diff --git a/ipynb/android/Android_Workloads.ipynb b/ipynb/android/Android_Workloads.ipynb deleted file mode 100644 index af1b765e..00000000 --- a/ipynb/android/Android_Workloads.ipynb +++ /dev/null @@ -1,652 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Android Workloads Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-06 19:28:40,088 INFO : root : Using LISA logging configuration:\n", - "2016-12-06 19:28:40,089 INFO : root : /home/vagrant/lisa/logging.conf\n" - ] - } - ], - "source": [ - "import logging\n", - "from conf import LisaLogging\n", - "LisaLogging.setup()" - ] - }, - { - "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": [ - "%pylab inline\n", - "\n", - "import collections\n", - "import copy\n", - "import json\n", - "import os\n", - "from time import sleep\n", - "\n", - "# Support to access the remote target\n", - "import devlib\n", - "from env import TestEnv\n", - "\n", - "# from devlib.utils.android import adb_command\n", - "\n", - "# Import support for Android devices\n", - "from android import Screen, Workload, System\n", - "\n", - "# Support for trace events analysis\n", - "from trace import Trace\n", - "\n", - "# Suport for FTrace events parsing and visualization\n", - "import trappy\n", - "\n", - "import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test Environment set up" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Devlib requires the ANDROID_HOME environment variable configured to point to your local installation of the Android SDK. If you have not this variable configured in the shell used to start the notebook server, you need to run an additional cell to define where your Android SDK is installed or add \"ANDROID_HOME\" in your target configuration.\n", - "CATAPULT_HOME is considered to be in LISA_HOME/tools/catapult." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In case more than one Android device are conencted to the host, you must specify the ID of the device you want to target in `my_target_conf`. Run `adb devices` on your host to get the ID." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Android device to target\n", - "DEVICE = 'HT6670300102'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup target configuration\n", - "my_conf = {\n", - "\n", - " # Target platform and board\n", - " \"platform\" : 'android',\n", - " \"device\" : DEVICE,\n", - " \"ANDROID_HOME\" : '/home/vagrant/lisa/tools/android-sdk-linux/',\n", - "\n", - "# \"emeter\" : {\n", - "# \"instrument\" : \"aep\",\n", - "# \"conf\" : {\n", - "# 'labels' : ['BAT'],\n", - "# 'resistor_values' : [0.099],\n", - "# 'device_entry' : '/dev/ttyACM1',\n", - "# }\n", - "# },\n", - "\n", - " # Folder where all the results will be collected\n", - " \"results_dir\" : \"Android_Workloads\",\n", - "\n", - " # Define devlib modules to load\n", - " \"modules\" : [\n", - " 'cpufreq' # enable CPUFreq support\n", - " ],\n", - "\n", - " # FTrace events to collect for all the tests configuration which have\n", - " # the \"ftrace\" flag enabled\n", - " \"ftrace\" : {\n", - " \"events\" : [\n", - " \"sched_switch\",\n", - " \"sched_overutilized\",\n", - " \"sched_contrib_scale_f\",\n", - " \"sched_load_avg_cpu\",\n", - " \"sched_load_avg_task\",\n", - " \"sched_tune_tasks_update\",\n", - " \"sched_boost_cpu\",\n", - " \"sched_boost_task\",\n", - " \"sched_energy_diff\",\n", - " \"cpu_frequency\",\n", - " \"cpu_idle\",\n", - " \"cpu_capacity\",\n", - " ],\n", - " \"buffsize\" : 10 * 1024,\n", - " },\n", - "\n", - " # Tools required by the experiments\n", - " \"tools\" : [ 'trace-cmd' ],\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# List of configurations to test (keys of 'confs' defined in cell #9)\n", - "test_confs = ['std']\n", - "\n", - "# List of workloads to run, each workload consists of a workload name\n", - "# followed by a list of workload specific parameters\n", - "test_wloads = [\n", - " \n", - "\n", - " # YouTube workload:\n", - "# Params:\n", - "# - video URL (with optional start time)\n", - "# - duration [s] to playback\n", - " 'YouTube https://youtu.be/XSGBVzeBUbk?t=45s 15',\n", - "\n", - "# Jankbench workload:\n", - "# Params:\n", - "# - id of the benchmakr to run\n", - " 'Jankbench list_view',\n", - "# 'Jankbench image_list_view',\n", - "# 'Jankbench shadow_grid',\n", - "# 'Jankbench low_hitrate_text',\n", - "# 'Jankbench high_hitrate_text',\n", - "# 'Jankbench edit_text',\n", - "\n", - "]\n", - "\n", - "# Iterations for each test\n", - "iterations = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Define what we want to collect as a list of strings.\n", - "# Supported values are\n", - "# energy - Use the my_conf's defined emeter to measure energy consumption across experiments\n", - "# ftrace - Collect an execution trace using trace-cmd\n", - "# systrace - Collect an execution trace using Systrace/Atrace\n", - "# NOTE: energy is automatically enabled in case an \"emeter\" confHT6670300102iguration is defined in my_conf\n", - "collect = 'systrace'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Support Functions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This set of support functions will help us running the benchmark using different CPUFreq governors." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def set_performance():\n", - " target.cpufreq.set_all_governors('performance')\n", - "\n", - "def set_powersave():\n", - " target.cpufreq.set_all_governors('powersave')\n", - "\n", - "def set_interactive():\n", - " target.cpufreq.set_all_governors('interactive')\n", - "\n", - "def set_sched():\n", - " target.cpufreq.set_all_governors('sched')\n", - "\n", - "def set_ondemand():\n", - " target.cpufreq.set_all_governors('ondemand')\n", - " \n", - " for cpu in target.list_online_cpus():\n", - " tunables = target.cpufreq.get_governor_tunables(cpu)\n", - " target.cpufreq.set_governor_tunables(\n", - " cpu,\n", - " 'ondemand',\n", - " **{'sampling_rate' : tunables['sampling_rate_min']}\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Available test configurations\n", - "confs = {\n", - " 'std' : {\n", - " 'label' : 'int',\n", - " 'set' : set_interactive,\n", - " },\n", - " 'eas' : {\n", - " 'label' : 'sch',\n", - " 'set' : set_sched,\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Experiments Execution Function" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def experiment(wl, res_dir, conf_name, wload_name, iterations, collect=''):\n", - " \n", - " # Load workload params\n", - " wload_kind = wload_name.split()[0]\n", - " wload_tag = wload_name.split()[1]\\\n", - " .replace('https://youtu.be/', '')\\\n", - " .replace('?t=', '_')\n", - " \n", - " # Check for workload being available\n", - " wload = Workload.get(te, wload_kind)\n", - " if not wload:\n", - " return {}\n", - " \n", - " # Setup test results folder\n", - " exp_dir = os.path.join(res_dir, conf_name, \"{}_{}\".format(wload_kind, wload_tag))\n", - " os.system('mkdir -p {}'.format(exp_dir));\n", - "\n", - " # Configure governor\n", - " confs[conf_name]['set']()\n", - " \n", - " # Configure screen to max brightness and no dimming\n", - " Screen.set_brightness(target, percent=100)\n", - " Screen.set_dim(target, auto=False)\n", - " Screen.set_timeout(target, 60*60*10) # 10 hours should be enought for an experiment\n", - " \n", - " # Start the required tracing command\n", - " if 'ftrace' in collect:\n", - " # Start FTrace and Energy monitoring\n", - " te.ftrace.start()\n", - " elif 'systrace' in collect:\n", - " # Start systrace\n", - " trace_file = os.path.join(exp_dir, 'trace.html')\n", - " systrace_output = System.systrace_start(te, trace_file, 10 * iterations)\n", - " \n", - " ###########################\n", - " # Run the required workload\n", - " \n", - " # Jankbench\n", - " if 'Jankbench' in wload_name:\n", - " db_file, nrg_report = wload.run(exp_dir, wload_tag, iterations, collect)\n", - " \n", - " # YouTube\n", - " elif 'YouTube' in wload_name:\n", - " video_url = wload_name.split()[1]\n", - " video_duration_s = wload_name.split()[2]\n", - " db_file, nrg_report = wload.run(exp_dir, video_url, int(video_duration_s), collect)\n", - "\n", - " ###########################\n", - " \n", - " # Stop the required trace command\n", - " if 'ftrace' in collect:\n", - " te.ftrace.stop()\n", - " # Collect and keep track of the trace\n", - " trace_file = os.path.join(exp_dir, 'trace.dat')\n", - " te.ftrace.get_trace(trace_file)\n", - " elif 'systrace' in collect:\n", - " if systrace_output:\n", - " logging.info('Waiting systrace report [%s]...', trace_file)\n", - " systrace_output.wait()\n", - " else:\n", - " logging.warning('Systrace is not running!')\n", - "\n", - " # Reset screen brightness and auto dimming\n", - " Screen.set_defaults(target, )\n", - " \n", - " # Dump platform descriptor\n", - " te.platform_dump(exp_dir)\n", - "\n", - " # return all the experiment data\n", - " if 'trace' in collect:\n", - " return {\n", - " 'dir' : exp_dir,\n", - " 'db_file' : db_file,\n", - " 'nrg_report' : copy.deepcopy(nrg_report),\n", - " 'trace_file' : trace_file,\n", - " }\n", - " else:\n", - " return {\n", - " 'dir' : exp_dir,\n", - " 'db_file' : db_file,\n", - " 'nrg_report' : copy.deepcopy(nrg_report),\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Main" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Target Connection" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "caiman: no process found\r\n" - ] - } - ], - "source": [ - "# # Cleanup Caiman energy meter temporary folders\n", - "# !rm -rf /tmp/eprobe-caiman-*\n", - "# # Ensure there are not other \"caiman\" instanced running for the specified device\n", - "# # my_conf['emeter']['conf']['device_entry']\n", - "!killall caiman" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "adbd is already running as root\r\n" - ] - } - ], - "source": [ - "# Ensure ADB is running as root\n", - "!adb -s {DEVICE} root" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-06 19:28:52,064 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", - "2016-12-06 19:28:52,065 INFO : TestEnv : Loading custom (inline) target configuration\n", - "2016-12-06 19:28:52,065 INFO : TestEnv : External tools using:\n", - "2016-12-06 19:28:52,065 INFO : TestEnv : ANDROID_HOME: /home/vagrant/lisa/tools/android-sdk-linux/\n", - "2016-12-06 19:28:52,066 INFO : TestEnv : CATAPULT_HOME: /home/vagrant/lisa/tools/catapult\n", - "2016-12-06 19:28:52,066 INFO : TestEnv : Devlib modules to load: ['cpufreq']\n", - "2016-12-06 19:28:52,066 INFO : TestEnv : Connecting Android target [HT6670300102]\n", - "2016-12-06 19:28:52,067 INFO : TestEnv : Connection settings:\n", - "2016-12-06 19:28:52,067 INFO : TestEnv : {'device': 'HT6670300102'}\n", - "2016-12-06 19:28:52,146 INFO : android : ls command is set to ls -1\n", - "2016-12-06 19:28:52,979 INFO : TestEnv : Initializing target workdir:\n", - "2016-12-06 19:28:52,982 INFO : TestEnv : /data/local/tmp/devlib-target\n", - "2016-12-06 19:28:54,953 INFO : TestEnv : Topology:\n", - "2016-12-06 19:28:54,956 INFO : TestEnv : [[0, 1], [2, 3]]\n", - "2016-12-06 19:28:55,857 INFO : TestEnv : Enabled tracepoints:\n", - "2016-12-06 19:28:55,857 INFO : TestEnv : sched_switch\n", - "2016-12-06 19:28:55,858 INFO : TestEnv : sched_overutilized\n", - "2016-12-06 19:28:55,858 INFO : TestEnv : sched_contrib_scale_f\n", - "2016-12-06 19:28:55,859 INFO : TestEnv : sched_load_avg_cpu\n", - "2016-12-06 19:28:55,859 INFO : TestEnv : sched_load_avg_task\n", - "2016-12-06 19:28:55,860 INFO : TestEnv : sched_tune_tasks_update\n", - "2016-12-06 19:28:55,860 INFO : TestEnv : sched_boost_cpu\n", - "2016-12-06 19:28:55,861 INFO : TestEnv : sched_boost_task\n", - "2016-12-06 19:28:55,861 INFO : TestEnv : sched_energy_diff\n", - "2016-12-06 19:28:55,862 INFO : TestEnv : cpu_frequency\n", - "2016-12-06 19:28:55,862 INFO : TestEnv : cpu_idle\n", - "2016-12-06 19:28:55,863 INFO : TestEnv : cpu_capacity\n", - "2016-12-06 19:28:55,863 INFO : TestEnv : Set results folder to:\n", - "2016-12-06 19:28:55,863 INFO : TestEnv : /home/vagrant/lisa/results/Android_Workloads\n", - "2016-12-06 19:28:55,864 INFO : TestEnv : Experiment results available also in:\n", - "2016-12-06 19:28:55,864 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" - ] - } - ], - "source": [ - "# Initialize a test environment using:\n", - "te = TestEnv(my_conf, wipe=False)\n", - "target = te.target" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Workloads Execution and Data Collection" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-06 19:29:38,412 INFO : Workload : Workloads available on target:\n", - "2016-12-06 19:29:38,416 INFO : Workload : ['YouTube', 'Jankbench']\n", - "2016-12-06 19:29:38,419 INFO : root : ------------------------\n", - "2016-12-06 19:29:38,420 INFO : root : Test 1/2: YOUTUBE in STD configuration\n", - "2016-12-06 19:29:38,421 INFO : Workload : Workloads available on target:\n", - "2016-12-06 19:29:38,423 INFO : Workload : ['YouTube', 'Jankbench']\n", - "2016-12-06 19:29:39,389 INFO : Screen : Set brightness: 100%\n", - "2016-12-06 19:29:39,873 INFO : Screen : Dim screen mode: OFF\n", - "2016-12-06 19:29:40,333 INFO : Screen : Screen timeout: 36000 [s]\n", - "2016-12-06 19:29:40,336 INFO : System : SysTrace: /home/vagrant/lisa/tools/catapult/systrace/systrace/run_systrace.py -e HT6670300102 -o /home/vagrant/lisa/results/Android_Workloads/std/YouTube_XSGBVzeBUbk_45s/trace.html gfx view sched freq idle -t 10\n", - "2016-12-06 19:29:41,826 INFO : Screen : Force manual orientation\n", - "2016-12-06 19:29:41,829 INFO : Screen : Set orientation: LANDSCAPE\n", - "2016-12-06 19:29:45,347 INFO : YouTube : Play video for 15 [s]\n", - "2016-12-06 19:30:01,445 INFO : Screen : Set orientation: AUTO\n", - "2016-12-06 19:30:02,322 INFO : root : Waiting systrace report [/home/vagrant/lisa/results/Android_Workloads/std/YouTube_XSGBVzeBUbk_45s/trace.html]...\n", - "2016-12-06 19:30:02,323 INFO : Screen : Set orientation: AUTO\n", - "2016-12-06 19:30:03,628 INFO : Screen : Set brightness: AUTO\n", - "2016-12-06 19:30:04,065 INFO : Screen : Dim screen mode: ON\n", - "2016-12-06 19:30:04,500 INFO : Screen : Screen timeout: 30 [s]\n", - "2016-12-06 19:30:04,602 INFO : root : ------------------------\n", - "2016-12-06 19:30:04,603 INFO : root : Test 2/2: JANKBENCH in STD configuration\n", - "2016-12-06 19:30:04,604 INFO : Workload : Workloads available on target:\n", - "2016-12-06 19:30:04,604 INFO : Workload : ['YouTube', 'Jankbench']\n", - "2016-12-06 19:30:05,629 INFO : Screen : Set brightness: 100%\n", - "2016-12-06 19:30:06,085 INFO : Screen : Dim screen mode: OFF\n", - "2016-12-06 19:30:06,559 INFO : Screen : Screen timeout: 36000 [s]\n", - "2016-12-06 19:30:06,562 INFO : System : SysTrace: /home/vagrant/lisa/tools/catapult/systrace/systrace/run_systrace.py -e HT6670300102 -o /home/vagrant/lisa/results/Android_Workloads/std/Jankbench_list_view/trace.html gfx view sched freq idle -t 10\n", - "2016-12-06 19:30:09,377 INFO : Screen : Force manual orientation\n", - "2016-12-06 19:30:09,378 INFO : Screen : Set orientation: PORTRAIT\n", - "2016-12-06 19:30:10,322 INFO : Jankbench : am start -n \"com.android.benchmark/.app.RunLocalBenchmarksActivity\" --eia \"com.android.benchmark.EXTRA_ENABLED_BENCHMARK_IDS\" 0 --ei \"com.android.benchmark.EXTRA_RUN_COUNT\" 1\n", - "2016-12-06 19:30:10,785 INFO : Jankbench : adb -s HT6670300102 logcat ActivityManager:* System.out:I *:S BENCH:*\n", - "2016-12-06 19:30:45,646 INFO : Jankbench : Mean: 29.949 JankP: 0.061 StdDev: 24.786 Count Bad: 2 Count Jank: 1\n", - "2016-12-06 19:30:48,273 INFO : Screen : Set orientation: AUTO\n", - "2016-12-06 19:30:50,116 INFO : root : Waiting systrace report [/home/vagrant/lisa/results/Android_Workloads/std/Jankbench_list_view/trace.html]...\n", - "2016-12-06 19:30:50,117 INFO : Screen : Set orientation: AUTO\n", - "2016-12-06 19:30:51,516 INFO : Screen : Set brightness: AUTO\n", - "2016-12-06 19:30:51,946 INFO : Screen : Dim screen mode: ON\n", - "2016-12-06 19:30:52,340 INFO : Screen : Screen timeout: 30 [s]\n" - ] - } - ], - "source": [ - "# Unlock device screen (assume no password required)\n", - "target.execute('input keyevent 82')\n", - "\n", - "# Intialize Workloads for this test environment\n", - "wl = Workload(te)\n", - "\n", - "# The set of results for each comparison test\n", - "results = collections.defaultdict(dict)\n", - "\n", - "# Enable energy collection if an emeter has been configured\n", - "if 'emeter' in my_conf and te.emeter:\n", - " logging.info('Enabling ENERGY collection')\n", - " collect += ' energy'\n", - "\n", - "# Run the benchmark in all the configured governors\n", - "for conf_name in test_confs:\n", - "\n", - " for idx,wload_name in enumerate(test_wloads):\n", - " \n", - " wload_kind = wload_name.split()[0]\n", - " logging.info('------------------------')\n", - " logging.info('Test %d/%d: %s in %s configuration',\n", - " idx+1, len(test_wloads), wload_kind.upper(), conf_name.upper())\n", - " res = experiment(wl, te.res_dir, conf_name, wload_name, iterations, collect)\n", - " results[conf_name][wload_name] = res\n", - "\n", - " # Save collected results\n", - " conf_dir = os.path.join(te.res_dir, conf_name)\n", - " if not os.path.isdir(conf_dir): os.mkdir(conf_dir)\n", - " res_file = os.path.join(conf_dir, 'results.json')\n", - " with open(res_file, 'w') as fh:\n", - " json.dump(results[conf_name], fh, indent=4)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Energy Measurements Report" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Energy consumption STD, YOUTUBE HTTPS://YOUTU.BE/XSGBVZEBUBK?T=45S 15 : NaN\n", - "Energy consumption STD, JANKBENCH LIST_VIEW : NaN\n" - ] - } - ], - "source": [ - "for conf_name in test_confs:\n", - " for idx,wload_name in enumerate(test_wloads):\n", - " nrg = 'NaN'\n", - " result = results[conf_name][wload_name]\n", - " if 'nrg_report' in result and result['nrg_report']:\n", - " nrg = '{:6.1f}'.format(float(result['nrg_report'].channels['BAT']))\n", - " print \"Energy consumption {}, {:52}: {}\".format(conf_name.upper(), wload_name.upper(), nrg)" - ] - } - ], - "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": "2.7.6" - }, - "toc": { - "toc_cell": false, - "toc_number_sections": true, - "toc_threshold": 6, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/ipynb/android/benchmarks/Android_PCMark.ipynb b/ipynb/android/benchmarks/Android_PCMark.ipynb deleted file mode 100644 index e014ad2f..00000000 --- a/ipynb/android/benchmarks/Android_PCMark.ipynb +++ /dev/null @@ -1,523 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# EAS Testing - PCMark benchmark on Android" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The goal of this experiment is to run benchmarks on a Nexus N5X running Android with an EAS kernel and collect results. The analysis phase will consist in comparing EAS with other schedulers, that is comparing *sched* governor with:\n", - "\n", - " - interactive\n", - " - performance\n", - " - powersave\n", - " - ondemand\n", - " \n", - "The benchmark we will be using is ***PCMark*** (https://www.futuremark.com/benchmarks/pcmark-android). You will need to **manually install** the app on the Android device in order to run this Notebook.\n", - "\n", - "When opinening PCMark for the first time you will need to Install the work benchmark from inside the app." