01.basic-benchmarks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fbb77a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0608424d",
   "metadata": {},
   "outputs": [
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       "           s    h:m:s   max_rss   max_vms   max_uss   max_pss  io_in  io_out  \\\n",
       "0  1347.0233  0:22:27  12910.21  15135.63  13073.72  13073.72   0.01    0.14   \n",
       "\n",
       "   mean_load  cpu_time  n_query  n_against  \n",
       "0     2762.7  37225.36      100      10000  "
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_benchmark_and_annotate(filename, n_query, n_against):\n",
    "    df = pd.read_csv(filename, sep='\\t')\n",
    "    df['n_query'] = int(n_query)\n",
    "    df['n_against'] = int(n_against)\n",
    "    \n",
    "    return df\n",
    "\n",
    "load_benchmark_and_annotate('benchmarks/a_100_vs_a.txt', 100, 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0bc09f7e",
   "metadata": {},
   "outputs": [
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       "      <td>500</td>\n",
       "      <td>10000</td>\n",
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       "      <th>0</th>\n",
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       "      <td>17888.35</td>\n",
       "      <td>17888.35</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2755.67</td>\n",
       "      <td>46753.81</td>\n",
       "      <td>600</td>\n",
       "      <td>10000</td>\n",
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       "      <th>0</th>\n",
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       "      <td>700</td>\n",
       "      <td>10000</td>\n",
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       "      <th>0</th>\n",
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       "      <td>0.97</td>\n",
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       "           s    h:m:s   max_rss   max_vms   max_uss   max_pss     io_in  \\\n",
       "0  1347.0233  0:22:27  12910.21  15135.63  13073.72  13073.72      0.01   \n",
       "0  1291.4875  0:21:31  14541.96  18260.40  14601.04  14601.05      0.01   \n",
       "0  1454.4474  0:24:14  16348.15  18954.26  16429.44  16429.44      0.02   \n",
       "0  1551.7362  0:25:51  14438.77  16864.46  14666.07  14666.08      0.02   \n",
       "0  1603.6622  0:26:43  18893.50  21000.83  18968.33  18968.33      0.03   \n",
       "0  1696.6006  0:28:16  17783.73  19868.45  17888.35  17888.35      0.03   \n",
       "0  1610.4366  0:26:50  16855.73  19042.42  16989.04  16989.04      0.04   \n",
       "0  1770.2948  0:29:30  16316.45  19000.11  16440.92  16440.92      0.04   \n",
       "0  1806.3763  0:30:06  16779.61  19569.78  16286.82  16286.82  95967.58   \n",
       "\n",
       "   io_out  mean_load  cpu_time  n_query  n_against  \n",
       "0    0.14    2762.70  37225.36      100      10000  \n",
       "0    0.24    3044.57  39323.17      200      10000  \n",
       "0    0.31    2828.24  41136.09      300      10000  \n",
       "0    0.40    2751.87  42702.26      400      10000  \n",
       "0    0.48    2802.76  44947.46      500      10000  \n",
       "0    0.59    2755.67  46753.81      600      10000  \n",
       "0    0.70    3032.36  48835.22      700      10000  \n",
       "0    0.85    2863.60  50694.71      800      10000  \n",
       "0    0.97    2889.70  52198.87      900      10000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_line = [ load_benchmark_and_annotate(f\"benchmarks/a_{x}_vs_a.txt\", x, 10000) for x in range(100, 1000, 100) ]\n",
    "#query_line.append(load_benchmark_and_annotate('benchmarks/a_vs_a.txt', 1000, 10000))\n",
    "query_line_df = pd.concat(query_line)\n",
    "query_line_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f69c666a",
   "metadata": {},
   "outputs": [
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       "      <td>15028.22</td>\n",
       "      <td>15028.30</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2722.32</td>\n",
       "      <td>19182.34</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
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       "      <td>0:17:53</td>\n",
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       "      <td>14048.74</td>\n",
       "      <td>12201.99</td>\n",
       "      <td>12201.99</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2635.62</td>\n",
       "      <td>28288.41</td>\n",
       "      <td>1000</td>\n",
       "      <td>6000</td>\n",
