From 4be6eea36bb38daa31d5016a6834db44f0311ba8 Mon Sep 17 00:00:00 2001
From: JiaweiZhuang <jiaweizhuang@g.harvard.edu>
Date: Fri, 13 Dec 2019 20:46:24 -0500
Subject: [PATCH] Plot multi-pair pt2pt bandwidth

---
 notebooks/osu_util.py       |    2 +-
 notebooks/pt2pt_multi.ipynb | 1120 +++++++++++++++++++++++++++++++++++
 2 files changed, 1121 insertions(+), 1 deletion(-)
 create mode 100644 notebooks/pt2pt_multi.ipynb

diff --git a/notebooks/osu_util.py b/notebooks/osu_util.py
index 230b9ed..bdf5b6a 100644
--- a/notebooks/osu_util.py
+++ b/notebooks/osu_util.py
@@ -12,7 +12,7 @@ def read_osu_log(filename, skiprows=3):
         filename, index_col=0, squeeze=True, names=['size', None],
         delim_whitespace=True, skiprows=skiprows, header=None
     )
-    return df
+    return df.iloc[:,:1]  # only keep two columns
 
 
 def read_osu_log_multi(filename_list, columns=None, **kwargs):
diff --git a/notebooks/pt2pt_multi.ipynb b/notebooks/pt2pt_multi.ipynb
new file mode 100644
index 0000000..43732fb
--- /dev/null
+++ b/notebooks/pt2pt_multi.ipynb
@@ -0,0 +1,1120 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from osu_util import read_osu_log, read_osu_log_multi, plot_osu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[34mrun1\u001b[m\u001b[m/  \u001b[1m\u001b[34mrun10\u001b[m\u001b[m/ \u001b[1m\u001b[34mrun2\u001b[m\u001b[m/  \u001b[1m\u001b[34mrun3\u001b[m\u001b[m/  \u001b[1m\u001b[34mrun4\u001b[m\u001b[m/  \u001b[1m\u001b[34mrun5\u001b[m\u001b[m/  \u001b[1m\u001b[34mrun6\u001b[m\u001b[m/  \u001b[1m\u001b[34mrun7\u001b[m\u001b[m/  \u001b[1m\u001b[34mrun8\u001b[m\u001b[m/  \u001b[1m\u001b[34mrun9\u001b[m\u001b[m/\n"
+     ]
+    }
+   ],
+   "source": [
+    "ls osu_log_addon/pt2pt_multi/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[34mintelmpi-efa\u001b[m\u001b[m/ \u001b[1m\u001b[34mopenmpi3\u001b[m\u001b[m/     \u001b[1m\u001b[34mopenmpi4\u001b[m\u001b[m/\n",
+      "\u001b[1m\u001b[34mintelmpi-tcp\u001b[m\u001b[m/ \u001b[1m\u001b[34mopenmpi3-efa\u001b[m\u001b[m/ \u001b[1m\u001b[34mopenmpi4-efa\u001b[m\u001b[m/\n"
+     ]
+    }
+   ],
+   "source": [
+    "ls osu_log_addon/pt2pt_multi/run1/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_osu_all_runs(suffix, prefix='osu_log_addon/pt2pt_multi/', runs=10):\n",
+    "    file_list = [\n",
+    "        prefix + 'run{}'.format(i) + suffix for i in range(1, 10)  # skip last unfinished one\n",
+    "    ]\n",
+    "    df = read_osu_log_multi(file_list, skiprows=4)\n",
+    "    return df\n",
+    "\n",
+    "def stats(df):\n",
+    "    # similar to df.T.describe().T\n",
+    "    return df.apply(['mean', 'std', 'median', 'min', 'max'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mbw_impi_efa = read_osu_all_runs('/intelmpi-efa/mbw_mr.log')\n",
+    "mbw_impi_tcp = read_osu_all_runs('/intelmpi-tcp/mbw_mr.log')\n",
+    "mbw_ompi3 = read_osu_all_runs('/openmpi3/mbw_mr.log')\n",
+    "mbw_ompi3_efa = read_osu_all_runs('/openmpi3-efa/mbw_mr.log')\n",
+    "mbw_ompi4 = read_osu_all_runs('/openmpi4/mbw_mr.log')\n",
+    "mbw_ompi4_efa = read_osu_all_runs('/openmpi4-efa/mbw_mr.log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>IntelMPI-EFA</th>\n",
+       "      <th>IntelMPI-TCP</th>\n",
+       "      <th>OpenMPI3-TCP</th>\n",
+       "      <th>OpenMPI3-EFA</th>\n",
+       "      <th>OpenMPI4-TCP</th>\n",
+       "      <th>OpenMPI4-EFA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.164444</td>\n",
+       "      <td>4.970000</td>\n",
+       "      <td>8.911111</td>\n",
+       "      <td>9.023333</td>\n",
+       "      <td>10.005556</td>\n",
+       "      <td>9.563333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11.457778</td>\n",
+       "      <td>10.023333</td>\n",
+       "      <td>18.077778</td>\n",
+       "      <td>18.563333</td>\n",
+       "      <td>20.063333</td>\n",
+       "      <td>19.184444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>22.593333</td>\n",
+       "      <td>19.992222</td>\n",
+       "      <td>36.776667</td>\n",
+       "      <td>37.886667</td>\n",
+       "      <td>40.155556</td>\n",
+       "      <td>38.270000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>45.155556</td>\n",