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import logging\n", - "from conf import LisaLogging\n", - "LisaLogging.setup()" - ] - }, - { - "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": [ - "%pylab inline\n", - "\n", - "import copy\n", - "import os\n", - "from time import sleep\n", - "from subprocess import Popen\n", - "import pandas as pd\n", - "\n", - "# Support to access the remote target\n", - "import devlib\n", - "from env import TestEnv\n", - "\n", - "# Support for trace events analysis\n", - "from trace import Trace\n", - "\n", - "# Suport for FTrace events parsing and visualization\n", - "import trappy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test Environment set up" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In case more than one Android device are conencted to the host, you must specify the ID of the device you want to target in `my_target_conf`. Run `adb devices` on your host to get the ID." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup a target configuration\n", - "my_target_conf = {\n", - " \n", - " # Target platform and board\n", - " \"platform\" : 'android',\n", - "\n", - " # Add target support\n", - " \"board\" : 'n5x',\n", - " \n", - " # Device ID\n", - " #\"device\" : \"00b1346f0878ccb1\",\n", - " \n", - " # Define devlib modules to load\n", - " \"modules\" : [\n", - " 'cpufreq' # enable CPUFreq support\n", - " ],\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "my_tests_conf = {\n", - "\n", - " # Folder where all the results will be collected\n", - " \"results_dir\" : \"Android_PCMark\",\n", - "\n", - " # Platform configurations to test\n", - " \"confs\" : [\n", - " {\n", - " \"tag\" : \"pcmark\",\n", - " \"flags\" : \"ftrace\", # Enable FTrace events\n", - " \"sched_features\" : \"ENERGY_AWARE\", # enable EAS\n", - " },\n", - " ],\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-04-04 09:56:39,384 INFO : Target - Using base path: /home/pippo/work/lisa\n", - "2016-04-04 09:56:39,385 INFO : Target - Loading custom (inline) target configuration\n", - "2016-04-04 09:56:39,386 INFO : Target - Loading custom (inline) test configuration\n", - "2016-04-04 09:56:39,386 INFO : Target - Devlib modules to load: ['bl', 'cpufreq']\n", - "2016-04-04 09:56:39,387 INFO : Target - Connecting Android target [DEFAULT]\n", - "2016-04-04 09:56:39,772 INFO : Target - Initializing target workdir:\n", - "2016-04-04 09:56:39,774 INFO : Target - /data/local/tmp/devlib-target\n", - "2016-04-04 09:56:41,938 INFO : Target - Topology:\n", - "2016-04-04 09:56:41,940 INFO : Target - [[0, 1, 2, 3], [4, 5]]\n", - "2016-04-04 09:56:42,062 INFO : TestEnv - Set results folder to:\n", - "2016-04-04 09:56:42,063 INFO : TestEnv - /home/pippo/work/lisa/results/Android_PCMark\n", - "2016-04-04 09:56:42,064 INFO : TestEnv - Experiment results available also in:\n", - "2016-04-04 09:56:42,065 INFO : TestEnv - /home/pippo/work/lisa/results_latest\n" - ] - } - ], - "source": [ - "# Initialize a test environment using:\n", - "# the provided target configuration (my_target_conf)\n", - "# the provided test configuration (my_test_conf)\n", - "te = TestEnv(target_conf=my_target_conf, test_conf=my_tests_conf)\n", - "target = te.target" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Support Functions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This set of support functions will help us running the benchmark using different CPUFreq governors." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def set_performance():\n", - " target.cpufreq.set_all_governors('performance')\n", - "\n", - "def set_powersave():\n", - " target.cpufreq.set_all_governors('powersave')\n", - "\n", - "def set_interactive():\n", - " target.cpufreq.set_all_governors('interactive')\n", - "\n", - "def set_sched():\n", - " target.cpufreq.set_all_governors('sched')\n", - "\n", - "def set_ondemand():\n", - " target.cpufreq.set_all_governors('ondemand')\n", - " \n", - " for cpu in target.list_online_cpus():\n", - " tunables = target.cpufreq.get_governor_tunables(cpu)\n", - " target.cpufreq.set_governor_tunables(\n", - " cpu,\n", - " 'ondemand',\n", - " **{'sampling_rate' : tunables['sampling_rate_min']}\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# CPUFreq configurations to test\n", - "confs = {\n", - " 'performance' : {\n", - " 'label' : 'prf',\n", - " 'set' : set_performance,\n", - " },\n", - " #'powersave' : {\n", - " # 'label' : 'pws',\n", - " # 'set' : set_powersave,\n", - " #},\n", - " 'interactive' : {\n", - " 'label' : 'int',\n", - " 'set' : set_interactive,\n", - " },\n", - " #'sched' : {\n", - " # 'label' : 'sch',\n", - " # 'set' : set_sched,\n", - " #},\n", - " #'ondemand' : {\n", - " # 'label' : 'odm',\n", - " # 'set' : set_ondemand,\n", - " #}\n", - "}\n", - "\n", - "# The set of results for each comparison test\n", - "results = {}" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def check_packages(pkgname):\n", - " try:\n", - " output = target.execute('pm list packages -f | grep -i {}'.format(pkgname))\n", - " except Exception:\n", - " raise RuntimeError('Package: [{}] not availabe on target'.format(pkgname))\n", - "\n", - "# Check for specified PKG name being available on target\n", - "check_packages('com.futuremark.pcmark.android.benchmark')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def pcmark_run(exp_dir):\n", - " # Unlock device screen (assume no password required)\n", - " target.execute('input keyevent 82')\n", - " # Start PCMark on the target device\n", - " target.execute('monkey -p com.futuremark.pcmark.android.benchmark -c android.intent.category.LAUNCHER 1')\n", - " # Wait few seconds to make sure the app is loaded\n", - " sleep(5)\n", - " \n", - " # Flush entire log\n", - " target.clear_logcat()\n", - " \n", - " # Run performance workload (assume screen is vertical)\n", - " target.execute('input tap 750 1450')\n", - " # Wait for completion (7 minutes in total) and collect log\n", - " log_file = os.path.join(exp_dir, 'log.txt')\n", - " # Wait 5 minutes\n", - " sleep(300)\n", - " # Start collecting the log\n", - " with open(log_file, 'w') as log:\n", - " logcat = Popen(['adb logcat', 'com.futuremark.pcmandroid.VirtualMachineState:*', '*:S'],\n", - " stdout=log,\n", - " shell=True)\n", - " # Wait additional two minutes for benchmark to complete\n", - " sleep(120)\n", - "\n", - " # Terminate logcat\n", - " logcat.kill()\n", - "\n", - " # Get scores from logcat\n", - " score_file = os.path.join(exp_dir, 'score.txt')\n", - " os.popen('grep -o \"PCMA_.*_SCORE .*\" {} | sed \"s/ = / /g\" | sort -u > {}'.format(log_file, score_file))\n", - " \n", - " # Close application\n", - " target.execute('am force-stop com.futuremark.pcmark.android.benchmark')\n", - " \n", - " return score_file" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def experiment(governor, exp_dir):\n", - " os.system('mkdir -p {}'.format(exp_dir));\n", - "\n", - " logging.info('------------------------')\n", - " logging.info('Run workload using %s governor', governor)\n", - " confs[governor]['set']()\n", - "\n", - " ### Run the benchmark ###\n", - " score_file = pcmark_run(exp_dir)\n", - " \n", - " # Save the score as a dictionary\n", - " scores = dict()\n", - " with open(score_file, 'r') as f:\n", - " lines = f.readlines()\n", - " for l in lines:\n", - " info = l.split()\n", - " scores.update({info[0] : float(info[1])})\n", - " \n", - " # return all the experiment data\n", - " return {\n", - " 'dir' : exp_dir,\n", - " 'scores' : scores,\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run PCMark and collect scores" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-04-04 09:57:04,879 INFO : ------------------------\n", - "2016-04-04 09:57:04,881 INFO : Run workload using performance governor\n", - "2016-04-04 10:04:13,684 INFO : ------------------------\n", - "2016-04-04 10:04:13,685 INFO : Run workload using interactive governor\n" - ] - } - ], - "source": [ - "# Run the benchmark in all the configured governors\n", - "for governor in confs:\n", - " test_dir = os.path.join(te.res_dir, governor)\n", - " res = experiment(governor, test_dir)\n", - " results[governor] = copy.deepcopy(res)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "After running the benchmark for the specified governors we can show the scores" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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interactiveperformance
PCMA_PHOTO_EDITING_SCORE7847.5955879281.817920
PCMA_VIDEO_PLAYBACK_SCORE3519.1559753484.256511
PCMA_WEB_SCORE6201.2261466444.737547
PCMA_WORK_SCORE4676.9507464934.220982
PCMA_WRITING_SCORE2793.8204072843.976358
\n", - "
" - ], - "text/plain": [ - " interactive performance\n", - "PCMA_PHOTO_EDITING_SCORE 7847.595587 9281.817920\n", - "PCMA_VIDEO_PLAYBACK_SCORE 3519.155975 3484.256511\n", - "PCMA_WEB_SCORE 6201.226146 6444.737547\n", - "PCMA_WORK_SCORE 4676.950746 4934.220982\n", - "PCMA_WRITING_SCORE 2793.820407 2843.976358" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create results DataFrame\n", - "data = {}\n", - "for governor in confs:\n", - " data[governor] = {}\n", - " for score_name, score in results[governor]['scores'].iteritems():\n", - " data[governor][score_name] = score\n", - "\n", - "df = pd.DataFrame.from_dict(data)\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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AAKiW0AoAAEC1hFYAAACqJbQCAABQLaEVAACAagmtAAAAVEtoBQAAoFpCKwAA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\ncHzLtUYUSg3U3xdzHkkcsusyrpOG0yyS8e8C4PM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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df.plot(kind='bar', rot=45, figsize=(16,8),\n", - " title='PCMark scores vs SchedFreq governors');" - ] - } - ], - "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" - }, - "toc": { - "toc_cell": false, - "toc_number_sections": true, - "toc_threshold": 6, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/ipynb/android/workloads/Android_Recents_Fling.ipynb b/ipynb/android/workloads/Android_Recents_Fling.ipynb deleted file mode 100644 index 8b39a49f..00000000 --- a/ipynb/android/workloads/Android_Recents_Fling.ipynb +++ /dev/null @@ -1,595 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# EAS Testing - Recents Fling on Android" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The goal of this experiment is to collect frame statistics while swiping up and down tabs of recently opened applications on a Nexus N5X running Android with an EAS kernel. This process is name **Recents Fling**. The Analysis phase will consist in comparing EAS with other schedulers, that is comparing *sched* governor with:\n", - "\n", - " - interactive\n", - " - performance\n", - " - powersave\n", - " - ondemand\n", - " \n", - "For this experiment it is recommended to open many applications so that we can swipe over more recently opened applications." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import logging\n", - "from conf import LisaLogging\n", - "LisaLogging.setup()" - ] - }, - { - "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": [ - "%pylab inline\n", - "\n", - "import os\n", - "from time import sleep\n", - "\n", - "# Support to access the remote target\n", - "import devlib\n", - "from env import TestEnv\n", - "\n", - "from devlib.utils.android import adb_command\n", - "\n", - "# Support for trace events analysis\n", - "from trace import Trace\n", - "\n", - "# Suport for FTrace events parsing and visualization\n", - "import trappy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test Environment set up" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Devlib requires the ANDROID_HOME environment variable configured to point to your local installation of the Android SDK. If you have not this variable configured in the shell used to start the notebook server, you need to run the next cell to define where your Android SDK is installed." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import os\n", - "os.environ['ANDROID_HOME'] = '/ext/android-sdk-linux/'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In case more than one Android device are conencted to the host, you must specify the ID of the device you want to target in `my_target_conf`. Run `adb devices` on your host to get the ID." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup a target configuration\n", - "my_conf = {\n", - " \n", - " # Target platform and board\n", - " \"platform\" : 'android',\n", - "\n", - " # Device ID\n", - " # \"device\" : \"0123456789abcdef\",\n", - "\n", - " # Folder where all the results will be collected\n", - " \"results_dir\" : \"Android_RecentsFling\",\n", - " \n", - " # Define devlib modules to load\n", - " \"modules\" : [\n", - " 'cpufreq' # enable CPUFreq support\n", - " ],\n", - "\n", - " # FTrace events to collect for all the tests configuration which have\n", - " # the \"ftrace\" flag enabled\n", - " \"ftrace\" : {\n", - " \"events\" : [\n", - " \"sched_switch\",\n", - " \"sched_load_avg_cpu\",\n", - " \"cpu_frequency\",\n", - " \"cpu_capacity\"\n", - " ],\n", - " \"buffsize\" : 10 * 1024,\n", - " },\n", - "\n", - " # Tools required by the experiments\n", - " \"tools\" : [ 'trace-cmd' ],\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-04-27 10:03:27,438 INFO : Target - Using base path: /home/derkling/Code/lisa\n", - "2016-04-27 10:03:27,439 INFO : Target - Loading custom (inline) target configuration\n", - "2016-04-27 10:03:27,440 INFO : Target - Devlib modules to load: ['cpufreq']\n", - "2016-04-27 10:03:27,441 INFO : Target - Connecting Android target [DEFAULT]\n", - "2016-04-27 10:03:29,160 INFO : Target - Initializing target workdir:\n", - "2016-04-27 10:03:29,161 INFO : Target - /data/local/tmp/devlib-target\n", - "2016-04-27 10:03:32,592 INFO : Target - Topology:\n", - "2016-04-27 10:03:32,593 INFO : Target - [[0, 1], [2, 3]]\n", - "2016-04-27 10:03:33,947 WARNING : Event [sched_load_avg_cpu] not available for tracing\n", - "2016-04-27 10:03:33,949 WARNING : Event [cpu_capacity] not available for tracing\n", - "2016-04-27 10:03:33,950 INFO : FTrace - Enabled tracepoints:\n", - "2016-04-27 10:03:33,950 INFO : FTrace - sched_switch\n", - "2016-04-27 10:03:33,951 INFO : FTrace - sched_load_avg_cpu\n", - "2016-04-27 10:03:33,951 INFO : FTrace - cpu_frequency\n", - "2016-04-27 10:03:33,952 INFO : FTrace - cpu_capacity\n", - "2016-04-27 10:03:33,953 WARNING : TestEnv - Wipe previous contents of the results folder:\n", - "2016-04-27 10:03:33,953 WARNING : TestEnv - /home/derkling/Code/lisa/results/Android_RecentsFling\n", - "2016-04-27 10:03:33,963 INFO : TestEnv - Set results folder to:\n", - "2016-04-27 10:03:33,963 INFO : TestEnv - /home/derkling/Code/lisa/results/Android_RecentsFling\n", - "2016-04-27 10:03:33,964 INFO : TestEnv - Experiment results available also in:\n", - "2016-04-27 10:03:33,964 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n" - ] - } - ], - "source": [ - "# Initialize a test environment using:\n", - "te = TestEnv(my_conf)\n", - "target = te.target" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Support Functions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This set of support functions will help us running the benchmark using different CPUFreq governors." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def set_performance():\n", - " target.cpufreq.set_all_governors('performance')\n", - "\n", - "def set_powersave():\n", - " target.cpufreq.set_all_governors('powersave')\n", - "\n", - "def set_interactive():\n", - " target.cpufreq.set_all_governors('interactive')\n", - "\n", - "def set_sched():\n", - " target.cpufreq.set_all_governors('sched')\n", - "\n", - "def set_ondemand():\n", - " target.cpufreq.set_all_governors('ondemand')\n", - " \n", - " for cpu in target.list_online_cpus():\n", - " tunables = target.cpufreq.get_governor_tunables(cpu)\n", - " target.cpufreq.set_governor_tunables(\n", - " cpu,\n", - " 'ondemand',\n", - " **{'sampling_rate' : tunables['sampling_rate_min']}\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# CPUFreq configurations to test\n", - "confs = {\n", - " 'performance' : {\n", - " 'label' : 'prf',\n", - " 'set' : set_performance,\n", - " },\n", - " #'powersave' : {\n", - " # 'label' : 'pws',\n", - " # 'set' : set_powersave,\n", - " #},\n", - " 'interactive' : {\n", - " 'label' : 'int',\n", - " 'set' : set_interactive,\n", - " },\n", - " #'sched' : {\n", - " # 'label' : 'sch',\n", - " # 'set' : set_sched,\n", - " #},\n", - " #'ondemand' : {\n", - " # 'label' : 'odm',\n", - " # 'set' : set_ondemand,\n", - " #}\n", - "}\n", - "\n", - "# The set of results for each comparison test\n", - "results = {}" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def open_apps(n):\n", - " \"\"\"\n", - " Open `n` apps on the device\n", - " \n", - " :param n: number of apps to open\n", - " :type n: int\n", - " \"\"\"\n", - " # Get a list of third-party packages\n", - " android_version = target.getprop('ro.build.version.release')\n", - " if android_version >= 'N':\n", - " packages = target.execute('cmd package list packages | cut -d: -f 2')\n", - " packages = packages.splitlines()\n", - " else:\n", - " packages = target.execute('pm list packages -3 | cut -d: -f 2')\n", - " packages = packages.splitlines()\n", - "\n", - " # As a safe fallback let's use a list of standard Android AOSP apps which are always available\n", - " if len(packages) < 8:\n", - " packages = [\n", - " 'com.android.messaging',\n", - " 'com.android.calendar',\n", - " 'com.android.settings',\n", - " 'com.android.calculator2',\n", - " 'com.android.email',\n", - " 'com.android.music',\n", - " 'com.android.deskclock',\n", - " 'com.android.contacts',\n", - " ]\n", - " \n", - " LAUNCH_CMD = 'monkey -p {} -c android.intent.category.LAUNCHER 1 '\n", - " \n", - " if n > len(packages):\n", - " n = len(packages)\n", - " \n", - " logging.info('Trying to open %d apps...', n)\n", - " started = 0\n", - " for app in packages:\n", - " logging.debug(' Launching %s', app)\n", - " try:\n", - " target.execute(LAUNCH_CMD.format(app))\n", - " started = started + 1\n", - " logging.info(' %2d starting %s...', started, app)\n", - " except Exception:\n", - " pass\n", - " if started >= n:\n", - " break\n", - " \n", - " # Close Recents\n", - " target.execute('input keyevent KEYCODE_HOME')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def recentsfling_run(exp_dir):\n", - " # Open Recents on the target device\n", - " target.execute('input keyevent KEYCODE_APP_SWITCH')\n", - " # Allow the activity to start\n", - " sleep(5)\n", - " # Reset framestats collection\n", - " target.execute('dumpsys gfxinfo --reset')\n", - " \n", - " w, h = target.screen_resolution\n", - " x = w/2\n", - " yl = int(0.2*h)\n", - " yh = int(0.9*h)\n", - " \n", - " logging.info('Start Swiping Recents')\n", - " for i in range(5):\n", - " # Simulate two fast UP and DOWN swipes\n", - " target.execute('input swipe {} {} {} {} 50'.format(x, yl, x, yh))\n", - " sleep(0.3)\n", - " target.execute('input swipe {} {} {} {} 50'.format(x, yh, x, yl))\n", - " sleep(0.7)\n", - " logging.info('Swiping Recents Completed')\n", - " \n", - " # Get frame stats\n", - " framestats_file = os.path.join(exp_dir, \"framestats.txt\")\n", - " adb_command(target.adb_name, 'shell dumpsys gfxinfo com.android.systemui > {}'.format(framestats_file))\n", - " \n", - " # Close Recents\n", - " target.execute('input keyevent KEYCODE_HOME')\n", - "\n", - " return framestats_file" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def experiment(governor, exp_dir):\n", - " os.system('mkdir -p {}'.format(exp_dir));\n", - "\n", - " logging.info('------------------------')\n", - " logging.info('Run workload using %s governor', governor)\n", - " confs[governor]['set']()\n", - " \n", - " # Start FTrace\n", - " te.ftrace.start()\n", - " \n", - " ### Run the benchmark ###\n", - " framestats_file = recentsfling_run(exp_dir)\n", - " \n", - " # Stop FTrace\n", - " te.ftrace.stop() \n", - "\n", - " # Collect and keep track of the trace\n", - " trace_file = os.path.join(exp_dir, 'trace.dat')\n", - " te.ftrace.get_trace(trace_file)\n", - " \n", - " # Parse trace\n", - " tr = Trace(te.platform, exp_dir,\n", - " events=my_conf['ftrace']['events'])\n", - " \n", - " # return all the experiment data\n", - " return {\n", - " 'dir' : exp_dir,\n", - " 'framestats_file' : framestats_file,\n", - " 'trace_file' : trace_file,\n", - " 'ftrace' : tr.ftrace,\n", - " 'trace' : tr\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run Flinger" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare Environment" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "N_APPS = 20" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-04-27 10:04:15,735 INFO : Trying to open 20 apps...