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       "      <td>1269.1121</td>\n",
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       "      <td>17586.37</td>\n",
       "      <td>15507.43</td>\n",
       "      <td>15507.43</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.79</td>\n",
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       "      <td>37104.07</td>\n",
       "      <td>1000</td>\n",
       "      <td>7000</td>\n",
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       "      <th>0</th>\n",
       "      <td>1419.2742</td>\n",
       "      <td>0:23:39</td>\n",
       "      <td>16026.34</td>\n",
       "      <td>18511.11</td>\n",
       "      <td>16068.09</td>\n",
       "      <td>16068.09</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3018.64</td>\n",
       "      <td>42844.40</td>\n",
       "      <td>1000</td>\n",
       "      <td>8000</td>\n",
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       "      <th>0</th>\n",
       "      <td>1576.3652</td>\n",
       "      <td>0:26:16</td>\n",
       "      <td>16715.54</td>\n",
       "      <td>19374.40</td>\n",
       "      <td>16759.83</td>\n",
       "      <td>16759.83</td>\n",
       "      <td>7.45</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3052.70</td>\n",
       "      <td>48122.82</td>\n",
       "      <td>1000</td>\n",
       "      <td>9000</td>\n",
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       "           s    h:m:s   max_rss   max_vms   max_uss   max_pss  io_in  io_out  \\\n",
       "0   154.1568  0:02:34   8026.29   8707.47   8042.18   8042.25   0.07    0.11   \n",
       "0   269.0769  0:04:29   8977.02   9920.71   8961.81   8961.88   0.13    0.25   \n",
       "0   354.2737  0:05:54  11786.54  13038.87  11863.02  11863.11   0.20    0.36   \n",
       "0   478.0915  0:07:58  13964.38  15485.38  14074.45  14074.53   0.26    0.48   \n",
       "0   704.6193  0:11:44  15294.20  18001.37  15028.22  15028.30   0.33    0.64   \n",
       "0  1073.2904  0:17:53  12150.98  14048.74  12201.99  12201.99   0.39    0.73   \n",
       "0  1269.1121  0:21:09  15887.25  17586.37  15507.43  15507.43   0.46    0.79   \n",
       "0  1419.2742  0:23:39  16026.34  18511.11  16068.09  16068.09   0.52    0.89   \n",
       "0  1576.3652  0:26:16  16715.54  19374.40  16759.83  16759.83   7.45    1.00   \n",
       "\n",
       "   mean_load  cpu_time  n_query  n_against  \n",
       "0    2112.52   3256.75     1000       1000  \n",
       "0    2450.90   6594.63     1000       2000  \n",
       "0    2943.38  10428.58     1000       3000  \n",
       "0    3003.24  14358.91     1000       4000  \n",
       "0    2722.32  19182.34     1000       5000  \n",
       "0    2635.62  28288.41     1000       6000  \n",
       "0    2923.52  37104.07     1000       7000  \n",
       "0    3018.64  42844.40     1000       8000  \n",
       "0    3052.70  48122.82     1000       9000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "against_line = [ load_benchmark_and_annotate(f\"benchmarks/a_vs_a_{x}.txt\", 1000, x) for x in range(1000, 10000, 1000) ]\n",
    "#against_line.append(load_benchmark_and_annotate('benchmarks/a_vs_a.txt', 1000, 10000))\n",
    "against_line_df = pd.concat(against_line)\n",
    "against_line_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "850d18f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig = plt.figure()\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "ax = axs[0]\n",
    "ax.plot(query_line_df.n_query, query_line_df['s'] / 60, '-p')\n",
    "ax.axis(xmin=0, ymin=0, ymax=35)\n",
    "ax.set_xlabel('num queries')\n",
    "ax.set_ylabel('walltime (m)')\n",
    "ax.set_title('varying number of queries (10k subjects)')\n",
    "\n",
    "ax = axs[1]\n",
    "ax.plot(against_line_df.n_against, against_line_df['s'] / 60, '-p')\n",
    "ax.axis(xmin=0, ymin=0, ymax=35)\n",
    "ax.set_xlabel('num subject datasets')\n",
    "ax.set_ylabel('walltime (m)')\n",
    "ax.set_title('varying number of subjects (1000 queries)')\n",
    "\n",
    "plt.savefig('subsample_benchmarks.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f11cf360",
   "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>s</th>\n",
       "      <th>h:m:s</th>\n",
       "      <th>max_rss</th>\n",
       "      <th>max_vms</th>\n",
       "      <th>max_uss</th>\n",
       "      <th>max_pss</th>\n",
       "      <th>io_in</th>\n",
       "      <th>io_out</th>\n",
       "      <th>mean_load</th>\n",
       "      <th>cpu_time</th>\n",