+       "      <td>39.776667</td>\n",
+       "      <td>73.923333</td>\n",
+       "      <td>75.977778</td>\n",
+       "      <td>80.282222</td>\n",
+       "      <td>76.284444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>89.457778</td>\n",
+       "      <td>79.224444</td>\n",
+       "      <td>146.993333</td>\n",
+       "      <td>153.911111</td>\n",
+       "      <td>159.472222</td>\n",
+       "      <td>151.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>179.594444</td>\n",
+       "      <td>156.704444</td>\n",
+       "      <td>292.995556</td>\n",
+       "      <td>306.154444</td>\n",
+       "      <td>315.362222</td>\n",
+       "      <td>299.974444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>358.608889</td>\n",
+       "      <td>311.397778</td>\n",
+       "      <td>552.378889</td>\n",
+       "      <td>589.117778</td>\n",
+       "      <td>604.218889</td>\n",
+       "      <td>575.516667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>707.318889</td>\n",
+       "      <td>613.613333</td>\n",
+       "      <td>1066.624444</td>\n",
+       "      <td>1125.285556</td>\n",
+       "      <td>1157.770000</td>\n",
+       "      <td>1101.623333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>1388.386667</td>\n",
+       "      <td>772.440000</td>\n",
+       "      <td>2038.815556</td>\n",
+       "      <td>2173.053333</td>\n",
+       "      <td>2229.157778</td>\n",
+       "      <td>2128.963333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>2651.736667</td>\n",
+       "      <td>1515.818889</td>\n",
+       "      <td>3721.644444</td>\n",
+       "      <td>4014.838889</td>\n",
+       "      <td>4094.004444</td>\n",
+       "      <td>3917.944444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1024</th>\n",
+       "      <td>5216.915556</td>\n",
+       "      <td>2168.664444</td>\n",
+       "      <td>6373.132222</td>\n",
+       "      <td>6704.161111</td>\n",
+       "      <td>6878.360000</td>\n",
+       "      <td>6718.307778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2048</th>\n",
+       "      <td>9519.555556</td>\n",
+       "      <td>4175.991111</td>\n",
+       "      <td>9017.337778</td>\n",
+       "      <td>9474.734444</td>\n",
+       "      <td>9402.597778</td>\n",
+       "      <td>9489.707778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4096</th>\n",
+       "      <td>11619.321111</td>\n",
+       "      <td>7478.794444</td>\n",
+       "      <td>10420.471111</td>\n",
+       "      <td>10722.880000</td>\n",
+       "      <td>10469.040000</td>\n",
+       "      <td>10667.295556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8192</th>\n",
+       "      <td>12001.058889</td>\n",
+       "      <td>10466.297778</td>\n",
+       "      <td>11248.557778</td>\n",
+       "      <td>11197.362222</td>\n",
+       "      <td>10712.904444</td>\n",
+       "      <td>11105.977778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16384</th>\n",
+       "      <td>11878.734444</td>\n",
+       "      <td>11587.234444</td>\n",
+       "      <td>11632.650000</td>\n",
+       "      <td>11659.351111</td>\n",
+       "      <td>11436.592222</td>\n",
+       "      <td>11549.248889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32768</th>\n",
+       "      <td>11828.903333</td>\n",
+       "      <td>11856.432222</td>\n",
+       "      <td>11868.671111</td>\n",
+       "      <td>11873.036667</td>\n",
+       "      <td>11463.015556</td>\n",
+       "      <td>11537.782222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65536</th>\n",
+       "      <td>11840.230000</td>\n",
+       "      <td>11986.157778</td>\n",
+       "      <td>11860.595556</td>\n",
+       "      <td>11901.541111</td>\n",
+       "      <td>11350.562222</td>\n",
+       "      <td>11561.130000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131072</th>\n",
+       "      <td>11915.444444</td>\n",
+       "      <td>12015.871111</td>\n",
+       "      <td>11950.623333</td>\n",
+       "      <td>11817.281111</td>\n",
+       "      <td>11373.892222</td>\n",
+       "      <td>11511.865556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262144</th>\n",