\n", - "2016-04-27 10:04:19,062 INFO : 1 starting com.android.documentsui...\n", - "2016-04-27 10:04:20,636 INFO : 2 starting com.android.quicksearchbox...\n", - "2016-04-27 10:04:22,291 INFO : 3 starting com.android.messaging...\n", - "2016-04-27 10:04:26,125 INFO : 4 starting com.android.contacts...\n", - "2016-04-27 10:04:30,385 INFO : 5 starting com.android.calendar...\n", - "2016-04-27 10:04:33,122 INFO : 6 starting com.htc.android.ssdtest...\n", - "2016-04-27 10:04:37,992 INFO : 7 starting com.android.dialer...\n", - "2016-04-27 10:04:38,592 INFO : 8 starting com.android.gallery3d...\n", - "2016-04-27 10:04:45,168 INFO : 9 starting com.android.settings...\n", - "2016-04-27 10:04:45,718 INFO : 10 starting com.android.calculator2...\n", - "2016-04-27 10:04:46,798 INFO : 11 starting com.android.email...\n", - "2016-04-27 10:04:47,373 INFO : 12 starting com.android.music...\n", - "2016-04-27 10:04:49,977 INFO : 13 starting com.android.deskclock...\n", - "2016-04-27 10:04:52,633 INFO : 14 starting com.android.development...\n" - ] - } - ], - "source": [ - "open_apps(N_APPS)\n", - "\n", - "# Give apps enough time to open\n", - "sleep(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run workload and collect traces" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-04-27 10:05:16,261 INFO : ------------------------\n", - "2016-04-27 10:05:16,261 INFO : Run workload using performance governor\n", - "2016-04-27 10:05:23,910 INFO : Start Swiping Recents\n", - "2016-04-27 10:05:34,043 INFO : Swiping Recents Completed\n", - "2016-04-27 10:05:37,908 INFO : Parsing FTrace format...\n", - "2016-04-27 10:05:41,305 INFO : Collected events spans a 16.850 [s] time interval\n", - "2016-04-27 10:05:41,306 INFO : Set plots time range to (0.000000, 16.849682)[s]\n", - "2016-04-27 10:05:41,659 INFO : ------------------------\n", - "2016-04-27 10:05:41,660 INFO : Run workload using interactive governor\n", - "2016-04-27 10:05:49,387 INFO : Start Swiping Recents\n", - "2016-04-27 10:06:00,339 INFO : Swiping Recents Completed\n", - "2016-04-27 10:06:05,640 INFO : Parsing FTrace format...\n", - "2016-04-27 10:06:08,702 INFO : Collected events spans a 17.899 [s] time interval\n", - "2016-04-27 10:06:08,703 INFO : Set plots time range to (0.000000, 17.898734)[s]\n" - ] - } - ], - "source": [ - "# Unlock device screen (assume no password required)\n", - "target.execute('input keyevent 82')\n", - "\n", - "# Run the benchmark in all the configured governors\n", - "for governor in confs:\n", - " test_dir = os.path.join(te.res_dir, governor)\n", - " results[governor] = experiment(governor, test_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "# UI Performance Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Frame Statistics for PERFORMANCE governor\n", - "Stats since: 31757612331ns\n", - "Total frames rendered: 1052\n", - "Janky frames: 124 (11.79%)\n", - "50th percentile: 9ms\n", - "90th percentile: 17ms\n", - "95th percentile: 23ms\n", - "99th percentile: 34ms\n", - "\n", - "Frame Statistics for INTERACTIVE governor\n", - "Stats since: 31757612331ns\n", - "Total frames rendered: 1527\n", - "Janky frames: 230 (15.06%)\n", - "50th percentile: 10ms\n", - "90th percentile: 19ms\n", - "95th percentile: 23ms\n", - "99th percentile: 34ms\n", - "\n" - ] - } - ], - "source": [ - "for governor in confs:\n", - " framestats_file = results[governor]['framestats_file']\n", - " print \"Frame Statistics for {} governor\".format(governor.upper())\n", - " !sed '/Stats since/,/99th/!d;/99th/q' $framestats_file\n", - " print \"\"" - ] - } - ], - "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" - }, - "toc": { - "toc_cell": false, - "toc_number_sections": true, - "toc_threshold": 6, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/ipynb/android/workloads/Android_YouTube.ipynb b/ipynb/android/workloads/Android_YouTube.ipynb deleted file mode 100644 index e03dfcbd..00000000 --- a/ipynb/android/workloads/Android_YouTube.ipynb +++ /dev/null @@ -1,563 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# EAS Testing - YouTube on Android" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The goal of this experiment is to run Youtube videos on a Nexus N5X running Android with an EAS kernel and collect results. The Analysis phase will consist in comparing EAS with other schedulers, that is comparing *sched* governor with:\n", - "\n", - " - interactive\n", - " - performance\n", - " - powersave\n", - " - ondemand" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-06 19:46:08,105 INFO : root : Using LISA logging configuration:\n", - "2016-12-06 19:46:08,106 INFO : root : /home/vagrant/lisa/logging.conf\n" - ] - } - ], - "source": [ - "import logging\n", - "from conf import LisaLogging\n", - "LisaLogging.setup()" - ] - }, - { - "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": [ - "%pylab inline\n", - "\n", - "import os\n", - "from time import sleep\n", - "\n", - "# Support to access the remote target\n", - "import devlib\n", - "from env import TestEnv\n", - "\n", - "from devlib.utils.android import adb_command\n", - "\n", - "# Import support for Android devices\n", - "from android import System\n", - "\n", - "# Support for trace events analysis\n", - "from trace import Trace\n", - "\n", - "# Suport for FTrace events parsing and visualization\n", - "import trappy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test Environment set up" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In case more than one Android device are conencted to the host, you must specify the ID of the device you want to target in `my_target_conf`. Run `adb devices` on your host to get the ID." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup a target configuration\n", - "my_target_conf = {\n", - " \n", - " # Target platform and board\n", - " \"platform\" : 'android',\n", - "\n", - " # Add target support\n", - " \"board\" : 'pixel',\n", - " \n", - " # Device ID\n", - " \"device\" : \"HT6670300102\",\n", - " \n", - " # ANDROID_HOME\n", - " \"ANDROID_HOME\" : '/home/vagrant/lisa/tools/android-sdk-linux/',\n", - " \n", - " # Define devlib modules to load\n", - " \"modules\" : [\n", - " 'cpufreq' # enable CPUFreq support\n", - " ],\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "my_tests_conf = {\n", - "\n", - " # Folder where all the results will be collected\n", - " \"results_dir\" : \"Android_Youtube\",\n", - "\n", - " # Platform configurations to test\n", - " \"confs\" : [\n", - " {\n", - " \"tag\" : \"youtube\",\n", - " \"flags\" : \"ftrace\", # Enable FTrace events\n", - " \"sched_features\" : \"ENERGY_AWARE\", # enable EAS\n", - " },\n", - " ],\n", - " \n", - " # FTrace events to collect for all the tests configuration which have\n", - " # the \"ftrace\" flag enabled\n", - " \"ftrace\" : {\n", - " \"events\" : [\n", - " \"sched_switch\",\n", - " \"sched_load_avg_cpu\",\n", - " \"cpu_frequency\",\n", - " \"cpu_capacity\"\n", - " ],\n", - " \"buffsize\" : 10 * 1024,\n", - " },\n", - " \n", - " # Tools required by the experiments\n", - " \"tools\" : [ 'trace-cmd' ],\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "adbd is already running as root\r\n" - ] - } - ], - "source": [ - "# Ensure ADB has root priviledges, which are required by systrace\n", - "!adb root" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-06 19:46:11,262 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", - "2016-12-06 19:46:11,263 INFO : TestEnv : Loading custom (inline) target configuration\n", - "2016-12-06 19:46:11,264 INFO : TestEnv : Loading custom (inline) test configuration\n", - "2016-12-06 19:46:11,264 INFO : TestEnv : External tools using:\n", - "2016-12-06 19:46:11,264 INFO : TestEnv : ANDROID_HOME: /home/vagrant/lisa/tools/android-sdk-linux/\n", - "2016-12-06 19:46:11,265 INFO : TestEnv : CATAPULT_HOME: /home/vagrant/lisa/tools/catapult\n", - "2016-12-06 19:46:11,265 INFO : TestEnv : Loading board:\n", - "2016-12-06 19:46:11,266 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/pixel.json\n", - "2016-12-06 19:46:11,266 INFO : TestEnv : Devlib modules to load: [u'bl', u'cpufreq']\n", - "2016-12-06 19:46:11,267 INFO : TestEnv : Connecting Android target [HT6670300102]\n", - "2016-12-06 19:46:11,267 INFO : TestEnv : Connection settings:\n", - "2016-12-06 19:46:11,267 INFO : TestEnv : {'device': 'HT6670300102'}\n", - "2016-12-06 19:46:11,355 INFO : android : ls command is set to ls -1\n", - "2016-12-06 19:46:12,105 INFO : TestEnv : Initializing target workdir:\n", - "2016-12-06 19:46:12,108 INFO : TestEnv : /data/local/tmp/devlib-target\n", - "2016-12-06 19:46:14,169 INFO : TestEnv : Topology:\n", - "2016-12-06 19:46:14,171 INFO : TestEnv : [[0, 1], [2, 3]]\n", - "2016-12-06 19:46:14,400 INFO : TestEnv : Loading default EM:\n", - "2016-12-06 19:46:14,400 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/pixel.json\n", - "2016-12-06 19:46:15,115 INFO : TestEnv : Enabled tracepoints:\n", - "2016-12-06 19:46:15,116 INFO : TestEnv : sched_switch\n", - "2016-12-06 19:46:15,116 INFO : TestEnv : sched_load_avg_cpu\n", - "2016-12-06 19:46:15,117 INFO : TestEnv : cpu_frequency\n", - "2016-12-06 19:46:15,117 INFO : TestEnv : cpu_capacity\n", - "2016-12-06 19:46:15,118 WARNING : TestEnv : Wipe previous contents of the results folder:\n", - "2016-12-06 19:46:15,118 WARNING : TestEnv : /home/vagrant/lisa/results/Android_Youtube\n", - "2016-12-06 19:46:15,129 INFO : TestEnv : Set results folder to:\n", - "2016-12-06 19:46:15,129 INFO : TestEnv : /home/vagrant/lisa/results/Android_Youtube\n", - "2016-12-06 19:46:15,130 INFO : TestEnv : Experiment results available also in:\n", - "2016-12-06 19:46:15,130 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" - ] - } - ], - "source": [ - "# Initialize a test environment using:\n", - "# the provided target configuration (my_target_conf)\n", - "# the provided test configuration (my_test_conf)\n", - "te = TestEnv(target_conf=my_target_conf, test_conf=my_tests_conf)\n", - "target = te.target" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Support Functions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This set of support functions will help us running the benchmark using different CPUFreq governors." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def set_performance():\n", - " target.cpufreq.set_all_governors('performance')\n", - "\n", - "def set_powersave():\n", - " target.cpufreq.set_all_governors('powersave')\n", - "\n", - "def set_interactive():\n", - " target.cpufreq.set_all_governors('interactive')\n", - "\n", - "def set_sched():\n", - " target.cpufreq.set_all_governors('sched')\n", - "\n", - "def set_ondemand():\n", - " target.cpufreq.set_all_governors('ondemand')\n", - " \n", - " for cpu in target.list_online_cpus():\n", - " tunables = target.cpufreq.get_governor_tunables(cpu)\n", - " target.cpufreq.set_governor_tunables(\n", - " cpu,\n", - " 'ondemand',\n", - " **{'sampling_rate' : tunables['sampling_rate_min']}\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# CPUFreq configurations to test\n", - "confs = {\n", - " 'performance' : {\n", - " 'label' : 'prf',\n", - " 'set' : set_performance,\n", - " },\n", - " #'powersave' : {\n", - " # 'label' : 'pws',\n", - " # 'set' : set_powersave,\n", - " #},\n", - " 'interactive' : {\n", - " 'label' : 'int',\n", - " 'set' : set_interactive,\n", - " },\n", - " #'sched' : {\n", - " # 'label' : 'sch',\n", - " # 'set' : set_sched,\n", - " #},\n", - " #'ondemand' : {\n", - " # 'label' : 'odm',\n", - " # 'set' : set_ondemand,\n", - " #}\n", - "}\n", - "\n", - "# The set of results for each comparison test\n", - "results = {}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "YOUTUBE_CMD = 'shell dumpsys gfxinfo com.google.android.youtube > {}'\n", - "\n", - "def youtube_run(exp_dir, video_url, video_duration_s):\n", - " # Unlock device screen (assume no password required)\n", - " target.execute('input keyevent 82')\n", - " # Press Back button to be sure we run the video from the start\n", - " target.execute('input keyevent KEYCODE_BACK')\n", - "\n", - " # Start YouTube video on the target device\n", - " target.execute('am start -a android.intent.action.VIEW \"{}\"'.format(video_url))\n", - " # Allow the activity to start\n", - " sleep(3)\n", - " # Reset framestats collection\n", - " target.execute('dumpsys gfxinfo --reset')\n", - " # Wait until the end of the video\n", - " sleep(video_duration_s)\n", - " \n", - " # Get frame stats\n", - " framestats_file = os.path.join(exp_dir, \"framestats.txt\")\n", - " adb_command(target.adb_name, YOUTUBE_CMD.format(framestats_file))\n", - "\n", - " # Close application\n", - " target.execute('am force-stop com.google.android.youtube')\n", - "\n", - " # Clear application data\n", - " target.execute('pm clear com.google.android.youtube')\n", - "\n", - " return framestats_file" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def experiment(governor, exp_dir, collect='ftrace', trace_time=30):\n", - " os.system('mkdir -p {}'.format(exp_dir));\n", - "\n", - " logging.info('------------------------')\n", - " logging.info('Run workload using %s governor', governor)\n", - " confs[governor]['set']()\n", - "\n", - " # Start the required tracing command\n", - " if 'ftrace' in collect:\n", - " # Start FTrace and Energy monitoring\n", - " te.ftrace.start()\n", - " elif 'systrace' in collect:\n", - " # Start systrace\n", - " trace_file = os.path.join(exp_dir, 'trace.html')\n", - " systrace_output = System.systrace_start(te, trace_file, trace_time)\n", - "\n", - " ### Run the benchmark ###\n", - " framestats_file = youtube_run(exp_dir, \"https://youtu.be/XSGBVzeBUbk?t=45s\", trace_time)\n", - "\n", - " # Stop the required trace command\n", - " if 'ftrace' in collect:\n", - " te.ftrace.stop()\n", - " # Collect and keep track of the trace\n", - " trace_file = os.path.join(exp_dir, 'trace.dat')\n", - " te.ftrace.get_trace(trace_file)\n", - " elif 'systrace' in collect:\n", - " if systrace_output:\n", - " logging.info('Waiting systrace report [%s]...', trace_file)\n", - " systrace_output.wait()\n", - " else:\n", - " logging.warning('Systrace is not running!') \n", - "\n", - " # Parse trace\n", - " tr = Trace(te.platform, trace_file,\n", - " events=my_tests_conf['ftrace']['events'])\n", - "\n", - " # return all the experiment data\n", - " return {\n", - " 'dir' : exp_dir,\n", - " 'framestats_file' : framestats_file,\n", - " 'trace' : trace_file,\n", - " 'ftrace' : tr.ftrace\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run experiments and collect traces" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-06 19:46:21,457 INFO : root : ------------------------\n", - "2016-12-06 19:46:21,458 INFO : root : Run workload using performance governor\n", - "2016-12-06 19:46:21,556 INFO : System : SysTrace: /home/vagrant/lisa/tools/catapult/systrace/systrace/run_systrace.py -e HT6670300102 -o /home/vagrant/lisa/results/Android_Youtube/performance/trace.html gfx view sched freq idle -t 15\n", - "2016-12-06 19:46:42,226 INFO : root : Waiting systrace report [/home/vagrant/lisa/results/Android_Youtube/performance/trace.html]...\n", - "2016-12-06 19:46:43,203 INFO : Trace : Parsing SysTrace format...\n", - "2016-12-06 19:46:48,092 INFO : Trace : Collected events spans a 9.888 [s] time interval\n", - "2016-12-06 19:46:48,092 INFO : Trace : Set plots time range to (0.000000, 9.888454)[s]\n", - "2016-12-06 19:46:48,093 INFO : Analysis : Registering trace analysis modules:\n", - "2016-12-06 19:46:48,094 INFO : Analysis : tasks\n", - "2016-12-06 19:46:48,094 INFO : Analysis : status\n", - "2016-12-06 19:46:48,095 INFO : Analysis : frequency\n", - "2016-12-06 19:46:48,096 INFO : Analysis : cpus\n", - "2016-12-06 19:46:48,097 INFO : Analysis : latency\n", - "2016-12-06 19:46:48,097 INFO : Analysis : idle\n", - "2016-12-06 19:46:48,098 INFO : Analysis : functions\n", - "2016-12-06 19:46:48,098 INFO : Analysis : eas\n", - "2016-12-06 19:46:48,208 INFO : root : ------------------------\n", - "2016-12-06 19:46:48,209 INFO : root : Run workload using interactive governor\n", - "2016-12-06 19:46:48,316 INFO : System : SysTrace: /home/vagrant/lisa/tools/catapult/systrace/systrace/run_systrace.py -e HT6670300102 -o /home/vagrant/lisa/results/Android_Youtube/interactive/trace.html gfx view sched freq idle -t 15\n", - "2016-12-06 19:47:09,175 INFO : root : Waiting systrace report [/home/vagrant/lisa/results/Android_Youtube/interactive/trace.html]...\n", - "2016-12-06 19:47:10,185 INFO : Trace : Parsing SysTrace format...