       "      <th>ident</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1394.1251</td>\n",
       "      <td>0:23:14</td>\n",
       "      <td>16106.77</td>\n",
       "      <td>18410.19</td>\n",
       "      <td>16167.91</td>\n",
       "      <td>16167.94</td>\n",
       "      <td>95974.41</td>\n",
       "      <td>1.14</td>\n",
       "      <td>2914.11</td>\n",
       "      <td>40626.79</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1394.1750</td>\n",
       "      <td>0:23:14</td>\n",
       "      <td>17553.49</td>\n",
       "      <td>20057.43</td>\n",
       "      <td>17685.95</td>\n",
       "      <td>17685.99</td>\n",
       "      <td>94003.57</td>\n",
       "      <td>1.16</td>\n",
       "      <td>2771.80</td>\n",
       "      <td>38643.81</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1357.7237</td>\n",
       "      <td>0:22:37</td>\n",
       "      <td>18257.87</td>\n",
       "      <td>20041.38</td>\n",
       "      <td>18277.77</td>\n",
       "      <td>18277.80</td>\n",
       "      <td>91280.15</td>\n",
       "      <td>1.01</td>\n",
       "      <td>2794.07</td>\n",
       "      <td>37936.37</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1505.0091</td>\n",
       "      <td>0:25:05</td>\n",
       "      <td>16107.48</td>\n",
       "      <td>19187.17</td>\n",
       "      <td>16137.28</td>\n",
       "      <td>16137.32</td>\n",
       "      <td>93738.74</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2605.24</td>\n",
       "      <td>39209.39</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1596.4828</td>\n",
       "      <td>0:26:36</td>\n",
       "      <td>14046.44</td>\n",
       "      <td>15868.38</td>\n",
       "      <td>14061.43</td>\n",
       "      <td>14061.46</td>\n",
       "      <td>91948.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>2367.13</td>\n",
       "      <td>37791.57</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           s    h:m:s   max_rss   max_vms   max_uss   max_pss     io_in  \\\n",
       "0  1394.1251  0:23:14  16106.77  18410.19  16167.91  16167.94  95974.41   \n",
       "0  1394.1750  0:23:14  17553.49  20057.43  17685.95  17685.99  94003.57   \n",
       "0  1357.7237  0:22:37  18257.87  20041.38  18277.77  18277.80  91280.15   \n",
       "0  1505.0091  0:25:05  16107.48  19187.17  16137.28  16137.32  93738.74   \n",
       "0  1596.4828  0:26:36  14046.44  15868.38  14061.43  14061.46  91948.11   \n",
       "\n",
       "   io_out  mean_load  cpu_time ident  \n",
       "0    1.14    2914.11  40626.79     a  \n",
       "0    1.16    2771.80  38643.81     b  \n",
       "0    1.01    2794.07  37936.37     c  \n",
       "0    1.02    2605.24  39209.39     d  \n",
       "0    0.96    2367.13  37791.57     e  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_benchmark_replicate(filename, ident):\n",
    "    df = pd.read_csv(filename, sep='\\t')\n",
    "    df['ident'] = ident\n",
    "    \n",
    "    return df\n",
    "\n",
    "replicates_df = [ load_benchmark_replicate(f\"benchmarks/a_vs_{x}.txt\", x) for x in ('a','b','c','d','e')]\n",
    "#query_line.append(load_benchmark_and_annotate('benchmarks/a_vs_a.txt', 1000, 10000))\n",
    "replicates_df = pd.concat(replicates_df)\n",
    "replicates_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b8dbec83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean (minutes) 24.158385666666664, std 1.650659128576706\n"
     ]
    },
    {
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.boxplot(replicates_df.s)\n",
    "ax.set_title('time (s)')\n",
    "\n",
    "replicates_df['m'] = replicates_df['s'] / 60.0\n",
    "print(f\"mean (minutes) {replicates_df.m.mean()}, std {replicates_df.m.std()}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8cb4d0cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean 16414.41, std 1619.5661713403376\n"
     ]
    },
    {
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.boxplot(replicates_df.max_rss)\n",
    "ax.set_title('max RSS (mb)')\n",
    "\n",
    "print(f\"mean {replicates_df.max_rss.mean()}, std {replicates_df.max_rss.std()}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0f4a6b97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean 93388.996, std 1851.2003899578276\n"
     ]
    },
    {
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.boxplot(replicates_df.io_in)\n",
    "ax.set_title('I/O in (mb)')\n",
    "\n",