+       "      <td>11959.908889</td>\n",
+       "      <td>12068.712222</td>\n",
+       "      <td>12102.635556</td>\n",
+       "      <td>11877.886667</td>\n",
+       "      <td>11342.281111</td>\n",
+       "      <td>11567.481111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524288</th>\n",
+       "      <td>12017.102222</td>\n",
+       "      <td>12078.778889</td>\n",
+       "      <td>12134.346667</td>\n",
+       "      <td>11887.037778</td>\n",
+       "      <td>11398.695556</td>\n",
+       "      <td>11617.685556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1048576</th>\n",
+       "      <td>12052.054444</td>\n",
+       "      <td>12097.413333</td>\n",
+       "      <td>12132.722222</td>\n",
+       "      <td>11897.421111</td>\n",
+       "      <td>11383.303333</td>\n",
+       "      <td>11639.371111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2097152</th>\n",
+       "      <td>12109.692222</td>\n",
+       "      <td>12096.722222</td>\n",
+       "      <td>12125.087778</td>\n",
+       "      <td>11899.544444</td>\n",
+       "      <td>11407.748889</td>\n",
+       "      <td>11634.663333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4194304</th>\n",
+       "      <td>12137.278889</td>\n",
+       "      <td>12101.296667</td>\n",
+       "      <td>12120.181111</td>\n",
+       "      <td>11886.986667</td>\n",
+       "      <td>11392.941111</td>\n",
+       "      <td>11639.058889</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         IntelMPI-EFA  IntelMPI-TCP  OpenMPI3-TCP  OpenMPI3-EFA  OpenMPI4-TCP  \\\n",
+       "1            4.164444      4.970000      8.911111      9.023333     10.005556   \n",
+       "2           11.457778     10.023333     18.077778     18.563333     20.063333   \n",
+       "4           22.593333     19.992222     36.776667     37.886667     40.155556   \n",
+       "8           45.155556     39.776667     73.923333     75.977778     80.282222   \n",
+       "16          89.457778     79.224444    146.993333    153.911111    159.472222   \n",
+       "32         179.594444    156.704444    292.995556    306.154444    315.362222   \n",
+       "64         358.608889    311.397778    552.378889    589.117778    604.218889   \n",
+       "128        707.318889    613.613333   1066.624444   1125.285556   1157.770000   \n",
+       "256       1388.386667    772.440000   2038.815556   2173.053333   2229.157778   \n",
+       "512       2651.736667   1515.818889   3721.644444   4014.838889   4094.004444   \n",
+       "1024      5216.915556   2168.664444   6373.132222   6704.161111   6878.360000   \n",
+       "2048      9519.555556   4175.991111   9017.337778   9474.734444   9402.597778   \n",
+       "4096     11619.321111   7478.794444  10420.471111  10722.880000  10469.040000   \n",
+       "8192     12001.058889  10466.297778  11248.557778  11197.362222  10712.904444   \n",
+       "16384    11878.734444  11587.234444  11632.650000  11659.351111  11436.592222   \n",
+       "32768    11828.903333  11856.432222  11868.671111  11873.036667  11463.015556   \n",
+       "65536    11840.230000  11986.157778  11860.595556  11901.541111  11350.562222   \n",
+       "131072   11915.444444  12015.871111  11950.623333  11817.281111  11373.892222   \n",
+       "262144   11959.908889  12068.712222  12102.635556  11877.886667  11342.281111   \n",
+       "524288   12017.102222  12078.778889  12134.346667  11887.037778  11398.695556   \n",
+       "1048576  12052.054444  12097.413333  12132.722222  11897.421111  11383.303333   \n",
+       "2097152  12109.692222  12096.722222  12125.087778  11899.544444  11407.748889   \n",
+       "4194304  12137.278889  12101.296667  12120.181111  11886.986667  11392.941111   \n",
+       "\n",
+       "         OpenMPI4-EFA  \n",
+       "1            9.563333  \n",
+       "2           19.184444  \n",
+       "4           38.270000  \n",
+       "8           76.284444  \n",
+       "16         151.666667  \n",
+       "32         299.974444  \n",
+       "64         575.516667  \n",
+       "128       1101.623333  \n",
+       "256       2128.963333  \n",
+       "512       3917.944444  \n",
+       "1024      6718.307778  \n",