\n", - "2016-12-06 19:47:13,573 INFO : Trace : Platform clusters verified to be Frequency coherent\n", - "2016-12-06 19:47:15,675 INFO : Trace : Collected events spans a 8.481 [s] time interval\n", - "2016-12-06 19:47:15,675 INFO : Trace : Set plots time range to (0.000000, 8.480988)[s]\n", - "2016-12-06 19:47:15,676 INFO : Analysis : Registering trace analysis modules:\n", - "2016-12-06 19:47:15,676 INFO : Analysis : tasks\n", - "2016-12-06 19:47:15,677 INFO : Analysis : status\n", - "2016-12-06 19:47:15,677 INFO : Analysis : frequency\n", - "2016-12-06 19:47:15,678 INFO : Analysis : cpus\n", - "2016-12-06 19:47:15,678 INFO : Analysis : latency\n", - "2016-12-06 19:47:15,679 INFO : Analysis : idle\n", - "2016-12-06 19:47:15,680 INFO : Analysis : functions\n", - "2016-12-06 19:47:15,680 INFO : Analysis : eas\n" - ] - } - ], - "source": [ - "# Run the benchmark in all the configured governors\n", - "for governor in confs:\n", - " test_dir = os.path.join(te.res_dir, governor)\n", - " results[governor] = experiment(governor, test_dir,\n", - " collect='systrace', trace_time=15)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "# UI Performance Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Frame Statistics for PERFORMANCE governor\n", - "Stats since: 107266916813060ns\n", - "Total frames rendered: 747\n", - "Janky frames: 44 (5.89%)\n", - "50th percentile: 5ms\n", - "90th percentile: 8ms\n", - "95th percentile: 19ms\n", - "99th percentile: 113ms\n", - "\n", - "Frame Statistics for INTERACTIVE governor\n", - "Stats since: 107266916813060ns\n", - "Total frames rendered: 942\n", - "Janky frames: 60 (6.37%)\n", - "50th percentile: 5ms\n", - "90th percentile: 9ms\n", - "95th percentile: 20ms\n", - "99th percentile: 113ms\n", - "\n" - ] - } - ], - "source": [ - "for governor in confs:\n", - " framestats_file = results[governor]['framestats_file']\n", - " print \"Frame Statistics for {} governor\".format(governor.upper())\n", - " !sed '/Stats since/,/99th/!d;/99th/q' $framestats_file\n", - " print \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "trace_file = results['interactive']['trace']\n", - "!xdg-open {trace_file}" - ] - } - ], - "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": "2.7.6" - }, - "toc": { - "toc_cell": false, - "toc_number_sections": true, - "toc_threshold": 6, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/ipynb/devlib/examples/cgroups.ipynb b/ipynb/devlib/examples/cgroups.ipynb deleted file mode 100644 index 344552ec..00000000 --- a/ipynb/devlib/examples/cgroups.ipynb +++ /dev/null @@ -1,1267 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import logging\n", - "from conf import LisaLogging\n", - "LisaLogging.setup()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import json\n", - "import operator\n", - "\n", - "import devlib\n", - "import trappy\n", - "import bart\n", - "\n", - "from bart.sched.SchedMultiAssert import SchedMultiAssert\n", - "from wlgen import RTA, Periodic" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Target connection" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import os\n", - "os.environ['ANDROID_HOME'] = '/ext/android-sdk-linux/'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "04:23:46 INFO : Target - Using base path: /home/derkling/Code/lisa\n", - "04:23:46 INFO : Target - Loading custom (inline) target configuration\n", - "04:23:46 INFO : Target - Devlib modules to load: ['bl', 'cpufreq', 'cgroups']\n", - "04:23:46 INFO : Target - Connecting linux target:\n", - "04:23:46 INFO : Target - username : root\n", - "04:23:46 INFO : Target - host : 192.168.0.1\n", - "04:23:46 INFO : Target - password : \n", - "04:23:46 INFO : Target - Connection settings:\n", - "04:23:46 INFO : Target - {'username': 'root', 'host': '192.168.0.1', 'password': ''}\n", - "04:23:50 INFO : Target - Initializing target workdir:\n", - "04:23:50 INFO : Target - /root/devlib-target\n", - "04:24:16 INFO : Target - Topology:\n", - "04:24:16 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n", - "04:24:18 INFO : Platform - Loading default EM:\n", - "04:24:18 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/juno.json\n", - "04:24:19 INFO : FTrace - Enabled tracepoints:\n", - "04:24:19 INFO : FTrace - sched_switch\n", - "04:24:19 WARNING : Target - Using configuration provided RTApp calibration\n", - "04:24:19 INFO : Target - Using RT-App calibration values:\n", - "04:24:19 INFO : Target - {\"0\": 363, \"1\": 138, \"2\": 139, \"3\": 352, \"4\": 353, \"5\": 361}\n", - "04:24:19 INFO : HWMon - HWMON module not enabled\n", - "04:24:19 WARNING : HWMon - Energy sampling disabled by configuration\n", - "04:24:19 INFO : TestEnv - Set results folder to:\n", - "04:24:19 INFO : TestEnv - /home/derkling/Code/lisa/results/20161108_162419\n", - "04:24:19 INFO : TestEnv - Experiment results available also in:\n", - "04:24:19 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n", - "04:24:19 INFO : Connected to arm64 target\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DONE\n" - ] - } - ], - "source": [ - "from env import TestEnv\n", - "\n", - "my_conf = {\n", - "\n", - " # JUNO Linux\n", - " \"platform\" : \"linux\",\n", - " \"board\" : \"juno\",\n", - " \"host\" : \"192.168.0.1\",\n", - " \"username\" : \"root\",\n", - " \"password\" : \"\",\n", - " \"exclude_modules\" : [ \"hwmon\" ],\n", - "\n", - "# # JUNO Android\n", - "# \"platform\" : \"android\",\n", - "# \"board\" : \"juno\",\n", - "# \"host\" : \"192.168.0.1\",\n", - "# \"exclude_modules\" : [ \"hwmon\" ],\n", - " \n", - "\n", - " # RT-App calibration values\n", - " \"rtapp-calib\" : {\n", - " '0': 363, '1': 138, '2': 139, '3': 352, '4': 353, '5': 361\n", - " },\n", - "\n", - " # List of additional devlib modules to install \n", - " \"modules\" : ['cgroups', 'bl', 'cpufreq'],\n", - " \n", - " # List of additional binary tools to install\n", - " \"tools\" : ['rt-app', 'trace-cmd'],\n", - " \n", - " # FTrace events to collect\n", - " \"ftrace\" : {\n", - " \"events\" : [\n", - " \"sched_switch\"\n", - " ],\n", - " \"buffsize\" : 10240\n", - " }\n", - "}\n", - "\n", - "te = TestEnv(my_conf, force_new=True)\n", - "target = te.target\n", - "\n", - "# Report target connection\n", - "logging.info('Connected to %s target', target.abi)\n", - "print \"DONE\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## List available Controllers" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "04:24:19 INFO : CGroup - Available controllers:\n", - "04:24:20 INFO : CGroup - cpuset (hierarchy id: 1) has 4 cgroups\n", - "04:24:20 INFO : CGroup - cpu (hierarchy id: 2) has 2 cgroups\n", - "04:24:20 INFO : CGroup - cpuacct (hierarchy id: 3) has 1 cgroups\n", - "04:24:20 INFO : CGroup - schedtune (hierarchy id: 4) has 1 cgroups\n", - "04:24:20 INFO : CGroup - blkio (hierarchy id: 5) has 1 cgroups\n", - "04:24:20 INFO : CGroup - memory (hierarchy id: 6) has 1 cgroups\n", - "04:24:20 INFO : CGroup - devices (hierarchy id: 7) has 1 cgroups\n", - "04:24:20 INFO : CGroup - perf_event (hierarchy id: 8) has 1 cgroups\n", - "04:24:20 INFO : CGroup - hugetlb (hierarchy id: 9) has 1 cgroups\n", - "04:24:20 INFO : CGroup - pids (hierarchy id: 10) has 1 cgroups\n" - ] - } - ], - "source": [ - "logging.info('%14s - Available controllers:', 'CGroup')\n", - "ssys = target.cgroups.list_subsystems()\n", - "for (n,h,g,e) in ssys:\n", - " logging.info('%14s - %10s (hierarchy id: %d) has %d cgroups',\n", - " 'CGroup', n, h, g)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example of CPUSET controller usage" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Get a reference to the CPUSet controller\n", - "cpuset = target.cgroups.controller('cpuset')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "04:24:20 INFO : Existing CGropups:\n", - "04:24:20 INFO : /\n", - "04:24:20 INFO : /DEVLIB_ISOL\n", - "04:24:20 INFO : /LITTLE\n", - "04:24:20 INFO : /DEVLIB_SBOX\n" - ] - } - ], - "source": [ - "# Get the list of current configured CGroups for that controller\n", - "cgroups = cpuset.list_all()\n", - "logging.info('Existing CGropups:')\n", - "for cg in cgroups:\n", - " logging.info(' %s', cg)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "04:24:20 INFO : cpuset:/ cpus: 0-5\n", - "04:24:21 INFO : cpuset:/DEVLIB_ISOL cpus: 0\n", - "04:24:22 INFO : cpuset:/LITTLE cpus: 0,3-5\n", - "04:24:23 INFO : cpuset:/DEVLIB_SBOX cpus: 1-5\n" - ] - } - ], - "source": [ - "# Dump the configuraiton of each controller\n", - "for cgname in cgroups:\n", - " #print cgname\n", - " cgroup = cpuset.cgroup(cgname)\n", - " attrs = cgroup.get()\n", - " #print attrs\n", - " cpus = attrs['cpus']\n", - " logging.info('%s:%-15s cpus: %s', cpuset.kind, cgroup.name, cpus)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create a LITTLE partition\n", - "cpuset_littles = cpuset.cgroup('/LITTLE')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LITTLE:\n", - "{\n", - " \"cpu_exclusive\": \"0\", \n", - " \"memory_spread_page\": \"0\", \n", - " \"sched_load_balance\": \"1\", \n", - " \"cpus\": \"0,3-5\", \n", - " \"effective_mems\": \"0\", \n", - " \"mem_hardwall\": \"0\", \n", - " \"mem_exclusive\": \"0\", \n", - " \"memory_pressure\": \"0\", \n", - " \"effective_cpus\": \"0,3-5\", \n", - " \"mems\": \"0\", \n", - " \"sched_relax_domain_level\": \"-1\", \n", - " \"memory_migrate\": \"0\", \n", - " \"memory_spread_slab\": \"0\"\n", - "}\n" - ] - } - ], - "source": [ - "# Check the attributes available for this control group\n", - "print \"LITTLE:\\n\", json.dumps(cpuset_littles.get(), indent=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LITTLE:\n", - "{\n", - " \"cpu_exclusive\": \"0\", \n", - " \"memory_spread_page\": \"0\", \n", - " \"sched_load_balance\": \"1\", \n", - " \"cpus\": \"0,3-5\", \n", - " \"effective_mems\": \"0\", \n", - " \"mem_hardwall\": \"0\", \n", - " \"mem_exclusive\": \"0\", \n", - " \"memory_pressure\": \"0\", \n", - " \"effective_cpus\": \"0,3-5\", \n", - " \"mems\": \"0\", \n", - " \"sched_relax_domain_level\": \"-1\", \n", - " \"memory_migrate\": \"0\", \n", - " \"memory_spread_slab\": \"0\"\n", - "}\n" - ] - } - ], - "source": [ - "# Tune CPUs and MEMs attributes\n", - "# they must be initialize for the group to be usable\n", - "cpuset_littles.set(cpus=target.bl.littles, mems=0)\n", - "print \"LITTLE:\\n\", json.dumps(cpuset_littles.get(), indent=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "04:24:25 INFO : WlGen - Setup new workload simple\n", - "04:24:25 INFO : RTApp - Workload duration defined by longest task\n", - "04:24:25 INFO : RTApp - Default policy: SCHED_OTHER\n", - "04:24:25 INFO : RTApp - ------------------------\n", - "04:24:25 INFO : RTApp - task [task0], sched: using default policy\n", - "04:24:25 INFO : RTApp - | calibration CPU: 1\n", - "04:24:25 INFO : RTApp - | loops count: 1\n", - "04:24:25 INFO : RTApp - + phase_000001: duration 5.000000 [s] (50 loops)\n", - "04:24:25 INFO : RTApp - | period 100000 [us], duty_cycle 80 %\n", - "04:24:25 INFO : RTApp - | run_time 80000 [us], sleep_time 20000 [us]\n", - "04:24:25 INFO : RTApp - ------------------------\n", - "04:24:25 INFO : RTApp - task [task1], sched: using default policy\n", - "04:24:25 INFO : RTApp - | calibration CPU: 1\n", - "04:24:25 INFO : RTApp - | loops count: 1\n", - "04:24:25 INFO : RTApp - + phase_000001: duration 5.000000 [s] (50 loops)\n", - "04:24:25 INFO : RTApp - | period 100000 [us], duty_cycle 80 %\n", - "04:24:25 INFO : RTApp - | run_time 80000 [us], sleep_time 20000 [us]\n", - "04:24:25 INFO : RTApp - ------------------------\n", - "04:24:25 INFO : RTApp - task [task2], sched: using default policy\n", - "04:24:25 INFO : RTApp - | calibration CPU: 1\n", - "04:24:25 INFO : RTApp - | loops count: 1\n", - "04:24:25 INFO : RTApp - + phase_000001: duration 5.000000 [s] (50 loops)\n", - "04:24:25 INFO : RTApp - | period 100000 [us], duty_cycle 80 %\n", - "04:24:25 INFO : RTApp - | run_time 80000 [us], sleep_time 20000 [us]\n", - "04:24:25 INFO : RTApp - ------------------------\n", - "04:24:25 INFO : RTApp - task [task3], sched: using default policy\n", - "04:24:25 INFO : RTApp - | calibration CPU: 1\n", - "04:24:25 INFO : RTApp - | loops count: 1\n", - "04:24:25 INFO : RTApp - + phase_000001: duration 5.000000 [s] (50 loops)\n", - "04:24:25 INFO : RTApp - | period 100000 [us], duty_cycle 80 %\n", - "04:24:25 INFO : RTApp - | run_time 80000 [us], sleep_time 20000 [us]\n", - "04:24:25 INFO : RTApp - ------------------------\n", - "04:24:25 INFO : RTApp - task [task4], sched: using default policy\n", - "04:24:25 INFO : RTApp - | calibration CPU: 1\n", - "04:24:25 INFO : RTApp - | loops count: 1\n", - "04:24:25 INFO : RTApp - + phase_000001: duration 5.000000 [s] (50 loops)\n", - "04:24:25 INFO : RTApp - | period 100000 [us], duty_cycle 80 %\n", - "04:24:25 INFO : RTApp - | run_time 80000 [us], sleep_time 20000 [us]\n", - "04:24:25 INFO : RTApp - ------------------------\n", - "04:24:25 INFO : RTApp - task [task5], sched: using default policy\n", - "04:24:25 INFO : RTApp - | calibration CPU: 1\n", - "04:24:25 INFO : RTApp - | loops count: 1\n", - "04:24:25 INFO : RTApp - + phase_000001: duration 5.000000 [s] (50 loops)\n", - "04:24:25 INFO : RTApp - | period 100000 [us], duty_cycle 80 %\n", - "04:24:25 INFO : RTApp - | run_time 80000 [us], sleep_time 20000 [us]\n" - ] - } - ], - "source": [ - "# Define a periodic big (80%) task\n", - "task = Periodic(\n", - " period_ms=100,\n", - " duty_cycle_pct=80,\n", - " duration_s=5).get()\n", - "\n", - "# Create one task per each CPU in the target\n", - "tasks={}\n", - "for tid in enumerate(target.core_names):\n", - " tasks['task{}'.format(tid[0])] = task\n", - "\n", - "# Configure RTA to run all these tasks\n", - "rtapp = RTA(target, 'simple', calibration=te.calibration())\n", - "rtapp.conf(kind='profile', params=tasks, run_dir=target.working_directory);" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "04:24:30 INFO : WlGen - Workload execution START:\n", - "04:24:30 INFO : WlGen - /root/devlib-target/bin/shutils cgroups_run_into /LITTLE /root/devlib-target/bin/rt-app /root/devlib-target/simple_00.json 2>&1\n", - "04:24:46 INFO : WlGen - Pulling trace file into [/home/derkling/Code/lisa/results/20161108_162419/simple_00.dat]...\n" - ] - } - ], - "source": [ - "# Test execution of all these tasks into the LITTLE cluster\n", - "trace = rtapp.run(ftrace=te.ftrace, cgroup=cpuset_littles.name, out_dir=te.res_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "\n", - "\n", - "\n", - " \n", - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Check tasks residency on little clsuter\n", - "trappy.plotter.plot_trace(trace)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"2258\": {\n", - " \"residency\": 100.0, \n", - " \"task_name\": \"rt-app\"\n", - " }, \n", - " \"2259\": {\n", - " \"residency\": 100.0, \n", - " \"task_name\": \"rt-app\"\n", - " }, \n", - " \"2260\": {\n", - " \"residency\": 100.0, \n", - " \"task_name\": \"rt-app\"\n", - " }, \n", - " \"2261\": {\n", - " \"residency\": 100.0, \n", - " \"task_name\": \"rt-app\"\n", - " }, \n", - " \"2262\": {\n", - " \"residency\": 100.0, \n", - " \"task_name\": \"rt-app\"\n", - " }, \n", - " \"2263\": {\n", - " \"residency\": 100.0, \n", - " \"task_name\": \"rt-app\"\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "# Compute and visualize tasks residencies on LITTLE clusterh CPUs\n", - "s = SchedMultiAssert(trappy.FTrace(trace), te.topology, execnames=tasks.keys())\n", - "residencies = s.getResidency('cluster', target.bl.littles, percent=True)\n", - "print json.dumps(residencies, indent=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Assert that ALL tasks have always executed only on LITTLE cluster\n", - "s.assertResidency('cluster', target.bl.littles,\n", - " 99.9, operator.ge, percent=True, rank=len(residencies))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## Example of CPU controller usage" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Get a reference to the CPU controller\n", - "cpu = target.cgroups.controller('cpu')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create a big partition on that CPUS\n", - "cpu_littles = cpu.cgroup('/LITTLE')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LITTLE:\n", - "{\n", - " \"shares\": \"512\"\n", - "}\n" - ] - } - ], - "source": [ - "# Check the attributes available for this control group\n", - "print \"LITTLE:\\n\", json.dumps(cpu_littles.get(), indent=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LITTLE:\n", - "{\n", - " \"shares\": \"512\"\n", - "}\n" - ] - } - ], - "source": [ - "# Set a 1CPU equivalent bandwidth for that CGroup\n", - "# cpu_littles.set(cfs_period_us=100000, cfs_quota_us=50000)\n", - "cpu_littles.set(shares=512)\n", - "print \"LITTLE:\\n\", json.dumps(cpu_littles.get(), indent=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "04:24:57 INFO : WlGen - Workload execution START:\n", - "04:24:57 INFO : WlGen - /root/devlib-target/bin/shutils cgroups_run_into /LITTLE /root/devlib-target/bin/rt-app /root/devlib-target/simple_00.json 2>&1\n", - "04:25:13 INFO : WlGen - Pulling trace file into [.//simple_00.dat]...\n" - ] - } - ], - "source": [ - "# Test execution of all these tasks into the LITTLE cluster\n", - "trace = rtapp.run(ftrace=te.ftrace, cgroup=cpu_littles.name)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "\n", - "\n", - "\n", - " \n", - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Check tasks residency on little clsuter\n", - "trappy.plotter.plot_trace(trace)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example of CPUs isolation" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Isolate CPU0\n", - "\n", - "# This works by moving all user-space tasks into a cpuset\n", - "# which does not include the specified list of CPUs to be\n", - "# isolated.\n", - "sandbox, isolated = target.cgroups.isolate(cpus=[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sandbox:\n", - "{\n", - " \"cpu_exclusive\": \"0\", \n", - " \"memory_spread_page\": \"0\", \n", - " \"sched_load_balance\": \"1\", \n", - " \"cpus\": \"1-5\", \n", - " \"effective_mems\": \"0\", \n", - " \"mem_hardwall\": \"0\", \n", - " \"mem_exclusive\": \"0\", \n", - " \"memory_pressure\": \"0\", \n", - " \"effective_cpus\": \"1-5\", \n", - " \"mems\": \"0\", \n", - " \"sched_relax_domain_level\": \"-1\", \n", - " \"memory_migrate\": \"0\", \n", - " \"memory_spread_slab\": \"0\"\n", - "}\n" - ] - } - ], - "source": [ - "# Check the attributes available for the SANDBOX group\n", - "print \"Sandbox:\\n\", json.dumps(sandbox.get(), indent=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Isolated:\n", - "{\n", - " \"cpu_exclusive\": \"0\", \n", - " \"memory_spread_page\": \"0\", \n", - " \"sched_load_balance\": \"1\", \n", - " \"cpus\": \"0\", \n", - " \"effective_mems\": \"0\", \n", - " \"mem_hardwall\": \"0\", \n", - " \"mem_exclusive\": \"0\", \n", - " \"memory_pressure\": \"0\", \n", - " \"effective_cpus\": \"0\", \n", - " \"mems\": \"0\", \n", - " \"sched_relax_domain_level\": \"-1\", \n", - " \"memory_migrate\": \"0\", \n", - " \"memory_spread_slab\": \"0\"\n", - "}\n" - ] - } - ], - "source": [ - "# Check the attributes available for the ISOLATED group\n", - "print \"Isolated:\\n\", json.dumps(isolated.get(), indent=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "04:25:28 INFO : WlGen - Workload execution START:\n", - "04:25:28 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/simple_00.json 2>&1\n", - "04:25:40 INFO : WlGen - Pulling trace file into [.//simple_00.dat]...\n" - ] - } - ], - "source": [ - "# Run some workload, which is expected to not run in the ISOLATED cpus:\n", - "trace = rtapp.run(ftrace=te.ftrace)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "\n", - "\n", - "\n", - " \n", - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Check tasks was not running on ISOLATED CPUs\n", - "trappy.plotter.plot_trace(trace)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"2389\": {\n", - " \"residency\": 0.0, \n", - " \"task_name\": \"rt-app\"\n", - " }, \n", - " \"2390\": {\n", - " \"residency\": 0.0, \n", - " \"task_name\": \"rt-app\"\n", - " }, \n", - " \"2391\": {\n", - " \"residency\": 0.0, \n", - " \"task_name\": \"rt-app\"\n", - " }, \n", - " \"2392\": {\n", - " \"residency\": 0.0, \n", - " \"task_name\": \"rt-app\"\n", - " }, \n", - " \"2393\": {\n", - " \"residency\": 0.0, \n", - " \"task_name\": \"rt-app\"\n", - " }, \n", - " \"2394\": {\n", - " \"residency\": 0.0, \n", - " \"task_name\": \"rt-app\"\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "# Compute and visualize tasks residencies on ISOLATED CPUs\n", - "s = SchedMultiAssert(trappy.FTrace(trace), te.topology, execnames=tasks.keys())\n", - "residencies = s.getResidency('cpu', [0], percent=True)\n", - "print json.dumps(residencies, indent=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Assert that ISOLATED CPUs was not running workload tasks\n", - "s.assertResidency('cpu', [0], 0.0, operator.eq, percent=True, rank=len(residencies))" - ] - } - ], - "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" - }, - "toc": { - "toc_cell": false, - "toc_number_sections": true, - "toc_threshold": 6, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/ipynb/examples/android/benchmarks/Android_Jankbench.ipynb b/ipynb/examples/android/benchmarks/Android_Jankbench.ipynb new file mode 100644 index 00000000..d675fd4a --- /dev/null +++ b/ipynb/examples/android/benchmarks/Android_Jankbench.ipynb @@ -0,0 +1,792 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Jankbench benchmark on Android\n", + "\n", + "Most devices today refresh their screens 60 times a second. If there’s an animation or transition running, or the user is scrolling, applications need to match the device’s refresh rate and put up a new picture, or frame, for each of those screen refreshes.