",
       "CloverLeaf                2.458707                      1.0   \n",
       "GROMACS                   2.283670                      1.0   \n",
       "\n",
       "            c6g.16xlarge/c5a.16xlarge  \n",
       "STREAM                       1.923172  \n",
       "TeaLeaf                      4.783230  \n",
       "CloverLeaf                   1.182357  \n",
       "GROMACS                      0.965825  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set_style(\"darkgrid\")\n",
    "\n",
    "raw = io.StringIO(\"\"\"\n",
    ",a1.metal,a1.4xlarge,c6g.4xlarge,c6g.16xlarge,c5a.16xlarge,Measurement ,units,better,tyoe,notes,URL\n",
    "STREAM,29710.5,29630.8,174045.4,172772.4,89837.2,Bandwidth,MB/s,higher,synthetic,\"Memory Bandwidth, \"\"Triad\"\" number\",https://www.cs.virginia.edu/stream/\n",
    "TeaLeaf,2948.4, 2962.25,1147.37,468.1,2239.03,Walltime,s,lower,mini-app,heat conduction,https://uk-mac.github.io/TeaLeaf/\n",
    "CloverLeaf,5989.71,6037.43,2455.53,730.38,863.570,Walltime,s,lower,mini-app,Lagrangian-Eulerian hydrodynamics,http://uk-mac.github.io/CloverLeaf/\n",
    "GROMACS,   8.004   , 7.967,18.194 , 58.727 ,60.805,Throughput,ns/day,higher,Application,\"Molecular dynamics, 2018.5\",http://www.gromacs.org/\"\"\")\n",
    "bm = pd.read_csv(raw, index_col=0)\n",
    "\n",
    "bm[\"a1.metal normalised\"] = bm[\"a1.metal\"] / bm[\"a1.metal\"]\n",
    "bm[\"a1.metal/a1.4xlarge\"] = bm[\"a1.metal\"] / bm[\"a1.4xlarge\"]\n",
    "bm.at[\"STREAM\", \"a1.metal/a1.4xlarge\"] = 1/bm[\"a1.metal/a1.4xlarge\"][\"STREAM\"]\n",
    "bm.at[\"GROMACS\", \"a1.metal/a1.4xlarge\"] = 1/bm[\"a1.metal/a1.4xlarge\"][\"GROMACS\"]\n",
    "\n",
    "bm[\"a1.4xlarge normalised\"] = bm[\"a1.4xlarge\"] / bm[\"a1.4xlarge\"]\n",
    "bm[\"c6g.4xlarge/a1.4xlarge\"] = bm[\"a1.4xlarge\"] / bm[\"c6g.4xlarge\"]\n",
    "bm.at[\"STREAM\", \"c6g.4xlarge/a1.4xlarge\"] = 1/bm[\"c6g.4xlarge/a1.4xlarge\"][\"STREAM\"]\n",
    "bm.at[\"GROMACS\", \"c6g.4xlarge/a1.4xlarge\"] = 1/bm[\"c6g.4xlarge/a1.4xlarge\"][\"GROMACS\"]\n",
    "\n",
    "bm[\"c5a.16xlarge normalised\"] = bm[\"c5a.16xlarge\"] / bm[\"c5a.16xlarge\"]\n",
    "bm[\"c6g.16xlarge/c5a.16xlarge\"] = bm[\"c5a.16xlarge\"] / bm[\"c6g.16xlarge\"]\n",
    "bm.at[\"STREAM\", \"c6g.16xlarge/c5a.16xlarge\"] = 1/bm[\"c6g.16xlarge/c5a.16xlarge\"][\"STREAM\"]\n",
    "bm.at[\"GROMACS\", \"c6g.16xlarge/c5a.16xlarge\"] = 1/bm[\"c6g.16xlarge/c5a.16xlarge\"][\"GROMACS\"]\n",
    "\n",
    "bm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(constrained_layout=True)\n",
    "bm[[\"a1.metal normalised\", \"a1.metal/a1.4xlarge\"]].plot.bar(ax=ax)\n",
    "ax.legend([\"Bare metal\", \"VM\"], loc=\"lower right\")\n",
    "ax.set_ylabel(\"Relative performance\")\n",
    "ax.figure.savefig(\"g1_bmvm.svg\")\n",
    "ax.figure.savefig(\"g1_bmvm.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(constrained_layout=True)\n",
    "bm[[\"a1.4xlarge normalised\", \"c6g.4xlarge/a1.4xlarge\"]].plot.bar(ax=ax)\n",
    "ax.legend([\"Graviton 1\", \"Graviton 2\"], loc=\"upper right\")\n",
    "ax.set_ylabel(\"Relative performance\")\n",
    "ax.figure.savefig(\"g1_g2.svg\")\n",
    "ax.figure.savefig(\"g1_g2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(constrained_layout=True)\n",
    "bm[[\"c5a.16xlarge normalised\", \"c6g.16xlarge/c5a.16xlarge\"]].plot.bar(ax=ax)\n",
    "ax.legend([\"AMD EPYC\", \"Graviton 2\"], loc=\"upper right\")\n",
    "ax.set_ylabel(\"Relative performance\")\n",
    "ax.figure.savefig(\"g2_amdepyc.svg\")\n",
    "ax.figure.savefig(\"g2_amdepyc.png\")"
   ]
  }
 ],
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