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|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# YouTube energy comparison with and without low-util mode\n",
"\n",
"Test: Run YouTube red video (in offline mode) for 120 seconds, and collect energy around 10 times for each configuration\n",
"\n",
"Conclusion: YouTube saves around 1.5-2% energy due to low-utilization mode.\n",
"\n",
"(side note: following is the energy consumed when offlining big cpus, the savings is ~4%)\n",
" \"output\": 20.935927396987264\n",
" \"output\": 20.886319054545837\n",
" \"output\": 20.873948319403212\n",
" \"output\": 20.80955464619092\n",
" \"output\": 20.816727961893836\n",
" \"output\": 20.803202597131868"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"marked": false,
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"data": {
"text/plain": [
"[21.33999307201658,\n",
" 21.365129290570263,\n",
" 21.34209450334471,\n",
" 21.289426409399248,\n",
" 21.263789089763883,\n",
" 21.329407238590395]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%pylab inline\n",
"\n",
"import pandas as pd\n",
"import sqlite3\n",
"import matplotlib.cm as cm\n",
"import os, json\n",
"from collections import namedtuple\n",
"\n",
"# Provide the root path where your test folders are stored\n",
"results_dir = '/home/joelaf/repo/lisa-aosp/external/lisa/results/'\n",
"\n",
"# Provide the names of the results folders you want compared\n",
"all_test_dirs = [\n",
" (\"YouTubeRed_4.4.56-g0583b6191868\", 'top-of-tree'),\n",
" (\"YouTubeRed_4.4.56-g931b784e1a3f-30070-ga668ea9c5165\", 'tot+lowutil+25pc'),\n",
" (\"YouTubeRed_4.4.56-g931b784e1a3f-30071-g099f7067358a\", 'tot+lowutilmode+50pc')\n",
" ]\n",
"\n",
"results_dirs = [x[0] for x in os.walk(results_dir)]\n",
"\n",
"def get_edir(test):\n",
" edir = test + '_energy'\n",
" return [x for x in results_dirs if edir in x]\n",
"\n",
"def get_samples(test):\n",
" samples = []\n",
" edirs = get_edir(test)\n",
" for edir in edirs:\n",
" with open(edir + '/energy.json') as f:\n",
" output = json.load(f)['output']\n",
" samples.append(output)\n",
" return samples\n",
"\n",
"get_samples(all_test_dirs[2][0])\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# Plot histograms of energy consumed for tests"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('YouTubeRed_4.4.56-g0583b6191868', 'top-of-tree')\n",
"21.5895253595\n",
"21.6164255475\n",
"21.5485038682\n",
"22.0296997264\n",
"21.6493742035\n",
"21.5483970834\n",
"21.5943349871\n",
"21.6647268717\n",
"('YouTubeRed_4.4.56-g931b784e1a3f-30070-ga668ea9c5165', 'tot+lowutil+25pc')\n",
"21.3110673127\n",
"21.4832853056\n",
"21.4711717277\n",
"21.48856738\n",
"21.3084231193\n",
"21.3057657682\n",
"21.2137620153\n",
"21.198791322\n",
"21.3047078781\n",
"21.312377348\n",
"21.2130661441\n",
"('YouTubeRed_4.4.56-g931b784e1a3f-30071-g099f7067358a', 'tot+lowutilmode+50pc')\n",
"21.339993072\n",
"21.3651292906\n",
"21.3420945033\n",
"21.2894264094\n",
"21.2637890898\n",
"21.3294072386\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc14487bb50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot a box plot\n",
"fig, axes = plt.subplots()\n",
"df_all = []\n",
"for test in all_test_dirs:\n",
" samples = get_samples(test[0])\n",
" df = pd.DataFrame(samples, columns=[test[1]])\n",
" # print df.describe()\n",
" print test\n",
" for s in samples:\n",
" print s\n",
" df_all.append(df)\n",
"\n",
"df_box = pd.concat(df_all, axis=1)\n",
"axes = df_box.plot.box(figsize=(10, 6), ax=axes, ylim=(21,21.8), title=\"Box plot comparing energy samples\")\n",
"\n",
"# Plot a histogram of energy values collected\n",
"# def plot_energy(test):\n",
"# test_dir = results_dir + \"/\" + test\n",
"# with open(test_dir + \"/energy_all_runs.json\") as f:\n",
"# samples = json.load(f)['energy_samples']\n",
"# df = pd.DataFrame(samples, columns=['energy'])\n",
"# fig, axes = plt.subplots()\n",
"# # print axes\n",
"# df.plot(kind='hist', bins=32, xlim=(6,10), title=test, figsize=(16,5), ax=axes)\n",
"\n",
"# for t in all_test_dirs:\n",
"# plot_energy(t)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "189px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
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"threshold": 4,
"toc_cell": false,
"toc_number_sections": true,
"toc_section_display": "block",
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"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|