+       "2048      9489.707778  \n",
+       "4096     10667.295556  \n",
+       "8192     11105.977778  \n",
+       "16384    11549.248889  \n",
+       "32768    11537.782222  \n",
+       "65536    11561.130000  \n",
+       "131072   11511.865556  \n",
+       "262144   11567.481111  \n",
+       "524288   11617.685556  \n",
+       "1048576  11639.371111  \n",
+       "2097152  11634.663333  \n",
+       "4194304  11639.058889  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mbw_list = [mbw_impi_efa, mbw_impi_tcp, mbw_ompi3, mbw_ompi3_efa, mbw_ompi4, mbw_ompi4_efa]\n",
+    "mpi_cases = ['IntelMPI-EFA', 'IntelMPI-TCP', 'OpenMPI3-TCP', 'OpenMPI3-EFA', 'OpenMPI4-TCP', 'OpenMPI4-EFA']\n",
+    "\n",
+    "mbw_mean_all = pd.concat([df.mean(axis=1) for df in mbw_list], keys=mpi_cases, axis=1)\n",
+    "mbw_mean_all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\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>IntelMPI-EFA</th>\n",
+       "      <th>IntelMPI-TCP</th>\n",
+       "      <th>OpenMPI3-TCP</th>\n",
+       "      <th>OpenMPI3-EFA</th>\n",
+       "      <th>OpenMPI4-TCP</th>\n",
+       "      <th>OpenMPI4-EFA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.637125</td>\n",
+       "      <td>0.055000</td>\n",
+       "      <td>1.475241</td>\n",
+       "      <td>0.333242</td>\n",
+       "      <td>0.133146</td>\n",
+       "      <td>1.137475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.116916</td>\n",
+       "      <td>0.109430</td>\n",
+       "      <td>2.995312</td>\n",
+       "      <td>0.592938</td>\n",
+       "      <td>0.276541</td>\n",
+       "      <td>2.230130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.232271</td>\n",
+       "      <td>0.210106</td>\n",
+       "      <td>5.733182</td>\n",
+       "      <td>0.883431</td>\n",
+       "      <td>0.566108</td>\n",
+       "      <td>4.923568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.562586</td>\n",
+       "      <td>0.458367</td>\n",
+       "      <td>11.355412</td>\n",
+       "      <td>1.607154</td>\n",
+       "      <td>1.030119</td>\n",
+       "      <td>10.574571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1.428713</td>\n",
+       "      <td>0.934065</td>\n",
+       "      <td>23.361082</td>\n",
+       "      <td>3.423293</td>\n",
+       "      <td>2.051504</td>\n",
+       "      <td>21.241548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>2.579894</td>\n",
+       "      <td>1.905716</td>\n",
+       "      <td>45.228300</td>\n",
+       "      <td>4.379815</td>\n",
+       "      <td>4.051746</td>\n",
+       "      <td>42.259044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>3.177753</td>\n",
+       "      <td>4.049197</td>\n",
+       "      <td>120.978034</td>\n",
+       "      <td>8.041176</td>\n",
+       "      <td>7.370099</td>\n",
+       "      <td>77.942193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>13.611944</td>\n",
+       "      <td>8.261215</td>\n",
+       "      <td>195.968228</td>\n",
+       "      <td>16.384667</td>\n",
+       "      <td>16.385983</td>\n",
+       "      <td>141.941023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>22.860588</td>\n",
+       "      <td>15.017545</td>\n",
+       "      <td>422.516835</td>\n",
+       "      <td>29.263033</td>\n",
+       "      <td>31.141632</td>\n",
+       "      <td>265.625710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>75.001175</td>\n",
+       "      <td>29.281533</td>\n",
+       "      <td>899.896139</td>\n",
+       "      <td>58.250412</td>\n",
+       "      <td>47.581811</td>\n",
+       "      <td>480.100308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1024</th>\n",
+       "      <td>161.374208</td>\n",
+       "      <td>41.722569</td>\n",
+       "      <td>1345.060299</td>\n",
+       "      <td>326.530890</td>\n",
+       "      <td>86.556018</td>\n",
+       "      <td>482.804252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2048</th>\n",
+       "      <td>148.497669</td>\n",
+       "      <td>74.984971</td>\n",
+       "      <td>1690.429228</td>\n",
+       "      <td>395.684893</td>\n",
+       "      <td>428.354342</td>\n",