\n", + "When one fails to meet this budget - 16.6 ms, the frame rate drops, and the content judders on screen. This is often referred to as jank, and it negatively impacts the user's experience.\n", + "\n", + "This benchmark is used to count the jank frames for different types of activities: list and image list view fling, text render, text editing, etc. Also **ftraces** are captured during the benchmark run and represented at the end of the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 18:27:52,350 INFO : root : Using LISA logging configuration:\n", + "2016-12-08 18:27:52,350 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], + "source": [ + "from conf import LisaLogging\n", + "LisaLogging.setup()" + ] + }, + { + "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": [ + "%pylab inline\n", + "\n", + "import json\n", + "import os\n", + "\n", + "# Support to access the remote target\n", + "import devlib\n", + "from env import TestEnv\n", + "\n", + "# Import support for Android devices\n", + "from android import Screen, Workload\n", + "\n", + "# Support for trace events analysis\n", + "from trace import Trace\n", + "#from trace_analysis import TraceAnalysis\n", + "\n", + "# Suport for FTrace events parsing and visualization\n", + "import trappy\n", + "\n", + "import pandas as pd\n", + "import sqlite3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Support Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function helps us run our experiments:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def experiment():\n", + " # Unlock device screen (assume no password required)\n", + " target.execute('input keyevent 82')\n", + " \n", + " # Configure governor\n", + " target.cpufreq.set_all_governors('sched')\n", + " \n", + " # Configure screen to max brightness and no dimming\n", + " Screen.set_brightness(target, percent=100)\n", + " Screen.set_dim(target, auto=False)\n", + " Screen.set_timeout(target, 60*60*10) # 10 hours should be enought for an experiment\n", + " \n", + " wload = Workload(te).get(te, 'Jankbench')\n", + " te.ftrace.start()\n", + " # Jankbench\n", + " db_file, nrg_report = wload.run(te.res_dir, 'list_view', iterations=1, collect='ftrace')\n", + "\n", + " # Stop the required trace command\n", + " te.ftrace.stop()\n", + " # Collect and keep track of the trace\n", + " trace_file = os.path.join(te.res_dir, 'trace.dat')\n", + " te.ftrace.get_trace(trace_file)\n", + "\n", + " # Reset screen brightness and auto dimming\n", + " Screen.set_defaults(target)\n", + " \n", + " # Dump platform descriptor\n", + " te.platform_dump(te.res_dir)\n", + "\n", + " # return all the experiment data\n", + " return {\n", + " 'dir' : te.res_dir,\n", + " 'db_file' : db_file,\n", + " 'nrg_report' : nrg_report,\n", + " 'trace_file' : trace_file,\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test environment setup\n", + "For more details on this please check out **examples/utils/testenv_example.ipynb**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**devlib** requires the ANDROID_HOME environment variable configured to point to your local installation of the Android SDK. If you have not this variable configured in the shell used to start the notebook server, you need to run a cell to define where your Android SDK is installed or specify the ANDROID_HOME in your target configuration." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case more than one Android device are conencted to the host, you must specify the ID of the device you want to target in **my_target_conf**. Run **adb devices** on your host to get the ID." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup target configuration\n", + "my_conf = {\n", + "\n", + " # Target platform and board\n", + " \"platform\" : 'android',\n", + " \"board\" : 'pixel',\n", + " \n", + " # Device\n", + " \"device\" : \"HT6670300102\",\n", + " \n", + " # Android home\n", + " \"ANDROID_HOME\" : \"/home/vagrant/lisa/tools/android-sdk-linux\",\n", + "\n", + " # Folder where all the results will be collected\n", + " \"results_dir\" : \"Jankbench_example\",\n", + "\n", + " # Define devlib modules to load\n", + " \"modules\" : [\n", + " 'cpufreq' # enable CPUFreq support\n", + " ],\n", + "\n", + " # FTrace events to collect for all the tests configuration which have\n", + " # the \"ftrace\" flag enabled\n", + " \"ftrace\" : {\n", + " \"events\" : [\n", + " \"sched_switch\",\n", + " \"sched_wakeup\",\n", + " \"sched_wakeup_new\",\n", + " \"sched_overutilized\",\n", + " \"sched_load_avg_cpu\",\n", + " \"sched_load_avg_task\",\n", + " \"cpu_capacity\",\n", + " \"cpu_frequency\",\n", + " ],\n", + " \"buffsize\" : 100 * 1024,\n", + " },\n", + "\n", + " # Tools required by the experiments\n", + " \"tools\" : [ 'trace-cmd', 'taskset'],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 18:27:58,260 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-08 18:27:58,261 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-08 18:27:58,261 INFO : TestEnv : External tools using:\n", + "2016-12-08 18:27:58,262 INFO : TestEnv : ANDROID_HOME: /home/vagrant/lisa/tools/android-sdk-linux\n", + "2016-12-08 18:27:58,263 INFO : TestEnv : CATAPULT_HOME: /home/vagrant/lisa/tools/catapult\n", + "2016-12-08 18:27:58,264 INFO : TestEnv : Loading board:\n", + "2016-12-08 18:27:58,264 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/pixel.json\n", + "2016-12-08 18:27:58,266 INFO : TestEnv : Devlib modules to load: [u'bl', u'cpufreq']\n", + "2016-12-08 18:27:58,266 INFO : TestEnv : Connecting Android target [HT6670300102]\n", + "2016-12-08 18:27:58,267 INFO : TestEnv : Connection settings:\n", + "2016-12-08 18:27:58,267 INFO : TestEnv : {'device': 'HT6670300102'}\n", + "2016-12-08 18:27:58,439 INFO : android : ls command is set to ls -1\n", + "2016-12-08 18:27:59,755 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-08 18:27:59,758 INFO : TestEnv : /data/local/tmp/devlib-target\n", + "2016-12-08 18:28:04,318 INFO : TestEnv : Topology:\n", + "2016-12-08 18:28:04,322 INFO : TestEnv : [[0, 1], [2, 3]]\n", + "2016-12-08 18:28:04,711 INFO : TestEnv : Loading default EM:\n", + "2016-12-08 18:28:04,714 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/pixel.json\n", + "2016-12-08 18:28:06,259 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-08 18:28:06,260 INFO : TestEnv : sched_switch\n", + "2016-12-08 18:28:06,260 INFO : TestEnv : sched_wakeup\n", + "2016-12-08 18:28:06,261 INFO : TestEnv : sched_wakeup_new\n", + "2016-12-08 18:28:06,261 INFO : TestEnv : sched_overutilized\n", + "2016-12-08 18:28:06,262 INFO : TestEnv : sched_load_avg_cpu\n", + "2016-12-08 18:28:06,262 INFO : TestEnv : sched_load_avg_task\n", + "2016-12-08 18:28:06,262 INFO : TestEnv : cpu_capacity\n", + "2016-12-08 18:28:06,263 INFO : TestEnv : cpu_frequency\n", + "2016-12-08 18:28:06,263 INFO : TestEnv : Set results folder to:\n", + "2016-12-08 18:28:06,264 INFO : TestEnv : /home/vagrant/lisa/results/Jankbench_example\n", + "2016-12-08 18:28:06,264 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-08 18:28:06,265 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" + ] + } + ], + "source": [ + "# Initialize a test environment using:\n", + "te = TestEnv(my_conf, wipe=False)\n", + "target = te.target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Workloads execution\n", + "\n", + "This is done using the **experiment** helper function defined above which is configured to run a **Jankbench - list view fling** experiment." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 18:28:09,603 INFO : Screen : Set brightness: 100%\n", + "2016-12-08 18:28:10,071 INFO : Screen : Dim screen mode: OFF\n", + "2016-12-08 18:28:10,551 INFO : Screen : Screen timeout: 36000 [s]\n", + "2016-12-08 18:28:12,578 WARNING : Workload : Package [com.android.test.uibench] not installed\n", + "2016-12-08 18:28:12,581 WARNING : Workload : Workload [UiBench] disabled\n", + "2016-12-08 18:28:12,583 INFO : Workload : Workloads available on target:\n", + "2016-12-08 18:28:12,585 INFO : Workload : ['YouTube', 'Jankbench']\n", + "2016-12-08 18:28:12,587 INFO : Workload : Workloads available on target:\n", + "2016-12-08 18:28:12,588 INFO : Workload : ['YouTube', 'Jankbench']\n", + "2016-12-08 18:28:17,475 INFO : Screen : Force manual orientation\n", + "2016-12-08 18:28:17,476 INFO : Screen : Set orientation: PORTRAIT\n", + "2016-12-08 18:28:18,672 INFO : Jankbench : am start -n \"com.android.benchmark/.app.RunLocalBenchmarksActivity\" --eia \"com.android.benchmark.EXTRA_ENABLED_BENCHMARK_IDS\" 0 --ei \"com.android.benchmark.EXTRA_RUN_COUNT\" 1\n", + "2016-12-08 18:28:19,340 INFO : Jankbench : adb -s HT6670300102 logcat ActivityManager:* System.out:I *:S BENCH:*\n", + "2016-12-08 18:28:54,501 INFO : Jankbench : Mean: 47.408 JankP: 0.061 StdDev: 48.564 Count Bad: 2 Count Jank: 1\n", + "2016-12-08 18:28:57,629 INFO : Screen : Set orientation: AUTO\n", + "2016-12-08 18:29:25,157 INFO : Screen : Set orientation: AUTO\n", + "2016-12-08 18:29:27,081 INFO : Screen : Set brightness: AUTO\n", + "2016-12-08 18:29:27,793 INFO : Screen : Dim screen mode: ON\n", + "2016-12-08 18:29:28,437 INFO : Screen : Screen timeout: 30 [s]\n" + ] + } + ], + "source": [ + "# Intialize Workloads for this test environment\n", + "results = experiment()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results collection" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DB[ sched ]: 1631 rows imported\n" + ] + } + ], + "source": [ + "def import_db(path):\n", + " # Selection of columns of interest\n", + " COLS = ['_id', 'name', 'run_id', 'iteration', 'total_duration', 'jank_frame']\n", + " data = []\n", + " db = '{}/{}'.format(te.res_dir, 'BenchmarkResults')\n", + " conn = sqlite3.connect(db)\n", + " for row in conn.execute('SELECT {} FROM ui_results'.format(','.join(COLS))):\n", + " row = ('sched', ) + row\n", + " data.append(row)\n", + " print \"DB[ {} ]: {:6d} rows imported\".format('sched', len(data))\n", + " return pd.DataFrame(data, columns=['test', ] + COLS)\n", + "\n", + "df = import_db(te.res_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmark plots and statistics\n", + "\n", + "All the plots below represent total duration statistics for all the frames, in different ways." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " total_duration \\\n", + " count mean std min 50% \n", + "name test \n", + "List View Fling sched 1631.0 5.362443 2.362088 2.769878 5.129805 \n", + "\n", + " \n", + " 90% 95% 99% max \n", + "name test \n", + "List View Fling sched 7.288264 7.610473 9.119775 81.748059 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def overall_statistics(df):\n", + " byname_test = df.groupby(['name','test']).total_duration.describe(percentiles=[0.9, 0.95, 0.99])\n", + " stats = pd.DataFrame(byname_test)\n", + " stats = stats.unstack()\n", + " return stats\n", + "\n", + "stats = overall_statistics(df)\n", + "stats" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+AQAAAIDeET4BAGja1NRU6QkAAFRI+AQAoGkzMzOlJwAAUCHhEwCApu3Zs6f0BAAAKiR8\nAgAAAAC9I3wCAAAAAL0jfAIAAAAAvSN8AgDQtMFgUHoCAAAVEj4BAGjali1bSk8AAKBCwicAAE0b\nHx8vPQEAgAoJnwAAAABA7wifAAAAAEDvCJ8AADRt//79pScAAFChGsLni5PckeT+JF9M8l1Lvv+m\n4fGFt3ddwH0AAFRsenq69AQAACpUQ/gcSXJ3kpuGXz+85PsPJ3lrkksX3K69YOsAAKjavn37Sk8A\nAKBCq0oPSPK24e10Lkry+SQfuzBzAAAAAIDW1fCMz8fycJKXJvlokr9NcluSLy85CAAAAACoWwvh\n861JvifJNyX5iSQvTPJnSZ5UchQAAAAAUK8WwucfpIufh5P8YZKXJ7kiybeXHAUAQB02bdpUegIA\nABVqIXwu9ZEk9yX5qtPd4dprr81gMFh0u/rqq7N///5F97vzzjszGAxO+vmbbropU1NTi47NzMxk\nMBhkbm5u0fHt27dncnJy0bH77rsvg8Egs7Ozi47feuutmZiYWHRsfn4+g8EgBw8eXHR8enr6lP+I\nv+GGG5yH83AezsN5OA/n4Tycx4LzePDBB3txHn357+E8nIfzcB7Ow3k4D+dxvs7jtttuW9T3rrzy\nylx//fUnPcbpXHTG97wwvpjkuiQHHuU+lyT5+ySvSnL7ku+NJTl06NChjI2NPT4LAQAAAIAiZmZm\nsmHDhiTZkGTm0e5bw1XdvzTdS9dPuDzJ1yT5eJJPJNmZ5M3pnun5nCQ/n+TBJG+5oCsBAAAAgGbU\nED5PXKwo6a7g/obh529K8mNJnpfk+5I8PcnR4X3/bZLPXtCVAAAAAEAzagifd+XR32v02y7QDgAA\nGnTw4MFcc801pWcAAFCZFi9uBAAA/2TXrl2lJwAAUCHhEwCApu3du7f0BAAAKiR8AgDQtJGRkdIT\nAACokPAJAAAAAPSO8AkAAAAA9I7wCQBA0yYmJkpPAACgQsInAABNGx0dLT0BAIAKCZ8AADRt69at\npScAAFAh4RMAAAAA6B3hEwAAAADoHeETAICmzc7Olp4AAECFhE8AAJq2bdu20hMAAKiQ8AkAQNN2\n795degIAABUSPgEAaNro6GjpCQAAVEj4BAAAAAB6R/gEAAAAAHpH+AQAoGmTk5OlJwAAUCHhEwCA\nps3Pz5eeAABAhYRPAACatnPnztITAACokPAJAAAAAPSO8AkAAAAA9I7wCQBA0+bm5kpPAACgQsIn\nAABN27x5c+kJAABUSPgEAKBpO3bsKD0BAIAKCZ8AADRtbGys9AQAACokfAIAAAAAvSN8AgAAAAC9\nI3wCANC0qamp0hMAAKiQ8AkAQNNmZmZKTwAAoELCJwAATduzZ0/pCQAAVEj4BAAAAAB6R/gEAAAA\nAHpH+AQAAAAAekf4BACgaYPBoPQEAAAqJHwCANC0LVu2lJ4AAECFhE8AAJo2Pj5eegIAABUSPgEA\nAACA3hE+AQAAAIDeET4BAGja/v37S08AAKBCwicAAE2bnp4uPQEAgAoJnwAANG3fvn2lJwAAUCHh\nEwAAAADoHeETAAAAAOgd4RMAAAAA6B3hEwCApm3atKn0BAAAKiR8AgDQtPHx8dITAACokPAJAEDT\nNm7cWHoCAAAVEj4BAAAAgN4RPgEAAACA3hE+AQBo2vbtB0tPAACgQsInAABN+/Vf31V6AgAAFRI+\nAQBo2tjY3tITAACokPAJAEDTVq0aKT0BAIAKrSo9AAAAzsb0dHc74Y47ksHgka83buxuAACsbMIn\nAABNWRo2B4PkwIFyewAAqJOXugMA0LTDhydKTwAAoELCJwAATVu9erT0BAAAKiR8AgDQtJtv3lp6\nAgAAFRI+AQBomgsZAQBwKsInAAAAANA7wicAAE2bnZ0tPQEAgAoJnwAANG3btm2lJwAAUCHhEwCA\npu3evbv0BAAAKiR8AgDQtNHR0dITAACokPAJAAAAAPSO8AkAAAAA9I7wCQBA0yYnJ0tPAACgQsIn\nAABNm5+fLz0BAIAKCZ8AADRt586dpScAAFAh4RMAAAAA6B3hEwAAAADoHeETAICmzc3NlZ4AAECF\nhE8AAJq2efPm0hMAAKiQ8AkAQNN27NhRegIAABUSPgEAaNrY2FjpCQAAVEj4BAAAAAB6R/gEAAAA\nAHpH+AQAoGlTU1OlJwAAUCHhEwCAps3MzJSeAABAhYRPAACatmfPntITAACokPAJAAAAAPSO8AkA\nAAAA9I7wCQAAAAD0jvAJAEDTBoNB6QkAAFRI+AQAoGlbtmwpPQEAgAoJnwAANG18fLz0BAAAKiR8\nAgAAAAC9I3wCANC06enSCwAAqJHwCQBA097whv2lJwAAUCHhEwCApj3wgKd8AgBwMuETAICmbdiw\nr/QEAAAqtKr0AAAAOBvT04vf1/OOO5LB4JGvN27sbgAArGzCJwAATVkaNi+9NDlwoNweAADq5KXu\nAAAAAEDvCJ8AADTtU5/aVHoCAAAV8lJ3AACasvQ9Po8fH/cenwAAnET4BACgKUvD5mCw0Xt8AgBw\nEi91BwAAAAB6R/gEAAAAAHpH+AQAoGkveMHB0hMAAKiQ8AkAQNPuvntX6QkAAFRI+AQAoGl79+4t\nPQEAgAoJnwAANG1kZKT0BAAAKiR8AgAAAAC9I3wCAAAAAL0jfAIA0LSJiYnSEwAAqJDwCQBA00ZH\nR0tPAACgQsInAABNu+SSraUnAABQIeETAICmTU+XXgAAQI2ETwAAAACgd4RPAACa9pnPzJaeAABA\nhVaVHgAAAGdjenrxy9vvumtbBoMD//T1xo3dDQCAlU34BACgKUvD5rd8y+4cOHD6+wMAsDJ5qTsA\nAE0bGRktPQEAgAoJnwAAAABA7wifAAA0zft5AgBwKsInAABNO3BgsvQEAAAqJHwCANC0mZn50hMA\nAKiQ8AkAQNOuvHJn6QkAAFRI+AQAAAAAemdV6QEAAHA2pqe72wl33JEMBo98vXGjCx4BACB8AgDQ\nmKVh81u/dS4HDlxSbhAAAFXyUncAAJr2nvdsLj0BAIAKCZ8AADTtuc/dUXoCAAAVEj4BAGja858/\nVnoCAAAVEj4BAGjahz9cegEAADUSPgEAAACA3hE+AQBo2n33TZWeAABAhVaVHgAAAGdjerq7nfCe\n98xkMPj3//T1xo3dDQCAlU34BACgKUvD5oYNe3LgQLk9AADUyUvdAQBo2v33l14AAECNhE8AAAAA\noHeETwAAmvbsZ5deAABAjbzHJwAATVl6caOZmUEGg0fe5NPFjQAASIRPAAAaszRsXnHFFhc3AgDg\nJF7qDgBA0z7zmfHSEwAAqJDwCQAAAAD0znLC5yuTfMeCr38pyaeSvDvJvzwPmwAA4Iy5uBEAAKey\nnPf4vDnJjw4/vzrJTUleneQ7k9yS5LvPzzQAADjZyRc32p/B4Lp/+trFjQAASJYXPv95knuGn1+X\n5L8luS3JXyR55zIe78VJJpKMJVmT5BVJ/vuS++xI8qokz0jyV+li6+Fl/C4AABq3NGw++9nTOXDg\nutP/AAAAK9JyXur+UJJLhp+PJ/mT4eefS7J6GY83kuTudDEzSR5e8v3XpXtG6U1JXpjkI8Pf+ZRl\n/C4AAHpmw4Z9pScAAFCh5Tzj80+S/Ga6WPncJH88PL4+yYeW8XhvG95O5aJ00fPnkuwfHvuBJB9N\n8j3pnmkKAAAAALDIcp7xuSXJu9I96/P6JHPD4xuS/P552nXCZUmemeTOBcc+n+4l9S86z78LAIAG\neT9PAABOZTnP+PyHdO/J+dVJvjzJYHh8Jie/TP1cXTr8+NElxz+WZPQ8/y4AABokfAIAcCrLCZ/f\nluR3k3zZab6/nGeRLsf5jqwAADToJS/ZlHe+87dKzwAAoDLLiZS3JvmDdFdgf+LwMRbezqePDD8+\nc8nxZy743kmuvfbaDAaDRberr746+/fvX3S/O++8M4PB4KSfv+mmmzI1NbXo2MzMTAaDQebm5hYd\n3759eyYnJxcdu++++zIYDDI7O7vo+K233pqJiYlFx+bn5zMYDHLw4MFFx6enp7Np06aTtt1www3O\nw3k4D+fhPJyH83AezmPBeczOPtiL8+jLfw/n4Tych/NwHs7DeTiP83Uet91226K+d+WVV+b6668/\n6TFO56IzvucjPp3kBUn+bhk/+1i+mOS6JAeGX1+U5P4ktyT5peGxJ6V7qftEkt9Y8vNjSQ4dOnQo\nY2Njj8M8AABqMxgkBw489v0AAGjfzMxMNmzYkHTXG5p5tPsu56Xub0ny0py/8PmlSa5Y8PXlSb4m\nyceT/H2SNya5Ock9ST44/PyhnP8LKQEA0KD77y+9AACAGi0nfN6U5M1J/nWS9yb5xyXf/5WzfLwX\nJvmz4ecPJ3nD8PM3JdmcZFeS1Ul+NckzkvxlkvEknz3L3wMAQA9MT3e3E2Zmumd9nrBxowseAQCw\nvJe6/3CSPUmOpXtW5tKLDF12rqPOgZe6AwCsME95ysE89NA1pWcAAHABnM1L3ZdzMaKfSbI9ydOS\nPCdd6Fx4AwCAC2Z+flfpCQAAVGg5L3X/kiR7012ICAAALqilL3V/+OG9XuoOAMBJlhM+fyfJDUl+\n/jxvAQCAx7Q0bD7taSOu6g4AwEmWEz6fkOR1Sb41yd/kkYsbXZTu/T5fe36mAQDAyZY+4/PTn3Zx\nIwAATrac8PnVSe4efv68BcdPhE8AAHjcLA2bl14az/gEAOAkywmfLz3fIwAAYLkefngiyS+VngEA\nQGWWc1V3AACoxld8xWjpCQAAVEj4BACgaTffvLX0BAAAKiR8AgDQNBcyAgDgVIRPAAAAAKB3lnNx\nIwAAemJ+fj6zs7O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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def total_duration_plot(data):\n", + " fig, axes = plt.subplots(figsize=(16,8))\n", + " bp = data.boxplot(by=['name','test'], column='total_duration', ax=axes, return_type='dict')\n", + " fig.suptitle('')\n", + " xlabels = [item.get_text() for item in axes.xaxis.get_ticklabels()]\n", + " axes.set_xticklabels(xlabels, rotation=90)\n", + " axes.set_ylim(0,20)\n", + " axes.set_ylabel('ms');\n", + "\n", + "total_duration_plot(data = df)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def total_duration_cumulative_distribution(df):\n", + " fig, axes = plt.subplots()\n", + " plt.axhline(y=16, linewidth=2, color='r', linestyle='--')\n", + " colors = iter(cm.rainbow(np.linspace(0, 1, 1)))\n", + " df = pd.DataFrame(sorted(df.total_duration))\n", + " df.plot(\n", + " figsize=(16,8),\n", + " ax=axes,\n", + " color=next(colors),\n", + " linewidth=2,\n", + " ylim=(0,80),\n", + " legend=False);\n", + "\n", + " plt.legend(('16ms',) + tuple(['sched']), loc='best');\n", + " \n", + "total_duration_cumulative_distribution(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Traces - Latency analysis\n", + "\n", + "For more information on this please check **examples/trace_analysis/TraceAnalysis_TasksLatencies.ipynb**." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 18:29:50,731 INFO : Trace : Parsing FTrace format...