ipynb: add an example notebook to plot kernel function profiling data
Kernel functions profiling support allows to collect statistics on time
spend whiting a set of specified kernel function.
Profiling data are collected into a JSON file "trace_stat.json" in the
same output folder where the trace of an experiment is generated.
This patch provides a simple notebook to process and plots all the profiling
data collected during some experiments. This should be just good to have
an initial visual representation of the overheads. However, a proper
evaluation can require a specific setup of the experiments. For example
it could be required to focus on a single CPU by pinning the workload there
as well as to use the proper CPUFreq governor. All these details are left
to the user expertise.
Signed-off-by: Patrick Bellasi <patrick.bellasi@arm.com>
diff --git a/ipynb/profiling/kernel_functions_profiling.ipynb b/ipynb/profiling/kernel_functions_profiling.ipynb
new file mode 100644
index 0000000..858e081
--- /dev/null
+++ b/ipynb/profiling/kernel_functions_profiling.ipynb
@@ -0,0 +1,271 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<font size=\"9\">Kernel Functions Profiling</font><br>\n",
+ "<hr>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "reload(logging)\n",
+ "logging.basicConfig(\n",
+ " format='%(asctime)-9s %(levelname)-8s: %(message)s',\n",
+ " datefmt='%I:%M:%S')\n",
+ "\n",
+ "# Enable logging at INFO level\n",
+ "logging.getLogger().setLevel(logging.INFO)\n",
+ "# Comment the follwing line to disable devlib debugging statements\n",
+ "logging.getLogger('ssh').setLevel(logging.DEBUG)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Generate plots inline\n",
+ "%pylab inline\n",
+ "\n",
+ "import json\n",
+ "import os\n",
+ "\n",
+ "import re\n",
+ "import collections\n",
+ "import pandas"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Define folder containgin profiling data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "11:50:08 INFO : Content of the output folder ../../results_latest/\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Base folder where tests folder are located\n",
+ "res_dir = '../../results_latest/'\n",
+ "logging.info('Content of the output folder %s', res_dir)\n",
+ "#!tree {res_dir}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load function profiling data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "def autodict():\n",
+ " return collections.defaultdict(autodict)\n",
+ "\n",
+ "def parse_perf_stat(res_dir):\n",
+ " TEST_DIR_RE = re.compile(r'.*/([^:]*):([^:]*):([^:]*)')\n",
+ " profiling_data = autodict()\n",
+ "\n",
+ " for test_idx in sorted(os.listdir(res_dir)):\n",
+ " test_dir = os.path.join(res_dir, test_idx)\n",
+ " if not os.path.isdir(test_dir):\n",
+ " continue\n",
+ " match = TEST_DIR_RE.search(test_dir)\n",
+ " if not match:\n",
+ " continue\n",
+ " wtype = match.group(1)\n",
+ " tconf = match.group(2)\n",
+ " wload = match.group(3)\n",
+ "\n",
+ " #logging.info('Processing %s:%s:%s', wtype, tconf, wload)\n",
+ " trace_stat_file = os.path.join(test_dir, '1', 'trace_stat.json')\n",
+ " if not os.path.isfile(trace_stat_file):\n",
+ " continue\n",
+ " with open(trace_stat_file, 'r') as fh:\n",
+ " data = json.load(fh)\n",
+ " for cpu_id, cpu_stats in sorted(data.items()):\n",
+ " for fname in cpu_stats:\n",
+ " profiling_data[cpu_id][tconf][fname] = cpu_stats[fname]\n",
+ "\n",
+ " return profiling_data\n",
+ " \n",
+ "profiling_data = parse_perf_stat(res_dir)\n",
+ "#logging.info(\"Profiling data:\\n%s\", json.dumps(profiling_data, indent=4))\n",
+ "#profiling_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Build Pandas DataFrame from profiling data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def get_df(profiling_data):\n",
+ " cpu_ids = []\n",
+ " cpu_frames = []\n",
+ " for cpu_id, cpu_data in sorted(profiling_data.items()):\n",
+ " cpu_ids.append(cpu_id)\n",
+ " conf_ids = []\n",
+ " conf_frames = []\n",
+ " for conf_id, conf_data in cpu_data.iteritems():\n",
+ " conf_ids.append(conf_id)\n",
+ " function_data = pandas.DataFrame.from_dict(conf_data, orient='index')\n",
+ " conf_frames.append(function_data)\n",
+ " df = pandas.concat(conf_frames, keys=conf_ids)\n",
+ " cpu_frames.append(df)\n",
+ " df = pandas.concat(cpu_frames, keys=cpu_ids)\n",
+ " #df.head()\n",
+ " return df\n",
+ "\n",
+ "stats_df = get_df(profiling_data)\n",
+ "#stats_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot profiling data per function and CPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def plot_stats(df, fname, axes=None):\n",
+ " func_data = df.xs(fname, level=2)\n",
+ " func_stats = func_data.xs(['avg', 's_2'], axis=1)\n",
+ " #func_stats\n",
+ " func_avg = func_stats.unstack(level=1)['avg']\n",
+ " func_std = func_stats.unstack(level=1)['s_2'].apply(numpy.sqrt)\n",
+ " func_avg.plot(kind='bar', title=fname, yerr=func_std, ax=axes);\n",
+ "\n",
+ "#plot_stats(stats_df, 'sched_group_energy')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "11:50:21 INFO : Plotting stats for [sched_group_energy] function\n",
+ "11:50:21 INFO : Plotting stats for [select_task_rq_fair] function\n",
+ "11:50:21 INFO : Plotting stats for [energy_diff] function\n",
+ "11:50:21 INFO : Plotting stats for [schedtune_dequeue_task] function\n",
+ "11:50:21 INFO : Plotting stats for [schedtune_enqueue_task] function\n",
+ "11:50:21 INFO : Plotting stats for [schedtune_task_boost] function\n"
+ ]
+ },
+ {
+ "data": {
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ATE5OzqjNBzzgAbnyyitz55135iEPeUiOPvroPTqGM844IytWrMiBBx6YE088MZdffnmS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\nAACA7v4/WNEZI4XAvtUAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f2fe9b29bd0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def plot_all_functions(df):\n",
+ " functions = df.index.get_level_values(2).unique()\n",
+ " fcount = len(functions)\n",
+ "\n",
+ " fig, pltaxes = plt.subplots(fcount, 1, figsize=(16, 8*fcount))\n",
+ "\n",
+ " fig_id = 0\n",
+ " for fname in functions:\n",
+ " logging.info(\"Plotting stats for [%s] function\", fname)\n",
+ " axes = pltaxes[fig_id]\n",
+ " plot_stats(df, fname, axes)\n",
+ " fig_id = fig_id + 1\n",
+ " \n",
+ "plot_all_functions(stats_df)"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "2.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}