+       "      <td>473.795717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4096</th>\n",
+       "      <td>78.904540</td>\n",
+       "      <td>140.134477</td>\n",
+       "      <td>1872.913206</td>\n",
+       "      <td>626.307154</td>\n",
+       "      <td>529.739021</td>\n",
+       "      <td>695.817705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8192</th>\n",
+       "      <td>48.784500</td>\n",
+       "      <td>575.511377</td>\n",
+       "      <td>1392.338894</td>\n",
+       "      <td>770.014463</td>\n",
+       "      <td>501.828080</td>\n",
+       "      <td>712.254131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16384</th>\n",
+       "      <td>58.740175</td>\n",
+       "      <td>760.133688</td>\n",
+       "      <td>1346.242191</td>\n",
+       "      <td>723.245971</td>\n",
+       "      <td>622.469586</td>\n",
+       "      <td>406.813297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32768</th>\n",
+       "      <td>24.476174</td>\n",
+       "      <td>828.073389</td>\n",
+       "      <td>1139.440222</td>\n",
+       "      <td>869.454528</td>\n",
+       "      <td>576.091913</td>\n",
+       "      <td>659.536174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65536</th>\n",
+       "      <td>36.252665</td>\n",
+       "      <td>796.418013</td>\n",
+       "      <td>1121.102842</td>\n",
+       "      <td>881.108260</td>\n",
+       "      <td>569.141201</td>\n",
+       "      <td>733.205520</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131072</th>\n",
+       "      <td>41.365171</td>\n",
+       "      <td>842.618495</td>\n",
+       "      <td>1101.819007</td>\n",
+       "      <td>964.415556</td>\n",
+       "      <td>538.064711</td>\n",
+       "      <td>736.043776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262144</th>\n",
+       "      <td>15.834209</td>\n",
+       "      <td>827.510941</td>\n",
+       "      <td>817.560455</td>\n",
+       "      <td>979.006371</td>\n",
+       "      <td>572.776762</td>\n",
+       "      <td>755.747297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524288</th>\n",
+       "      <td>10.525080</td>\n",
+       "      <td>814.244782</td>\n",
+       "      <td>749.834855</td>\n",
+       "      <td>975.770841</td>\n",
+       "      <td>609.221139</td>\n",
+       "      <td>794.749976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1048576</th>\n",
+       "      <td>2.464483</td>\n",
+       "      <td>865.311710</td>\n",
+       "      <td>783.426289</td>\n",
+       "      <td>947.149736</td>\n",
+       "      <td>592.618415</td>\n",
+       "      <td>791.705601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2097152</th>\n",
+       "      <td>5.103834</td>\n",
+       "      <td>892.925105</td>\n",
+       "      <td>793.454454</td>\n",
+       "      <td>932.893366</td>\n",
+       "      <td>596.729271</td>\n",
+       "      <td>808.833718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4194304</th>\n",
+       "      <td>2.273139</td>\n",
+       "      <td>904.183661</td>\n",
+       "      <td>792.933437</td>\n",
+       "      <td>955.377804</td>\n",
+       "      <td>592.823550</td>\n",
+       "      <td>806.801015</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         IntelMPI-EFA  IntelMPI-TCP  OpenMPI3-TCP  OpenMPI3-EFA  OpenMPI4-TCP  \\\n",
+       "1            0.637125      0.055000      1.475241      0.333242      0.133146   \n",
+       "2            0.116916      0.109430      2.995312      0.592938      0.276541   \n",
+       "4            0.232271      0.210106      5.733182      0.883431      0.566108   \n",
+       "8            0.562586      0.458367     11.355412      1.607154      1.030119   \n",
+       "16           1.428713      0.934065     23.361082      3.423293      2.051504   \n",
+       "32           2.579894      1.905716     45.228300      4.379815      4.051746   \n",
+       "64           3.177753      4.049197    120.978034      8.041176      7.370099   \n",
+       "128         13.611944      8.261215    195.968228     16.384667     16.385983   \n",
+       "256         22.860588     15.017545    422.516835     29.263033     31.141632   \n",