\n", + "2016-12-08 18:30:13,701 INFO : Trace : Platform clusters verified to be Frequency coherent\n", + "2016-12-08 18:30:22,283 INFO : Trace : Collected events spans a 45.779 [s] time interval\n", + "2016-12-08 18:30:22,285 INFO : Trace : Overutilized time: 7.302662 [s] (15.952% of trace time)\n", + "2016-12-08 18:30:22,285 INFO : Trace : Set plots time range to (0.000000, 45.779498)[s]\n", + "2016-12-08 18:30:22,286 INFO : Analysis : Registering trace analysis modules:\n", + "2016-12-08 18:30:22,291 INFO : Analysis : tasks\n", + "2016-12-08 18:30:22,292 INFO : Analysis : status\n", + "2016-12-08 18:30:22,296 INFO : Analysis : frequency\n", + "2016-12-08 18:30:22,298 INFO : Analysis : cpus\n", + "2016-12-08 18:30:22,300 INFO : Analysis : latency\n", + "2016-12-08 18:30:22,303 INFO : Analysis : idle\n", + "2016-12-08 18:30:22,303 INFO : Analysis : functions\n", + "2016-12-08 18:30:22,306 INFO : Analysis : eas\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + "\n", + "\n", + "\n", + " \n", + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Parse all traces\n", + "platform_file = os.path.join(te.res_dir, 'platform.json')\n", + "with open(platform_file, 'r') as fh:\n", + " platform = json.load(fh)\n", + "trace_file = os.path.join(te.res_dir, 'trace.dat')\n", + "trace = Trace(platform, trace_file, events=my_conf['ftrace']['events'])\n", + "\n", + "trappy.plotter.plot_trace(trace.ftrace)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 18:32:01,881 WARNING : Analysis : Multiple PIDs for task named [droid.benchmark]\n", + "2016-12-08 18:32:01,914 WARNING : Analysis : 20074 : Binder:20062_1,droid.benchmark\n", + "2016-12-08 18:32:01,947 WARNING : Analysis : 20076 : Profile Saver,droid.benchmark\n", + "2016-12-08 18:32:01,981 WARNING : Analysis : 20110 : AsyncTask #1,droid.benchmark\n", + "2016-12-08 18:32:02,021 WARNING : Analysis : 20082 : RenderThread,droid.benchmark\n", + "2016-12-08 18:32:02,059 WARNING : Analysis : 20085 : AutomatorThread,droid.benchmark\n", + "2016-12-08 18:32:02,096 WARNING : Analysis : 20086 : FrameStatsColle,droid.benchmark\n", + "2016-12-08 18:32:02,135 WARNING : Analysis : 20062 : droid.benchmar,re-initialized>,main,droid.benchmark\n", + "2016-12-08 18:32:02,136 WARNING : Analysis : Returning stats only for PID: 20074\n", + "2016-12-08 18:32:02,386 INFO : Analysis : Found: 871 WAKEUP latencies\n", + "2016-12-08 18:32:02,426 INFO : Analysis : Found: 69 PREEMPT latencies\n", + "2016-12-08 18:32:02,428 INFO : Analysis : Total: 940 latency events\n" + ] + }, + { + "data": { + "image/png": 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VNmGCJOnA8LC0dWsGCUvHzbcdrqv/pLHfFjKgeOP3OvWt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\nNHnykRoc+m3o5RBQtIz3grJ5s3ny4Jg9O/7xhOq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RZA2jBJl7E3sL5SuvNHVQKz965YlS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pYrQkyviIiIiIiIiIiIBBRml+fH039ergbuBu5Jf/4ecBpwGeZNzk670t8/C1QBT2BaKXp6/5//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\nJHWovr6e+vp6Ghsbi50VSZIkKS8VFVCsq6tj7Nixxc6GJElS3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot latency events for a specified task\n", + "latency_stats_df = trace.analysis.latency.plotLatency('droid.benchmark')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 18:32:46,701 INFO : Analysis : LITTLE cluster average frequency: 1.122 GHz\n", + "2016-12-08 18:32:46,702 INFO : Analysis : big cluster average frequency: 1.486 GHz\n" + ] + }, + { + "data": { + "image/png": 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aG8nevRtHTg5cdVXC25lMTPfddx933XVXusOQiaSujinvvsuS9nYKm5vJ3rkT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MzIwGt5eXjsqafw1VdfYcS55+JvEyagcOxY5Nx8s2YbxcXFGDVqFCDfH1NsuPEWHWEGZSqA4S0/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GtC0IIUQbkQEGIURLSN8hhGgJ6TuEEMGSfkOEq5bexXsTalBQsw814LeC4GdldgYGuz0fAFwC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\nWlo40NZ26DN2x+furv279usrvfmc31NMnes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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "try:\n", + " trace.analysis.frequency.plotClusterFrequencies();\n", + " logging.info('Plotting cluster frequencies for [sched]...')\n", + "except: pass" + ] + } + ], + "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": "2.7.6" + }, + "toc": { + "toc_cell": false, + "toc_number_sections": true, + "toc_threshold": 6, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ipynb/examples/android/benchmarks/Android_PCMark.ipynb b/ipynb/examples/android/benchmarks/Android_PCMark.ipynb new file mode 100644 index 00000000..cbd0e451 --- /dev/null +++ b/ipynb/examples/android/benchmarks/Android_PCMark.ipynb @@ -0,0 +1,560 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PCMark benchmark on Android" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of this experiment is to run benchmarks on a Pixel device running Android with an EAS kernel and collect results. The analysis phase will consist in comparing EAS with other schedulers, that is comparing *sched* governor with:\n", + "\n", + " - interactive\n", + " - performance\n", + " - powersave\n", + " - ondemand\n", + " \n", + "The benchmark we will be using is ***PCMark*** (https://www.futuremark.com/benchmarks/pcmark-android). You will need to **manually install** the app on the Android device in order to run this Notebook.\n", + "\n", + "When opinening PCMark for the first time you will need to Install the work benchmark from inside the app." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 13:09:13,035 INFO : root : Using LISA logging configuration:\n", + "2016-12-12 13:09:13,035 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], + "source": [ + "import logging\n", + "from conf import LisaLogging\n", + "LisaLogging.setup()" + ] + }, + { + "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": [ + "%pylab inline\n", + "\n", + "import copy\n", + "import os\n", + "from time import sleep\n", + "from subprocess import Popen\n", + "import pandas as pd\n", + "\n", + "# Support to access the remote target\n", + "import devlib\n", + "from env import TestEnv\n", + "\n", + "# Support for trace events analysis\n", + "from trace import Trace\n", + "\n", + "# Suport for FTrace events parsing and visualization\n", + "import trappy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test environment setup\n", + "\n", + "For more details on this please check out **examples/utils/testenv_example.ipynb**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case more than one Android device are conencted to the host, you must specify the ID of the device you want to target in `my_target_conf`. Run `adb devices` on your host to get the ID. Also, you have to specify the path to your android sdk in ANDROID_HOME." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a target configuration\n", + "my_target_conf = {\n", + " \n", + " # Target platform and board\n", + " \"platform\" : 'android',\n", + "\n", + " # Add target support\n", + " \"board\" : 'pixel',\n", + " \n", + " # Device ID\n", + " \"device\" : \"HT6670300102\",\n", + " \n", + " \"ANDROID_HOME\" : \"/home/vagrant/lisa/tools/android-sdk-linux/\",\n", + " \n", + " # Define devlib modules to load\n", + " \"modules\" : [\n", + " 'cpufreq' # enable CPUFreq support\n", + " ],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "my_tests_conf = {\n", + "\n", + " # Folder where all the results will be collected\n", + " \"results_dir\" : \"Android_PCMark\",\n", + "\n", + " # Platform configurations to test\n", + " \"confs\" : [\n", + " {\n", + " \"tag\" : \"pcmark\",\n", + " \"flags\" : \"ftrace\", # Enable FTrace events\n", + " \"sched_features\" : \"ENERGY_AWARE\", # enable EAS\n", + " },\n", + " ],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 17:14:32,454 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-08 17:14:32,455 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-08 17:14:32,456 INFO : TestEnv : Loading custom (inline) test configuration\n", + "2016-12-08 17:14:32,457 INFO : TestEnv : External tools using:\n", + "2016-12-08 17:14:32,458 INFO : TestEnv : ANDROID_HOME: /home/vagrant/lisa/tools/android-sdk-linux/\n", + "2016-12-08 17:14:32,458 INFO : TestEnv : CATAPULT_HOME: /home/vagrant/lisa/tools/catapult\n", + "2016-12-08 17:14:32,459 INFO : TestEnv : Loading board:\n", + "2016-12-08 17:14:32,460 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/pixel.json\n", + "2016-12-08 17:14:32,462 INFO : TestEnv : Devlib modules to load: [u'bl', u'cpufreq']\n", + "2016-12-08 17:14:32,463 INFO : TestEnv : Connecting Android target [HT6670300102]\n", + "2016-12-08 17:14:32,463 INFO : TestEnv : Connection settings:\n", + "2016-12-08 17:14:32,464 INFO : TestEnv : {'device': 'HT6670300102'}\n", + "2016-12-08 17:14:32,562 INFO : android : ls command is set to ls -1\n", + "2016-12-08 17:14:33,287 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-08 17:14:33,288 INFO : TestEnv : /data/local/tmp/devlib-target\n", + "2016-12-08 17:14:35,211 INFO : TestEnv : Topology:\n", + "2016-12-08 17:14:35,213 INFO : TestEnv : [[0, 1], [2, 3]]\n", + "2016-12-08 17:14:35,471 INFO : TestEnv : Loading default EM:\n", + "2016-12-08 17:14:35,472 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/pixel.json\n", + "2016-12-08 17:14:35,475 WARNING : TestEnv : Wipe previous contents of the results folder:\n", + "2016-12-08 17:14:35,475 WARNING : TestEnv : /home/vagrant/lisa/results/Android_PCMark\n", + "2016-12-08 17:14:35,476 INFO : TestEnv : Set results folder to:\n", + "2016-12-08 17:14:35,476 INFO : TestEnv : /home/vagrant/lisa/results/Android_PCMark\n", + "2016-12-08 17:14:35,476 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-08 17:14:35,477 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" + ] + } + ], + "source": [ + "# Initialize a test environment using:\n", + "# the provided target configuration (my_target_conf)\n", + "# the provided test configuration (my_test_conf)\n", + "te = TestEnv(target_conf=my_target_conf, test_conf=my_tests_conf)\n", + "target = te.target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Support Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This set of support functions will help us running the benchmark using different CPUFreq governors." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def set_performance():\n", + " target.cpufreq.set_all_governors('performance')\n", + "\n", + "def set_powersave():\n", + " target.cpufreq.set_all_governors('powersave')\n", + "\n", + "def set_interactive():\n", + " target.cpufreq.set_all_governors('interactive')\n", + "\n", + "def set_sched():\n", + " target.cpufreq.set_all_governors('sched')\n", + "\n", + "def set_ondemand():\n", + " target.cpufreq.set_all_governors('ondemand')\n", + " \n", + " for cpu in target.list_online_cpus():\n", + " tunables = target.cpufreq.get_governor_tunables(cpu)\n", + " target.cpufreq.set_governor_tunables(\n", + " cpu,\n", + " 'ondemand',\n", + " **{'sampling_rate' : tunables['sampling_rate_min']}\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# CPUFreq configurations to test\n", + "confs = {\n", + " 'performance' : {\n", + " 'label' : 'prf',\n", + " 'set' : set_performance,\n", + " },\n", + " #'powersave' : {\n", + " # 'label' : 'pws',\n", + " # 'set' : set_powersave,\n", + " #},\n", + " 'interactive' : {\n", + " 'label' : 'int',\n", + " 'set' : set_interactive,\n", + " },\n", + " #'sched' : {\n", + " # 'label' : 'sch',\n", + " # 'set' : set_sched,\n", + " #},\n", + " #'ondemand' : {\n", + " # 'label' : 'odm',\n", + " # 'set' : set_ondemand,\n", + " #}\n", + "}\n", + "\n", + "# The set of results for each comparison test\n", + "results = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#Check if PCMark si available on the device\n", + "\n", + "def check_packages(pkgname):\n", + " try:\n", + " output = target.execute('pm list packages -f | grep -i {}'.format(pkgname))\n", + " except Exception:\n", + " raise RuntimeError('Package: [{}] not availabe on target'.format(pkgname))\n", + "\n", + "# Check for specified PKG name being available on target\n", + "check_packages('com.futuremark.pcmark.android.benchmark')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Function that helps run a PCMark experiment\n", + "\n", + "def pcmark_run(exp_dir):\n", + " # Unlock device screen (assume no password required)\n", + " target.execute('input keyevent 82')\n", + " # Start PCMark on the target device\n", + " target.execute('monkey -p com.futuremark.pcmark.android.benchmark -c android.intent.category.LAUNCHER 1')\n", + " # Wait few seconds to make sure the app is loaded\n", + " sleep(5)\n", + " \n", + " # Flush entire log\n", + " target.clear_logcat()\n", + " \n", + " # Run performance workload (assume screen is vertical)\n", + " target.execute('input tap 750 1450')\n", + " # Wait for completion (10 minutes in total) and collect log\n", + " log_file = os.path.join(exp_dir, 'log.txt')\n", + " # Wait 5 minutes\n", + " sleep(300)\n", + " # Start collecting the log\n", + " with open(log_file, 'w') as log:\n", + " logcat = Popen(['adb logcat', 'com.futuremark.pcmandroid.VirtualMachineState:*', '*:S'],\n", + " stdout=log,\n", + " shell=True)\n", + " # Wait additional two minutes for benchmark to complete\n", + " sleep(300)\n", + "\n", + " # Terminate logcat\n", + " logcat.kill()\n", + "\n", + " # Get scores from logcat\n", + " score_file = os.path.join(exp_dir, 'score.txt')\n", + " os.popen('grep -o \"PCMA_.*_SCORE .*\" {} | sed \"s/ = / /g\" | sort -u > {}'.format(log_file, score_file))\n", + " \n", + " # Close application\n", + " target.execute('am force-stop com.futuremark.pcmark.android.benchmark')\n", + " \n", + " return score_file" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Function that helps run PCMark for different governors\n", + "\n", + "def experiment(governor, exp_dir):\n", + " os.system('mkdir -p {}'.format(exp_dir));\n", + "\n", + " logging.info('------------------------')\n", + " logging.info('Run workload using %s governor', governor)\n", + " confs[governor]['set']()\n", + "\n", + " ### Run the benchmark ###\n", + " score_file = pcmark_run(exp_dir)\n", + " \n", + " # Save the score as a dictionary\n", + " scores = dict()\n", + " with open(score_file, 'r') as f:\n", + " lines = f.readlines()\n", + " for l in lines:\n", + " info = l.split()\n", + " scores.update({info[0] : float(info[1])})\n", + " \n", + " # return all the experiment data\n", + " return {\n", + " 'dir' : exp_dir,\n", + " 'scores' : scores,\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run PCMark and collect scores" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 17:14:43,080 INFO : root : ------------------------\n", + "2016-12-08 17:14:43,081 INFO : root : Run workload using performance governor\n", + "2016-12-08 17:24:50,386 INFO : root : ------------------------\n", + "2016-12-08 17:24:50,387 INFO : root : Run workload using interactive governor\n" + ] + } + ], + "source": [ + "# Run the benchmark in all the configured governors\n", + "for governor in confs:\n", + " test_dir = os.path.join(te.res_dir, governor)\n", + " res = experiment(governor, test_dir)\n", + " results[governor] = copy.deepcopy(res)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "After running the benchmark for the specified governors we can show and plot the scores:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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interactiveperformance
PCMA_DATA_MANIPULATION_SCORE4264.3553194260.128135
PCMA_PHOTO_EDITING_V2_SCORE16853.97914016422.056987
PCMA_VIDEO_EDITING_SCORE6281.3207056314.691918
PCMA_WEB_V2_SCORE5513.3581305610.058655
PCMA_WORK_V2_SCORE6803.3546476790.043529
PCMA_WRITING_V2_SCORE5855.8850775823.619700
\n", + "
" + ], + "text/plain": [ + " interactive performance\n", + "PCMA_DATA_MANIPULATION_SCORE 4264.355319 4260.128135\n", + "PCMA_PHOTO_EDITING_V2_SCORE 16853.979140 16422.056987\n", + "PCMA_VIDEO_EDITING_SCORE 6281.320705 6314.691918\n", + "PCMA_WEB_V2_SCORE 5513.358130 5610.058655\n", + "PCMA_WORK_V2_SCORE 6803.354647 6790.043529\n", + "PCMA_WRITING_V2_SCORE 5855.885077 5823.619700" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create results DataFrame\n", + "data = {}\n", + "for governor in confs:\n", + " data[governor] = {}\n", + " for score_name, score in results[governor]['scores'].iteritems():\n", + " data[governor][score_name] = score\n", + "\n", + "df = pd.DataFrame.from_dict(data)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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fv3507dqVv/zlL/Tu3ZuCggLOPfdcPvjgA3Jzc7npppuYNGkS7du3p3HjxvTr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\nYSMAAAAAMAlhIwAAAAAwCWEjAAAAADA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O2xsXfG2Fzeb+jWvHdy6UPaDRPg5r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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.plot(kind='bar', rot=45, figsize=(16,8),\n", + " title='PCMark scores vs SchedFreq governors');" + ] + } + ], + "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": "2.7.6" + }, + "toc": { + "toc_cell": false, + "toc_number_sections": true, + "toc_threshold": 6, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ipynb/examples/android/workloads/Android_Recents_Fling.ipynb b/ipynb/examples/android/workloads/Android_Recents_Fling.ipynb new file mode 100644 index 00000000..97dbf7b9 --- /dev/null +++ b/ipynb/examples/android/workloads/Android_Recents_Fling.ipynb @@ -0,0 +1,750 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# EAS Testing - Recents Fling on Android" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of this experiment is to collect frame statistics while swiping up and down tabs of recently opened applications on a Pixel device running Android with an EAS kernel. This process is name **Recents Fling**. The Analysis phase will consist in comparing EAS with other schedulers, that is comparing *sched* governor with:\n", + "\n", + " - interactive\n", + " - performance\n", + " - powersave\n", + " - ondemand\n", + " \n", + "For this experiment it is recommended to open many applications so that we can swipe over more recently opened applications." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-09 14:58:45,902 INFO : root : Using LISA logging configuration:\n", + "2016-12-09 14:58:45,902 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], + "source": [ + "import logging\n", + "from conf import LisaLogging\n", + "LisaLogging.setup()" + ] + }, + { + "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": [ + "%pylab inline\n", + "\n", + "import os\n", + "from time import sleep\n", + "\n", + "# Support to access the remote target\n", + "import devlib\n", + "from env import TestEnv\n", + "\n", + "# Import support for Android devices\n", + "from android import Screen, Workload\n", + "\n", + "from devlib.utils.android import adb_command\n", + "\n", + "# Support for trace events analysis\n", + "from trace import Trace\n", + "\n", + "# Suport for FTrace events parsing and visualization\n", + "import trappy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test environment setup\n", + "For more details on this please check out **examples/utils/testenv_example.ipynb**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**devlib** requires the ANDROID_HOME environment variable configured to point to your local installation of the Android SDK. If you have not this variable configured in the shell used to start the notebook server, you need to run a cell to define where your Android SDK is installed or specify the ANDROID_HOME in your target configuration.\n", + "\n", + "In case more than one Android device are conencted to the host, you must specify the ID of the device you want to target in **my_target_conf**. Run **adb devices** on your host to get the ID." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ['ANDROID_HOME'] = '/ext/android-sdk-linux/'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case more than one Android device are conencted to the host, you must specify the ID of the device you want to target in `my_target_conf`. Run `adb devices` on your host to get the ID." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a target configuration\n", + "my_conf = {\n", + " \n", + " # Target platform and board\n", + " \"platform\" : 'android',\n", + " \"board\" : 'pixel',\n", + "\n", + " # Device ID\n", + " \"device\" : \"HT6670300102\",\n", + " \n", + " # Android home\n", + " \"ANDROID_HOME\" : \"/home/vagrant/lisa/tools/android-sdk-linux\",\n", + "\n", + " # Folder where all the results will be collected\n", + " \"results_dir\" : \"Android_RecentsFling\",\n", + " \n", + " # Define devlib modules to load\n", + " \"modules\" : [\n", + " 'cpufreq' # enable CPUFreq support\n", + " ],\n", + "\n", + " # FTrace events to collect for all the tests configuration which have\n", + " # the \"ftrace\" flag enabled\n", + " \"ftrace\" : {\n", + " \"events\" : [\n", + " \"sched_switch\",\n", + " \"sched_load_avg_cpu\",\n", + " \"cpu_frequency\",\n", + " \"cpu_capacity\"\n", + " ],\n", + " \"buffsize\" : 10 * 1024,\n", + " },\n", + "\n", + " # Tools required by the experiments\n", + " \"tools\" : [ 'trace-cmd' ],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-09 14:58:50,762 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-09 14:58:50,763 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-09 14:58:50,764 INFO : TestEnv : External tools using:\n", + "2016-12-09 14:58:50,765 INFO : TestEnv : ANDROID_HOME: /home/vagrant/lisa/tools/android-sdk-linux\n", + "2016-12-09 14:58:50,766 INFO : TestEnv : CATAPULT_HOME: /home/vagrant/lisa/tools/catapult\n", + "2016-12-09 14:58:50,767 INFO : TestEnv : Loading board:\n", + "2016-12-09 14:58:50,767 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/pixel.json\n", + "2016-12-09 14:58:50,768 INFO : TestEnv : Devlib modules to load: [u'bl', u'cpufreq']\n", + "2016-12-09 14:58:50,768 INFO : TestEnv : Connecting Android target [HT6670300102]\n", + "2016-12-09 14:58:50,769 INFO : TestEnv : Connection settings:\n", + "2016-12-09 14:58:50,769 INFO : TestEnv : {'device': 'HT6670300102'}\n", + "2016-12-09 14:58:50,855 INFO : android : ls command is set to ls -1\n", + "2016-12-09 14:58:51,610 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-09 14:58:51,613 INFO : TestEnv : /data/local/tmp/devlib-target\n", + "2016-12-09 14:58:54,009 INFO : TestEnv : Topology:\n", + "2016-12-09 14:58:54,012 INFO : TestEnv : [[0, 1], [2, 3]]\n", + "2016-12-09 14:58:54,279 INFO : TestEnv : Loading default EM:\n", + "2016-12-09 14:58:54,282 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/pixel.json\n", + "2016-12-09 14:58:55,219 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-09 14:58:55,220 INFO : TestEnv : sched_switch\n", + "2016-12-09 14:58:55,221 INFO : TestEnv : sched_load_avg_cpu\n", + "2016-12-09 14:58:55,221 INFO : TestEnv : cpu_frequency\n", + "2016-12-09 14:58:55,222 INFO : TestEnv : cpu_capacity\n", + "2016-12-09 14:58:55,222 WARNING : TestEnv : Wipe previous contents of the results folder:\n", + "2016-12-09 14:58:55,223 WARNING : TestEnv : /home/vagrant/lisa/results/Android_RecentsFling\n", + "2016-12-09 14:58:55,291 INFO : TestEnv : Set results folder to:\n", + "2016-12-09 14:58:55,291 INFO : TestEnv : /home/vagrant/lisa/results/Android_RecentsFling\n", + "2016-12-09 14:58:55,292 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-09 14:58:55,292 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" + ] + } + ], + "source": [ + "# Initialize a test environment using:\n", + "te = TestEnv(my_conf)\n", + "target = te.target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Support Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This set of support functions will help us running the benchmark using different CPUFreq governors." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def set_performance():\n", + " target.cpufreq.set_all_governors('performance')\n", + "\n", + "def set_powersave():\n", + " target.cpufreq.set_all_governors('powersave')\n", + "\n", + "def set_interactive():\n", + " target.cpufreq.set_all_governors('interactive')\n", + "\n", + "def set_sched():\n", + " target.cpufreq.set_all_governors('sched')\n", + "\n", + "def set_ondemand():\n", + " target.cpufreq.set_all_governors('ondemand')\n", + " \n", + " for cpu in target.list_online_cpus():\n", + " tunables = target.cpufreq.get_governor_tunables(cpu)\n", + " target.cpufreq.set_governor_tunables(\n", + " cpu,\n", + " 'ondemand',\n", + " **{'sampling_rate' : tunables['sampling_rate_min']}\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# CPUFreq configurations to test\n", + "confs = {\n", + " 'performance' : {\n", + " 'label' : 'prf',\n", + " 'set' : set_performance,\n", + " },\n", + " 'powersave' : {\n", + " 'label' : 'pws',\n", + " 'set' : set_powersave,\n", + " },\n", + " 'interactive' : {\n", + " 'label' : 'int',\n", + " 'set' : set_interactive,\n", + " },\n", + " 'sched' : {\n", + " 'label' : 'sch',\n", + " 'set' : set_sched,\n", + " },\n", + " 'ondemand' : {\n", + " 'label' : 'odm',\n", + " 'set' : set_ondemand,\n", + " }\n", + "}\n", + "\n", + "# The set of results for each comparison test\n", + "results = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def open_apps(n):\n", + " \"\"\"\n", + " Open `n` apps on the device\n", + " \n", + " :param n: number of apps to open\n", + " :type n: int\n", + " \"\"\"\n", + " # Get a list of third-party packages\n", + " android_version = target.getprop('ro.build.version.release')\n", + " if android_version >= 'N':\n", + " packages = target.execute('cmd package list packages | cut -d: -f 2')\n", + " packages = packages.splitlines()\n", + " else:\n", + " packages = target.execute('pm list packages -3 | cut -d: -f 2')\n", + " packages = packages.splitlines()\n", + "\n", + " # As a safe fallback let's use a list of standard Android AOSP apps which are always available\n", + " if len(packages) < 8:\n", + " packages = [\n", + " 'com.android.messaging',\n", + " 'com.android.calendar',\n", + " 'com.android.settings',\n", + " 'com.android.calculator2',\n", + " 'com.android.email',\n", + " 'com.android.music',\n", + " 'com.android.deskclock',\n", + " 'com.android.contacts',\n", + " ]\n", + " \n", + " LAUNCH_CMD = 'monkey -p {} -c android.intent.category.LAUNCHER 1 '\n", + " \n", + " if n > len(packages):\n", + " n = len(packages)\n", + " \n", + " logging.info('Trying to open %d apps...', n)\n", + " started = 0\n", + " for app in packages:\n", + " logging.debug(' Launching %s', app)\n", + " try:\n", + " target.execute(LAUNCH_CMD.format(app))\n", + " started = started + 1\n", + " logging.info(' %2d starting %s...', started, app)\n", + " except Exception:\n", + " pass\n", + " if started >= n:\n", + " break\n", + " \n", + " # Close Recents\n", + " target.execute('input keyevent KEYCODE_HOME')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def recentsfling_run(exp_dir):\n", + " # Unlock device screen (assume no password required)\n", + " target.execute('input keyevent 82')\n", + "\n", + " # Configure screen to max brightness and no dimming\n", + " Screen.set_brightness(target, percent=100)\n", + " Screen.set_dim(target, auto=False)\n", + " Screen.set_timeout(target, 60*60*10) # 10 hours should be enought for an experiment\n", + "\n", + " # Open Recents on the target device\n", + " target.execute('input keyevent KEYCODE_APP_SWITCH')\n", + " # Allow the activity to start\n", + " sleep(5)\n", + " # Reset framestats collection\n", + " target.execute('dumpsys gfxinfo --reset')\n", + " \n", + " w, h = target.screen_resolution\n", + " x = w/2\n", + " yl = int(0.2*h)\n", + " yh = int(0.9*h)\n", + " \n", + " logging.info('Start Swiping Recents')\n", + " for i in range(5):\n", + " # Simulate two fast UP and DOWN swipes\n", + " target.execute('input swipe {} {} {} {} 50'.format(x, yl, x, yh))\n", + " sleep(0.3)\n", + " target.execute('input swipe {} {} {} {} 50'.format(x, yh, x, yl))\n", + " sleep(0.7)\n", + " logging.info('Swiping Recents Completed')\n", + " \n", + " # Reset screen brightness and auto dimming\n", + " Screen.set_defaults(target)\n", + "\n", + " # Get frame stats\n", + " framestats_file = os.path.join(exp_dir, \"framestats.txt\")\n", + " adb_command(target.adb_name, 'shell dumpsys gfxinfo com.android.systemui > {}'.format(framestats_file))\n", + " \n", + " # Close Recents\n", + " target.execute('input keyevent KEYCODE_HOME')\n", + "\n", + " return framestats_file" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def experiment(governor, exp_dir):\n", + " os.system('mkdir -p {}'.format(exp_dir));\n", + "\n", + " logging.info('------------------------')\n", + " logging.info('Run workload using %s governor', governor)\n", + " confs[governor]['set']()\n", + " \n", + " # Start FTrace\n", + " te.ftrace.start()\n", + " \n", + " ### Run the benchmark ###\n", + " framestats_file = recentsfling_run(exp_dir)\n", + " \n", + " # Stop FTrace\n", + " te.ftrace.stop() \n", + "\n", + " # Collect and keep track of the trace\n", + " trace_file = os.path.join(exp_dir, 'trace.dat')\n", + " te.ftrace.get_trace(trace_file)\n", + " \n", + " # Parse trace\n", + " tr = Trace(te.platform, exp_dir,\n", + " events=my_conf['ftrace']['events'])\n", + " \n", + " # return all the experiment data\n", + " return {\n", + " 'dir' : exp_dir,\n", + " 'framestats_file' : framestats_file,\n", + " 'trace_file' : trace_file,\n", + " 'ftrace' : tr.ftrace,\n", + " 'trace' : tr\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Flinger" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare Environment" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "N_APPS = 20" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-09 14:59:08,075 INFO : root : Trying to open 8 apps...\n", + "2016-12-09 14:59:09,677 INFO : root : 1 starting com.android.settings...\n" + ] + } + ], + "source": [ + "open_apps(N_APPS)\n", + "\n", + "# Give apps enough time to open\n", + "sleep(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run workload and collect traces" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-09 14:59:18,399 INFO : root : ------------------------\n", + "2016-12-09 14:59:18,402 INFO : root : Run workload using performance governor\n", + "2016-12-09 14:59:20,895 INFO : Screen : Set brightness: 100%\n", + "2016-12-09 14:59:21,308 INFO : Screen : Dim screen mode: OFF\n", + "2016-12-09 14:59:21,782 INFO : Screen : Screen timeout: 36000 [s]\n", + "2016-12-09 14:59:27,693 INFO : root : Start Swiping Recents\n", + "2016-12-09 14:59:37,783 INFO : root : Swiping Recents Completed\n", + "2016-12-09 14:59:37,784 INFO : Screen : Set orientation: AUTO\n", + "2016-12-09 14:59:39,042 INFO : Screen : Set brightness: AUTO\n", + "2016-12-09 14:59:39,496 INFO : Screen : Dim screen mode: ON\n", + "2016-12-09 14:59:39,932 INFO : Screen : Screen timeout: 30 [s]\n", + "2016-12-09 14:59:45,387 INFO : Trace : Parsing FTrace format...\n", + "2016-12-09 14:59:53,031 INFO : Trace : Collected events spans a 21.305 [s] time interval\n", + "2016-12-09 14:59:53,031 INFO : Trace : Set plots time range to (0.000000, 21.305402)[s]\n", + "2016-12-09 14:59:53,032 INFO : Analysis : Registering trace analysis modules:\n", + "2016-12-09 14:59:53,033 INFO : Analysis : tasks\n", + "2016-12-09 14:59:53,034 INFO : Analysis : status\n", + "2016-12-09 14:59:53,034 INFO : Analysis : frequency\n", + "2016-12-09 14:59:53,035 INFO : Analysis : cpus\n", + "2016-12-09 14:59:53,036 INFO : Analysis : latency\n", + "2016-12-09 14:59:53,037 INFO : Analysis : idle\n", + "2016-12-09 14:59:53,038 INFO : Analysis : functions\n", + "2016-12-09 14:59:53,038 INFO : Analysis : eas\n", + "2016-12-09 14:59:53,051 INFO : root : ------------------------\n", + "2016-12-09 14:59:53,052 INFO : root : Run workload using sched governor\n", + "2016-12-09 14:59:56,256 INFO : Screen : Set brightness: 100%\n", + "2016-12-09 14:59:56,708 INFO : Screen : Dim screen mode: OFF\n", + "2016-12-09 14:59:57,303 INFO : Screen : Screen timeout: 36000 [s]\n", + "2016-12-09 15:00:03,345 INFO : root : Start Swiping Recents\n", + "2016-12-09 15:00:13,864 INFO : root : Swiping Recents Completed\n", + "2016-12-09 15:00:13,865 INFO : Screen : Set orientation: AUTO\n", + "2016-12-09 15:00:15,221 INFO : Screen : Set brightness: AUTO\n", + "2016-12-09 15:00:15,718 INFO : Screen : Dim screen mode: ON\n", + "2016-12-09 15:00:16,189 INFO : Screen : Screen timeout: 30 [s]\n", + "2016-12-09 15:00:27,259 INFO : Trace : Parsing FTrace format...\n", + "2016-12-09 15:00:35,017 INFO : Trace : Platform clusters verified to be Frequency coherent\n", + "2016-12-09 15:00:38,715 INFO : Trace : Collected events spans a 22.739 [s] time interval\n", + "2016-12-09 15:00:38,715 INFO : Trace : Set plots time range to (0.000000, 22.739051)[s]\n", + "2016-12-09 15:00:38,716 INFO : Analysis : Registering trace analysis modules:\n", + "2016-12-09 15:00:38,716 INFO : Analysis : tasks\n", + "2016-12-09 15:00:38,717 INFO : Analysis : status\n", + "2016-12-09 15:00:38,717 INFO : Analysis : frequency\n", + "2016-12-09 15:00:38,718 INFO : Analysis : cpus\n", + "2016-12-09 15:00:38,719 INFO : Analysis : latency\n", + "2016-12-09 15:00:38,719 INFO : Analysis : idle\n", + "2016-12-09 15:00:38,720 INFO : Analysis : functions\n", + "2016-12-09 15:00:38,720 INFO : Analysis : eas\n", + "2016-12-09 15:00:38,735 INFO : root : ------------------------\n", + "2016-12-09 15:00:38,736 INFO : root : Run workload using powersave governor\n", + "2016-12-09 15:00:49,017 INFO : Screen : Set brightness: 100%\n", + "2016-12-09 15:00:51,668 INFO : Screen : Dim screen mode: OFF\n", + "2016-12-09 15:00:54,328 INFO : Screen : Screen timeout: 36000 [s]\n", + "2016-12-09 15:01:03,584 INFO : root : Start Swiping Recents\n", + "2016-12-09 15:01:37,809 INFO : root : Swiping Recents Completed\n", + "2016-12-09 15:01:37,812 INFO : Screen : Set orientation: AUTO\n", + "2016-12-09 15:01:46,394 INFO : Screen : Set brightness: AUTO\n", + "2016-12-09 15:01:48,971 INFO : Screen : Dim screen mode: ON\n", + "2016-12-09 15:01:51,561 INFO : Screen : Screen timeout: 30 [s]\n", + "2016-12-09 15:02:17,487 INFO : Trace : Parsing FTrace format...\n", + "2016-12-09 15:02:33,993 INFO : Trace : Collected events spans a 75.655 [s] time interval\n", + "2016-12-09 15:02:33,994 INFO : Trace : Set plots time range to (0.000000, 75.654834)[s]\n", + "2016-12-09 15:02:33,994 INFO : Analysis : Registering trace analysis modules:\n", + "2016-12-09 15:02:33,995 INFO : Analysis : tasks\n", + "2016-12-09 15:02:33,996 INFO : Analysis : status\n", + "2016-12-09 15:02:33,996 INFO : Analysis : frequency\n", + "2016-12-09 15:02:33,997 INFO : Analysis : cpus\n", + "2016-12-09 15:02:33,997 INFO : Analysis : latency\n", + "2016-12-09 15:02:33,998 INFO : Analysis : idle\n", + "2016-12-09 15:02:33,998 INFO : Analysis : functions\n", + "2016-12-09 15:02:33,999 INFO : Analysis : eas\n", + "2016-12-09 15:02:34,022 INFO : root : ------------------------\n", + "2016-12-09 15:02:34,023 INFO : root : Run workload using ondemand governor\n", + "2016-12-09 15:02:41,668 INFO : Screen : Set brightness: 100%\n", + "2016-12-09 15:02:42,139 INFO : Screen : Dim screen mode: OFF\n", + "2016-12-09 15:02:42,656 INFO : Screen : Screen timeout: 36000 [s]\n", + "2016-12-09 15:02:48,429 INFO : root : Start Swiping Recents\n", + "2016-12-09 15:02:59,047 INFO : root : Swiping Recents Completed\n", + "2016-12-09 15:02:59,050 INFO : Screen : Set orientation: AUTO\n", + "2016-12-09 15:03:00,468 INFO : Screen : Set brightness: AUTO\n", + "2016-12-09 15:03:00,966 INFO : Screen : Dim screen mode: ON\n", + "2016-12-09 15:03:01,444 INFO : Screen : Screen timeout: 30 [s]\n", + "2016-12-09 15:03:12,685 INFO : Trace : Parsing FTrace format...\n", + "2016-12-09 15:03:21,734 INFO : Trace : Platform clusters verified to be Frequency coherent\n", + "2016-12-09 15:03:26,708 INFO : Trace : Collected events spans a 22.503 [s] time interval\n", + "2016-12-09 15:03:26,708 INFO : Trace : Set plots time range to (0.000000, 22.502716)[s]\n", + "2016-12-09 15:03:26,709 INFO : Analysis : Registering trace analysis modules:\n", + "2016-12-09 15:03:26,710 INFO : Analysis : tasks\n", + "2016-12-09 15:03:26,710 INFO : Analysis : status\n", + "2016-12-09 15:03:26,711 INFO : Analysis : frequency\n", + "2016-12-09 15:03:26,711 INFO : Analysis : cpus\n", + "2016-12-09 15:03:26,712 INFO : Analysis : latency\n", + "2016-12-09 15:03:26,712 INFO : Analysis : idle\n", + "2016-12-09 15:03:26,713 INFO : Analysis : functions\n", + "2016-12-09 15:03:26,713 INFO : Analysis : eas\n", + "2016-12-09 15:03:26,730 INFO : root : ------------------------\n", + "2016-12-09 15:03:26,731 INFO : root : Run workload using interactive governor\n", + "2016-12-09 15:03:29,716 INFO : Screen : Set brightness: 100%\n", + "2016-12-09 15:03:30,177 INFO : Screen : Dim screen mode: OFF\n", + "2016-12-09 15:03:30,645 INFO : Screen : Screen timeout: 36000 [s]\n", + "2016-12-09 15:03:36,422 INFO : root : Start Swiping Recents\n", + "2016-12-09 15:03:46,719 INFO : root : Swiping Recents Completed\n", + "2016-12-09 15:03:46,721 INFO : Screen : Set orientation: AUTO\n", + "2016-12-09 15:03:48,071 INFO : Screen : Set brightness: AUTO\n", + "2016-12-09 15:03:48,532 INFO : Screen : Dim screen mode: ON\n", + "2016-12-09 15:03:48,991 INFO : Screen : Screen timeout: 30 [s]\n", + "2016-12-09 15:03:55,795 INFO : Trace : Parsing FTrace format...\n", + "2016-12-09 15:04:06,933 INFO : Trace : Collected events spans a 21.791 [s] time interval\n", + "2016-12-09 15:04:06,934 INFO : Trace : Set plots time range to (0.000000, 21.791161)[s]\n", + "2016-12-09 15:04:06,934 INFO : Analysis : Registering trace analysis modules:\n", + "2016-12-09 15:04:06,935 INFO : Analysis : tasks\n", + "2016-12-09 15:04:06,936 INFO : Analysis : status\n", + "2016-12-09 15:04:06,936 INFO : Analysis : frequency\n", + "2016-12-09 15:04:06,937 INFO : Analysis : cpus\n", + "2016-12-09 15:04:06,938 INFO : Analysis : latency\n", + "2016-12-09 15:04:06,938 INFO : Analysis : idle\n", + "2016-12-09 15:04:06,939 INFO : Analysis : functions\n", + "2016-12-09 15:04:06,939 INFO : Analysis : eas\n" + ] + } + ], + "source": [ + "# Unlock device screen (assume no password required)\n", + "target.execute('input keyevent 82')\n", + "\n", + "# Run the benchmark in all the configured governors\n", + "for governor in confs:\n", + " test_dir = os.path.join(te.res_dir, governor)\n", + " results[governor] = experiment(governor, test_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "## UI Performance Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Frame Statistics for PERFORMANCE governor\n", + "Stats since: 22107824648ns\n", + "Total frames rendered: 10844\n", + "Janky frames: 550 (5.07%)\n", + "50th percentile: 6ms\n", + "90th percentile: 11ms\n", + "95th percentile: 16ms\n", + "99th percentile: 61ms\n", + "\n", + "Frame Statistics for SCHED governor\n", + "Stats since: 22107824648ns\n", + "Total frames rendered: 11367\n", + "Janky frames: 555 (4.88%)\n", + "50th percentile: 6ms\n", + "90th percentile: 10ms\n", + "95th percentile: 16ms\n", + "99th percentile: 61ms\n", + "\n", + "Frame Statistics for POWERSAVE governor\n", + "Stats since: 22107824648ns\n", + "Total frames rendered: 11500\n", + "Janky frames: 645 (5.61%)\n", + "50th percentile: 6ms\n", + "90th percentile: 11ms\n", + "95th percentile: 18ms\n", + "99th percentile: 61ms\n", + "\n", + "Frame Statistics for ONDEMAND governor\n", + "Stats since: 22107824648ns\n", + "Total frames rendered: 11660\n", + "Janky frames: 661 (5.67%)\n", + "50th percentile: 6ms\n", + "90th percentile: 11ms\n", + "95th percentile: 18ms\n", + "99th percentile: 61ms\n", + "\n", + "Frame Statistics for INTERACTIVE governor\n", + "Stats since: 22107824648ns\n", + "Total frames rendered: 12174\n", + "Janky frames: 661 (5.43%)\n", + "50th percentile: 6ms\n", + "90th percentile: 11ms\n", + "95th percentile: 17ms\n", + "99th percentile: 61ms\n", + "\n" + ] + } + ], + "source": [ + "for governor in confs:\n", + " framestats_file = results[governor]['framestats_file']\n", + " print \"Frame Statistics for {} governor\".format(governor.upper())\n", + " !sed '/Stats since/,/99th/!d;/99th/q' $framestats_file\n", + " print \"\"" + ] + } + ], + "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": "2.7.6" + }, + "toc": { + "toc_cell": false, + "toc_number_sections": true, + "toc_threshold": 6, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ipynb/examples/android/workloads/Android_Workloads.ipynb b/ipynb/examples/android/workloads/Android_Workloads.ipynb new file mode 100644 index 00000000..fb957e20 --- /dev/null +++ b/ipynb/examples/android/workloads/Android_Workloads.ipynb @@ -0,0 +1,707 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Android Multiple Workloads - complex example\n", + "\n", + "This complex example shows multiple workloads being executed in multiple configurations.