    "print(f\"mean {replicates_df.io_in.mean()}, std {replicates_df.io_in.std()}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d6d0b779",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_threads_benchmark_and_annotate(filename, n_threads):\n",
    "    df = pd.read_csv(filename.format(threads=n_threads), sep='\\t')\n",
    "    df['threads'] = n_threads\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "x = [ load_threads_benchmark_and_annotate(f'benchmarks/a_vs_a_1000_t{t}.txt', t) for t in (4,8,16) ]\n",
    "x.append(load_threads_benchmark_and_annotate(f'benchmarks/a_vs_a_1000.txt', 32))\n",
    "\n",
    "threads_df = pd.concat(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1722f685",
   "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>s</th>\n",
       "      <th>h:m:s</th>\n",
       "      <th>max_rss</th>\n",
       "      <th>max_vms</th>\n",
       "      <th>max_uss</th>\n",
       "      <th>max_pss</th>\n",
       "      <th>io_in</th>\n",
       "      <th>io_out</th>\n",
       "      <th>mean_load</th>\n",
       "      <th>cpu_time</th>\n",
       "      <th>threads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>814.4291</td>\n",
       "      <td>0:13:34</td>\n",
       "      <td>2246.58</td>\n",
       "      <td>2380.33</td>\n",
       "      <td>2243.58</td>\n",
       "      <td>2244.08</td>\n",
       "      <td>89.14</td>\n",
       "      <td>0.11</td>\n",
       "      <td>341.55</td>\n",
       "      <td>2782.05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>484.0369</td>\n",
       "      <td>0:08:04</td>\n",
       "      <td>2661.74</td>\n",
       "      <td>3352.14</td>\n",
       "      <td>2661.75</td>\n",
       "      <td>2662.25</td>\n",
       "      <td>1189.09</td>\n",
       "      <td>0.11</td>\n",
       "      <td>649.08</td>\n",
       "      <td>3142.19</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>329.2121</td>\n",
       "      <td>0:05:29</td>\n",
       "      <td>6144.40</td>\n",
       "      <td>7038.87</td>\n",
       "      <td>6169.71</td>\n",
       "      <td>6170.21</td>\n",
       "      <td>7404.52</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1137.80</td>\n",
       "      <td>3746.52</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>154.1568</td>\n",
       "      <td>0:02:34</td>\n",
       "      <td>8026.29</td>\n",
       "      <td>8707.47</td>\n",
       "      <td>8042.18</td>\n",
       "      <td>8042.25</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.11</td>\n",
       "      <td>2112.52</td>\n",
       "      <td>3256.75</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          s    h:m:s  max_rss  max_vms  max_uss  max_pss    io_in  io_out  \\\n",
       "0  814.4291  0:13:34  2246.58  2380.33  2243.58  2244.08    89.14    0.11   \n",
       "0  484.0369  0:08:04  2661.74  3352.14  2661.75  2662.25  1189.09    0.11   \n",
       "0  329.2121  0:05:29  6144.40  7038.87  6169.71  6170.21  7404.52    0.10   \n",
       "0  154.1568  0:02:34  8026.29  8707.47  8042.18  8042.25     0.07    0.11   \n",
       "\n",
       "   mean_load  cpu_time  threads  \n",
       "0     341.55   2782.05        4  \n",
       "0     649.08   3142.19        8  \n",
       "0    1137.80   3746.52       16  \n",
       "0    2112.52   3256.75       32  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threads_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8a522d68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig = plt.figure()\n",
    "fig, axs = plt.subplots(1, 3, figsize=(18, 4))\n",
    "\n",
    "ax = axs[0]\n",
    "ax.plot(threads_df.threads, threads_df.s, '-p', label='observed times')\n",
    "ax.plot([4, 8, 16, 32], [484 * 2, 484, 484 / 2, 484 / 4], '--', label='perfect scaling')\n",
    "ax.axis(xmin=0, ymin=0)\n",
    "ax.set_xlabel('num threads')\n",
    "ax.set_ylabel('walltime (s)')\n",
    "ax.set_title('walltime')\n",
    "ax.legend(loc='upper right')\n",
    "\n",
    "ax = axs[1]\n",
    "ax.plot(threads_df.threads, threads_df.cpu_time, '-p')\n",
    "ax.axis(xmin=0, ymin=0, ymax=4000)\n",
    "ax.set_xlabel('num threads')\n",
    "ax.set_ylabel('total CPU time (s)')\n",
    "ax.set_title('total compute')\n",
    "\n",
    "ax = axs[2]\n",
    "ax.plot(threads_df.threads, threads_df.max_rss, '-p')\n",
    "ax.axis(xmin=0, ymin=0)\n",
    "ax.set_xlabel('num threads')\n",
    "ax.set_ylabel('max RSS (GB)')\n",
    "ax.set_title('memory usage')\n",
    "\n",
    "plt.savefig('basic_benchmarks.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a413afd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}