+       "512         75.001175     29.281533    899.896139     58.250412     47.581811   \n",
+       "1024       161.374208     41.722569   1345.060299    326.530890     86.556018   \n",
+       "2048       148.497669     74.984971   1690.429228    395.684893    428.354342   \n",
+       "4096        78.904540    140.134477   1872.913206    626.307154    529.739021   \n",
+       "8192        48.784500    575.511377   1392.338894    770.014463    501.828080   \n",
+       "16384       58.740175    760.133688   1346.242191    723.245971    622.469586   \n",
+       "32768       24.476174    828.073389   1139.440222    869.454528    576.091913   \n",
+       "65536       36.252665    796.418013   1121.102842    881.108260    569.141201   \n",
+       "131072      41.365171    842.618495   1101.819007    964.415556    538.064711   \n",
+       "262144      15.834209    827.510941    817.560455    979.006371    572.776762   \n",
+       "524288      10.525080    814.244782    749.834855    975.770841    609.221139   \n",
+       "1048576      2.464483    865.311710    783.426289    947.149736    592.618415   \n",
+       "2097152      5.103834    892.925105    793.454454    932.893366    596.729271   \n",
+       "4194304      2.273139    904.183661    792.933437    955.377804    592.823550   \n",
+       "\n",
+       "         OpenMPI4-EFA  \n",
+       "1            1.137475  \n",
+       "2            2.230130  \n",
+       "4            4.923568  \n",
+       "8           10.574571  \n",
+       "16          21.241548  \n",
+       "32          42.259044  \n",
+       "64          77.942193  \n",
+       "128        141.941023  \n",
+       "256        265.625710  \n",
+       "512        480.100308  \n",
+       "1024       482.804252  \n",
+       "2048       473.795717  \n",
+       "4096       695.817705  \n",
+       "8192       712.254131  \n",
+       "16384      406.813297  \n",
+       "32768      659.536174  \n",
+       "65536      733.205520  \n",
+       "131072     736.043776  \n",
+       "262144     755.747297  \n",
+       "524288     794.749976  \n",
+       "1048576    791.705601  \n",
+       "2097152    808.833718  \n",
+       "4194304    806.801015  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mbw_std_all = pd.concat([df.std(axis=1) for df in mbw_list], keys=mpi_cases, axis=1)\n",
+    "mbw_std_all"
+   ]
+  },
+  {
+   "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>IntelMPI-EFA</th>\n",
+       "      <th>IntelMPI-TCP</th>\n",
+       "      <th>OpenMPI3-TCP</th>\n",
+       "      <th>OpenMPI3-EFA</th>\n",
+       "      <th>OpenMPI4-TCP</th>\n",
+       "      <th>OpenMPI4-EFA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.69</td>\n",
+       "      <td>5.04</td>\n",
+       "      <td>9.65</td>\n",
+       "      <td>9.42</td>\n",
+       "      <td>10.18</td>\n",
+       "      <td>10.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11.66</td>\n",
+       "      <td>10.17</td>\n",
+       "      <td>19.66</td>\n",
+       "      <td>19.17</td>\n",
+       "      <td>20.46</td>\n",
+       "      <td>20.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>23.01</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>39.85</td>\n",
+       "      <td>39.43</td>\n",
+       "      <td>40.98</td>\n",
+       "      <td>40.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>46.31</td>\n",
+       "      <td>40.43</td>\n",
+       "      <td>79.74</td>\n",
+       "      <td>77.06</td>\n",
+       "      <td>81.79</td>\n",
+       "      <td>80.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>90.79</td>\n",
+       "      <td>80.52</td>\n",
+       "      <td>158.26</td>\n",
+       "      <td>159.21</td>\n",
+       "      <td>162.69</td>\n",
+       "      <td>160.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>186.26</td>\n",
+       "      <td>159.49</td>\n",
+       "      <td>316.92</td>\n",
+       "      <td>313.79</td>\n",
+       "      <td>321.24</td>\n",
+       "      <td>317.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>364.41</td>\n",
+       "      <td>317.25</td>\n",
+       "      <td>607.02</td>\n",
+       "      <td>604.59</td>\n",
+       "      <td>613.81</td>\n",
+       "      <td>607.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>731.41</td>\n",
+       "      <td>625.63</td>\n",
+       "      <td>1156.15</td>\n",
+       "      <td>1157.33</td>\n",
+       "      <td>1180.92</td>\n",
+       "      <td>1160.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>1429.61</td>\n",
+       "      <td>794.24</td>\n",
+       "      <td>2233.32</td>\n",
+       "      <td>2233.08</td>\n",
+       "      <td>2275.18</td>\n",
+       "      <td>2234.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>2784.36</td>\n",
+       "      <td>1559.47</td>\n",
+       "      <td>4105.72</td>\n",
+       "      <td>4133.33</td>\n",
+       "      <td>4164.00</td>\n",
+       "      <td>4131.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1024</th>\n",
+       "      <td>5351.75</td>\n",
+       "      <td>2227.48</td>\n",
+       "      <td>6931.00</td>\n",
+       "      <td>6994.68</td>\n",
+       "      <td>7011.21</td>\n",
+       "      <td>7027.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2048</th>\n",
+       "      <td>9704.75</td>\n",
+       "      <td>4275.86</td>\n",
+       "      <td>9854.55</td>\n",
+       "      <td>9895.73</td>\n",
+       "      <td>9896.17</td>\n",
+       "      <td>10094.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4096</th>\n",
+       "      <td>11735.32</td>\n",
+       "      <td>7649.31</td>\n",
+       "      <td>11546.41</td>\n",
+       "      <td>11476.37</td>\n",
+       "      <td>11171.68</td>\n",
+       "      <td>11598.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8192</th>\n",
+       "      <td>12057.20</td>\n",
+       "      <td>11121.88</td>\n",
+       "      <td>12285.77</td>\n",
+       "      <td>12145.56</td>\n",
+       "      <td>11218.72</td>\n",
+       "      <td>11981.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16384</th>\n",
+       "      <td>11971.91</td>\n",
+       "      <td>12571.72</td>\n",
+       "      <td>12612.86</td>\n",
+       "      <td>12802.50</td>\n",
+       "      <td>12137.00</td>\n",
+       "      <td>11854.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32768</th>\n",
+       "      <td>11874.21</td>\n",
+       "      <td>12661.36</td>\n",
+       "      <td>12764.50</td>\n",
+       "      <td>12975.69</td>\n",
+       "      <td>12470.20</td>\n",
+       "      <td>12215.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65536</th>\n",
+       "      <td>11886.46</td>\n",
+       "      <td>13038.60</td>\n",
+       "      <td>12763.02</td>\n",
+       "      <td>12867.62</td>\n",
+       "      <td>12366.02</td>\n",
+       "      <td>12208.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131072</th>\n",
+       "      <td>11968.37</td>\n",
+       "      <td>13061.16</td>\n",
+       "      <td>12905.95</td>\n",
+       "      <td>12925.11</td>\n",
+       "      <td>12396.25</td>\n",
+       "      <td>12326.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262144</th>\n",
+       "      <td>11977.84</td>\n",
+       "      <td>13132.99</td>\n",
+       "      <td>12917.84</td>\n",
+       "      <td>13011.22</td>\n",
+       "      <td>12469.36</td>\n",
+       "      <td>12486.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524288</th>\n",
+       "      <td>12026.74</td>\n",
+       "      <td>13089.50</td>\n",
+       "      <td>12986.76</td>\n",
+       "      <td>12997.93</td>\n",
+       "      <td>12535.18</td>\n",
+       "      <td>12617.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1048576</th>\n",
+       "      <td>12055.93</td>\n",
+       "      <td>13146.42</td>\n",
+       "      <td>13053.04</td>\n",
+       "      <td>13157.76</td>\n",
+       "      <td>12480.33</td>\n",
+       "      <td>12544.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2097152</th>\n",
+       "      <td>12115.67</td>\n",
+       "      <td>13182.40</td>\n",
+       "      <td>13047.24</td>\n",
+       "      <td>12981.31</td>\n",
+       "      <td>12523.53</td>\n",
+       "      <td>12565.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4194304</th>\n",
+       "      <td>12140.40</td>\n",
+       "      <td>13208.58</td>\n",
+       "      <td>13069.83</td>\n",
+       "      <td>12914.85</td>\n",
+       "      <td>12500.78</td>\n",
+       "      <td>12577.47</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         IntelMPI-EFA  IntelMPI-TCP  OpenMPI3-TCP  OpenMPI3-EFA  OpenMPI4-TCP  \\\n",
+       "1                5.69          5.04          9.65          9.42         10.18   \n",
+       "2               11.66         10.17         19.66         19.17         20.46   \n",
+       "4               23.01         20.29         39.85         39.43         40.98   \n",
+       "8               46.31         40.43         79.74         77.06         81.79   \n",
+       "16              90.79         80.52        158.26        159.21        162.69   \n",
+       "32             186.26        159.49        316.92        313.79        321.24   \n",
+       "64             364.41        317.25        607.02        604.59        613.81   \n",
+       "128            731.41        625.63       1156.15       1157.33       1180.92   \n",
+       "256           1429.61        794.24       2233.32       2233.08       2275.18   \n",
+       "512           2784.36       1559.47       4105.72       4133.33       4164.00   \n",
+       "1024          5351.75       2227.48       6931.00       6994.68       7011.21   \n",
+       "2048          9704.75       4275.86       9854.55       9895.73       9896.17   \n",
+       "4096         11735.32       7649.31      11546.41      11476.37      11171.68   \n",
+       "8192         12057.20      11121.88      12285.77      12145.56      11218.72   \n",
+       "16384        11971.91      12571.72      12612.86      12802.50      12137.00   \n",
+       "32768        11874.21      12661.36      12764.50      12975.69      12470.20   \n",
+       "65536        11886.46      13038.60      12763.02      12867.62      12366.02   \n",
+       "131072       11968.37      13061.16      12905.95      12925.11      12396.25   \n",
+       "262144       11977.84      13132.99      12917.84      13011.22      12469.36   \n",
+       "524288       12026.74      13089.50      12986.76      12997.93      12535.18   \n",
+       "1048576      12055.93      13146.42      13053.04      13157.76      12480.33   \n",
+       "2097152      12115.67      13182.40      13047.24      12981.31      12523.53   \n",
+       "4194304      12140.40      13208.58      13069.83      12914.85      12500.78   \n",
+       "\n",
+       "         OpenMPI4-EFA  \n",
+       "1               10.07  \n",
+       "2               20.08  \n",
+       "4               40.37  \n",
+       "8               80.53  \n",
+       "16             160.55  \n",
+       "32             317.50  \n",
+       "64             607.14  \n",
+       "128           1160.71  \n",
+       "256           2234.27  \n",
+       "512           4131.25  \n",
+       "1024          7027.50  \n",
+       "2048         10094.20  \n",
+       "4096         11598.24  \n",
+       "8192         11981.18  \n",
+       "16384        11854.39  \n",
+       "32768        12215.92  \n",
+       "65536        12208.84  \n",
+       "131072       12326.90  \n",
+       "262144       12486.51  \n",
+       "524288       12617.91  \n",
+       "1048576      12544.26  \n",
+       "2097152      12565.97  \n",
+       "4194304      12577.47  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mbw_max_all = pd.concat([df.max(axis=1) for df in mbw_list], keys=mpi_cases, axis=1)\n",
+    "mbw_max_all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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": [
+    "plot_osu(mbw_mean_all)\n",
+    "plt.title('Multi-pair Bandwidth')\n",
+    "plt.ylabel('MB/s');"
+   ]
+  },
+  {
+   "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": [
+    "plot_osu(mbw_std_all)\n",
+    "plt.title('Standard deviation')\n",
+    "plt.ylabel('MB/s');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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": [
+    "plot_osu(mbw_max_all)\n",
+    "plt.title('Maximum multi-pair Bandwidth')\n",
+    "plt.ylabel('MB/s');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}