\n", + "\n", + "Please check the notebooks in **examples/android/benchmarks/** and **examples/android/workloads/** to get more details on each of the possible workloads and how you can visualise their results." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-09 17:56:46,425 INFO : root : Using LISA logging configuration:\n", + "2016-12-09 17:56:46,426 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], + "source": [ + "import logging\n", + "from conf import LisaLogging\n", + "LisaLogging.setup()" + ] + }, + { + "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": [ + "%pylab inline\n", + "\n", + "import collections\n", + "import copy\n", + "import json\n", + "import os\n", + "from time import sleep\n", + "\n", + "# Support to access the remote target\n", + "import devlib\n", + "from env import TestEnv\n", + "\n", + "# from devlib.utils.android import adb_command\n", + "\n", + "# Import support for Android devices\n", + "from android import Screen, Workload, System\n", + "\n", + "# Support for trace events analysis\n", + "from trace import Trace\n", + "\n", + "# Suport for FTrace events parsing and visualization\n", + "import trappy\n", + "\n", + "import datetime" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Support Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def set_performance():\n", + " target.cpufreq.set_all_governors('performance')\n", + "\n", + "def set_powersave():\n", + " target.cpufreq.set_all_governors('powersave')\n", + "\n", + "def set_interactive():\n", + " target.cpufreq.set_all_governors('interactive')\n", + "\n", + "def set_sched():\n", + " target.cpufreq.set_all_governors('sched')\n", + "\n", + "def set_ondemand():\n", + " target.cpufreq.set_all_governors('ondemand')\n", + " \n", + " for cpu in target.list_online_cpus():\n", + " tunables = target.cpufreq.get_governor_tunables(cpu)\n", + " target.cpufreq.set_governor_tunables(\n", + " cpu,\n", + " 'ondemand',\n", + " **{'sampling_rate' : tunables['sampling_rate_min']}\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def experiment(wl, res_dir, conf_name, wload_name, collect=''):\n", + " \n", + " ##################################\n", + " # Initial setup\n", + "\n", + " # Load workload params\n", + " wload_kind = wload_name.split()[0]\n", + " iterations = int(wload_name.split()[1])\n", + " wload_tag = wload_name.split()[2]\\\n", + " .replace('https://youtu.be/', '')\\\n", + " .replace('?t=', '_')\n", + " \n", + " # Check for workload being available\n", + " wload = Workload.get(te, wload_kind)\n", + " if not wload:\n", + " return {}\n", + " \n", + " # Setup test results folder\n", + " exp_dir = os.path.join(res_dir, conf_name, \"{}_{}\".format(wload_kind, wload_tag))\n", + " os.system('mkdir -p {}'.format(exp_dir));\n", + "\n", + " # Configure governor\n", + " confs[conf_name]['set']()\n", + " \n", + " # Unlock device screen (assume no password required)\n", + " target.execute('input keyevent 82')\n", + " # Configure screen to max brightness and no dimming\n", + " Screen.set_brightness(target, percent=100)\n", + " Screen.set_dim(target, auto=False)\n", + " Screen.set_timeout(target, 60*60*10) # 10 hours should be enought for an experiment\n", + " \n", + " ####################################\n", + " # Start the required tracing command\n", + " \n", + " if 'ftrace' in collect:\n", + " # Start FTrace and Energy monitoring\n", + " te.ftrace.start()\n", + " elif 'systrace' in collect:\n", + " # Get the systrace time\n", + " match = re.search(r'systrace_([0-9]+)', collect)\n", + " if match:\n", + " systrace_time = match.group(1)\n", + " else:\n", + " logging.warning(\"Systrace time NOT defined, tracing for 10[s]\")\n", + " systrace_time = 10\n", + " # Start systrace\n", + " trace_file = os.path.join(te.res_dir, 'trace.html')\n", + " systrace_output = System.systrace_start(te, trace_file, systrace_time)\n", + " \n", + " \n", + " ###########################\n", + " # Run the required workload\n", + " \n", + " # Jankbench\n", + " if 'Jankbench' in wload_name:\n", + " db_file, nrg_report = wload.run(exp_dir, wload_tag, iterations, collect)\n", + "\n", + " # UiBench\n", + " elif 'UiBench' in wload_name:\n", + " test_name = wload_name.split()[2]\n", + " duration_s = int(wload_name.split()[3])\n", + " db_file, nrg_report = wload.run(exp_dir, test_name, duration_s)\n", + "\n", + " # YouTube\n", + " elif 'YouTube' in wload_name:\n", + " video_url = wload_name.split()[2]\n", + " video_duration_s = int(wload_name.split()[3])\n", + " db_file, nrg_report = wload.run(exp_dir, video_url, video_duration_s)\n", + "\n", + " # RTApp based workloads\n", + " elif 'RTApp' in wload_name:\n", + " rtapp_kind = wload_name.replace('RTApp ', '')\n", + " db_file, nrg_report = rtapp_run(rtapp_kind)\n", + "\n", + " \n", + " ###########################\n", + " # Reset and return results\n", + "\n", + " # Stop the required trace command\n", + " if 'ftrace' in collect:\n", + " te.ftrace.stop()\n", + " # Collect and keep track of the trace\n", + " trace_file = os.path.join(exp_dir, 'trace.dat')\n", + " te.ftrace.get_trace(trace_file)\n", + " elif 'systrace' in collect:\n", + " if systrace_output:\n", + " logging.info('Waiting systrace report [%s]...', trace_file)\n", + " systrace_output.wait()\n", + " else:\n", + " logging.warning('Systrace is not running!')\n", + "\n", + " # Reset screen brightness and auto dimming\n", + " Screen.set_defaults(target)\n", + " \n", + " # Dump platform descriptor\n", + " te.platform_dump(exp_dir)\n", + "\n", + " # return all the experiment data\n", + " if 'trace' in collect:\n", + " return {\n", + " 'dir' : exp_dir,\n", + " 'db_file' : db_file,\n", + " 'nrg_report' : nrg_report,\n", + " 'trace_file' : trace_file,\n", + " }\n", + " else:\n", + " return {\n", + " 'dir' : exp_dir,\n", + " 'db_file' : db_file,\n", + " 'nrg_report' : nrg_report,\n", + " 'nrg_file' : nrg_file,\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_experiments(test_confs, wloads, confs, verbose=False):\n", + "\n", + " # Make sure we have a list of configuraitons to test\n", + " if not isinstance(test_confs, list):\n", + " test_confs = [test_confs]\n", + "\n", + " # Intialize Workloads for this test environment\n", + " wl = Workload(te)\n", + "\n", + " # Change to info once the notebook runs ok\n", + " if verbose:\n", + " LisaLogging.setup(level=logging.DEBUG)\n", + " else:\n", + " LisaLogging.setup(level=logging.INFO)\n", + "\n", + " # The set of results for each comparison test\n", + " results = collections.defaultdict(dict)\n", + "\n", + " # Run the benchmark in all the configured configurations\n", + " for conf_name in test_confs:\n", + "\n", + " # Setup data to be collected\n", + " try:\n", + " collect = confs[conf_name]['collect']\n", + " logging.info(\"Enabling collection of: %s\", collect)\n", + " except:\n", + " collect = ''\n", + "\n", + " # Enable energy collection only if an emeter has been configured\n", + " if 'energy' in collect:\n", + " if 'emeter' not in my_conf or not te.emeter:\n", + " logging.warning('Disabling ENERGY collection')\n", + " logging.info('EMeter not configured or not available')\n", + " collect = collect.replace('energy', '')\n", + " else:\n", + " logging.debug('Enabling ENERGY collection')\n", + "\n", + " # Run each workload\n", + " idx = 0\n", + " for wload_name in wloads:\n", + " \n", + " # Skip workload if not enabled by the configuration\n", + " try:\n", + " enabled = False\n", + " enabled_workloads = confs[conf_name]['wloads']\n", + " for wload in enabled_workloads:\n", + " if wload in wload_name:\n", + " enabled = True\n", + " break\n", + " if not enabled:\n", + " logging.debug('Workload [%s] disabled',\n", + " wload_name)\n", + " continue\n", + " except:\n", + " # No workload filters defined, execute all workloads\n", + " logging.debug('All workloads enabled')\n", + " pass\n", + "\n", + " # Log test being executed\n", + " idx = idx + 1\n", + " wload_kind = wload_name.split()[0]\n", + " logging.info('------------------------')\n", + " logging.info('Test %d: %s in %s configuration',\n", + " idx, wload_kind.upper(), conf_name.upper())\n", + " logging.info(' %s', wload_name)\n", + " \n", + " res = experiment(wl, te.res_dir, conf_name, wload_name, collect)\n", + " results[conf_name][wload_name] = copy.deepcopy(res)\n", + "\n", + " # Save collected results\n", + " res_file = os.path.join(te.res_dir, conf_name, 'results.json')\n", + " with open(res_file, 'w') as fh:\n", + " json.dump(results[conf_name], fh, indent=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test environment setup\n", + "For more details on this please check out **examples/utils/testenv_example.ipynb**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**devlib** requires the ANDROID_HOME environment variable configured to point to your local installation of the Android SDK. If you have not this variable configured in the shell used to start the notebook server, you need to run a cell to define where your Android SDK is installed or specify the ANDROID_HOME in your target configuration." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case more than one Android device are conencted to the host, you must specify the ID of the device you want to target in **my_target_conf**. Run **adb devices** on your host to get the ID." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup target configuration\n", + "my_conf = {\n", + "\n", + " # Target platform and board\n", + " \"platform\" : 'android',\n", + " \"device\" : \"HT6670300102\",\n", + " \"ANDROID_HOME\" : '/home/vagrant/lisa/tools/android-sdk-linux/',\n", + "\n", + " # Folder where all the results will be collected\n", + " \"results_dir\" : \"Android_Multiple_Workloads\",\n", + "\n", + " # Define devlib modules to load\n", + " \"modules\" : [\n", + " 'cpufreq' # enable CPUFreq support\n", + " ],\n", + "\n", + " # FTrace events to collect for all the tests configuration which have\n", + " # the \"ftrace\" flag enabled\n", + " \"ftrace\" : {\n", + " \"events\" : [\n", + " \"sched_switch\",\n", + " \"sched_overutilized\",\n", + " \"sched_contrib_scale_f\",\n", + " \"sched_load_avg_cpu\",\n", + " \"sched_load_avg_task\",\n", + " \"sched_tune_tasks_update\",\n", + " \"sched_boost_cpu\",\n", + " \"sched_boost_task\",\n", + " \"sched_energy_diff\",\n", + " \"cpu_frequency\",\n", + " \"cpu_idle\",\n", + " \"cpu_capacity\",\n", + " ],\n", + " \"buffsize\" : 10 * 1024,\n", + " },\n", + "\n", + " # Tools required by the experiments\n", + " \"tools\" : [ 'trace-cmd' ],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# List of possible workloads to run, each workload consists of a workload name\n", + "# followed by a list of workload specific parameters\n", + "test_wloads = [\n", + "# YouTube workload:\n", + "# Params:\n", + "# - iterations: number of read/write operations to execute\n", + "# - URL: link to the video to use (with optional start time)\n", + "# - duration: playback time in [s]\n", + " 'YouTube 1 https://youtu.be/XSGBVzeBUbk?t=45s 60',\n", + "\n", + "# Jankbench workload:\n", + "# Params:\n", + "# - iterations: number of read/write operations to execute\n", + "# - id: benchmakr to run\n", + " 'Jankbench 1 list_view',\n", + " 'Jankbench 1 image_list_view',\n", + " 'Jankbench 1 shadow_grid',\n", + " 'Jankbench 1 low_hitrate_text',\n", + " 'Jankbench 1 high_hitrate_text',\n", + " 'Jankbench 1 edit_text',\n", + " \n", + " # Multi iterations\n", + " 'Jankbench 3 list_view',\n", + " 'Jankbench 3 image_list_view',\n", + " 'Jankbench 3 shadow_grid',\n", + " 'Jankbench 3 low_hitrate_text',\n", + " 'Jankbench 3 high_hitrate_text',\n", + " 'Jankbench 3 edit_text',\n", + "\n", + "# UiBench workload:\n", + "# Params:\n", + "# - test_name: The name of the test to start\n", + "# - duration: playback time in [s]\n", + " 'UiBench 1 TrivialAnimation 10',\n", + "\n", + "# RT-App workload:\n", + "# Params:\n", + "# - configration: tasks configuration to run\n", + "# - [configuration specific parameters]\n", + " 'RTApp STAccount 6',\n", + " 'RTApp RAMP',\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Available test configurations\n", + "# 'set' : a setup function to be called before starting the test\n", + "# 'collect' defines what we want to collect as a list of strings.\n", + "# Supported values are\n", + "# energy - Use the my_conf's defined emeter to measure energy consumption across experiments\n", + "# ftrace - Collect an execution trace using trace-cmd\n", + "# systrace - Collect an execution trace using Systrace/Atrace\n", + "# NOTE: energy is automatically enabled in case an \"emeter\" configuration is defined in my_conf\n", + "\n", + "confs = {\n", + " 'std' : {\n", + " 'set' : set_interactive,\n", + " 'wloads' : ['Jankbench 1 list_view'],\n", + " 'collect' : 'ftrace',\n", + " },\n", + " 'eas' : {\n", + " 'set' : set_sched,\n", + " 'wloads' : ['Jankbench 1 list_view'],\n", + " 'collect' : 'ftrace',\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# List of experiments to run\n", + "experiments = ['std', 'eas']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-09 17:56:54,280 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-09 17:56:54,282 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-09 17:56:54,283 INFO : TestEnv : External tools using:\n", + "2016-12-09 17:56:54,284 INFO : TestEnv : ANDROID_HOME: /home/vagrant/lisa/tools/android-sdk-linux/\n", + "2016-12-09 17:56:54,284 INFO : TestEnv : CATAPULT_HOME: /home/vagrant/lisa/tools/catapult\n", + "2016-12-09 17:56:54,284 INFO : TestEnv : Devlib modules to load: ['cpufreq']\n", + "2016-12-09 17:56:54,285 INFO : TestEnv : Connecting Android target [HT6670300102]\n", + "2016-12-09 17:56:54,285 INFO : TestEnv : Connection settings:\n", + "2016-12-09 17:56:54,286 INFO : TestEnv : {'device': 'HT6670300102'}\n", + "2016-12-09 17:56:54,468 INFO : android : ls command is set to ls -1\n", + "2016-12-09 17:56:55,511 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-09 17:56:55,513 INFO : TestEnv : /data/local/tmp/devlib-target\n", + "2016-12-09 17:56:58,803 INFO : TestEnv : Topology:\n", + "2016-12-09 17:56:58,806 INFO : TestEnv : [[0, 1], [2, 3]]\n", + "2016-12-09 17:56:59,847 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-09 17:56:59,848 INFO : TestEnv : sched_switch\n", + "2016-12-09 17:56:59,848 INFO : TestEnv : sched_overutilized\n", + "2016-12-09 17:56:59,848 INFO : TestEnv : sched_contrib_scale_f\n", + "2016-12-09 17:56:59,849 INFO : TestEnv : sched_load_avg_cpu\n", + "2016-12-09 17:56:59,849 INFO : TestEnv : sched_load_avg_task\n", + "2016-12-09 17:56:59,850 INFO : TestEnv : sched_tune_tasks_update\n", + "2016-12-09 17:56:59,850 INFO : TestEnv : sched_boost_cpu\n", + "2016-12-09 17:56:59,850 INFO : TestEnv : sched_boost_task\n", + "2016-12-09 17:56:59,851 INFO : TestEnv : sched_energy_diff\n", + "2016-12-09 17:56:59,852 INFO : TestEnv : cpu_frequency\n", + "2016-12-09 17:56:59,854 INFO : TestEnv : cpu_idle\n", + "2016-12-09 17:56:59,855 INFO : TestEnv : cpu_capacity\n", + "2016-12-09 17:56:59,856 INFO : TestEnv : Set results folder to:\n", + "2016-12-09 17:56:59,856 INFO : TestEnv : /home/vagrant/lisa/results/Android_Multiple_Workloads\n", + "2016-12-09 17:56:59,857 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-09 17:56:59,857 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" + ] + } + ], + "source": [ + "# Initialize a test environment using:\n", + "te = TestEnv(my_conf, wipe=False)\n", + "target = te.target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Workloads Execution and Data Collection" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-09 17:57:12,090 INFO : Workload : Workloads available on target:\n", + "2016-12-09 17:57:12,093 INFO : Workload : ['YouTube', 'Jankbench', 'UiBench']\n", + "2016-12-09 17:57:12,098 INFO : root : Using LISA logging configuration:\n", + "2016-12-09 17:57:12,099 INFO : root : /home/vagrant/lisa/logging.conf\n", + "2016-12-09 17:57:12,101 INFO : root : Enabling collection of: ftrace\n", + "2016-12-09 17:57:12,102 DEBUG : root : Workload [YouTube 1 https://youtu.be/XSGBVzeBUbk?t=45s 60] disabled\n", + "2016-12-09 17:57:12,103 INFO : root : ------------------------\n", + "2016-12-09 17:57:12,104 INFO : root : Test 1: JANKBENCH in STD configuration\n", + "2016-12-09 17:57:12,104 INFO : root : Jankbench 1 list_view\n", + "2016-12-09 17:57:12,105 DEBUG : Jankbench : Workload created\n", + "2016-12-09 17:57:13,628 INFO : Screen : Set brightness: 100%\n", + "2016-12-09 17:57:14,052 INFO : Screen : Dim screen mode: OFF\n", + "2016-12-09 17:57:14,495 INFO : Screen : Screen timeout: 36000 [s]\n", + "2016-12-09 17:57:18,243 INFO : Screen : Force manual orientation\n", + "2016-12-09 17:57:18,245 INFO : Screen : Set orientation: PORTRAIT\n", + "2016-12-09 17:57:19,220 DEBUG : Jankbench : Start Jank Benchmark [0:list_view]\n", + "2016-12-09 17:57:19,222 INFO : Jankbench : am start -n \"com.android.benchmark/.app.RunLocalBenchmarksActivity\" --eia \"com.android.benchmark.EXTRA_ENABLED_BENCHMARK_IDS\" 0 --ei \"com.android.benchmark.EXTRA_RUN_COUNT\" 1\n", + "2016-12-09 17:57:19,672 INFO : Jankbench : adb -s HT6670300102 logcat ActivityManager:* System.out:I *:S BENCH:*\n", + "2016-12-09 17:57:19,673 DEBUG : Jankbench : Iterations:\n", + "2016-12-09 17:57:19,763 DEBUG : Jankbench : Benchmark started!\n", + "2016-12-09 17:57:20,781 DEBUG : Jankbench : Iteration 1:\n", + "2016-12-09 17:57:54,518 INFO : Jankbench : Mean: 54.182 JankP: 0.061 StdDev: 0.000 Count Bad: 1 Count Jank: 1\n", + "2016-12-09 17:57:55,554 DEBUG : Jankbench : Benchmark done!\n", + "2016-12-09 17:57:57,168 INFO : Screen : Set orientation: AUTO\n", + "2016-12-09 17:58:08,777 INFO : Screen : Set orientation: AUTO\n", + "2016-12-09 17:58:10,025 INFO : Screen : Set brightness: AUTO\n", + "2016-12-09 17:58:10,439 INFO : Screen : Dim screen mode: ON\n", + "2016-12-09 17:58:10,869 INFO : Screen : Screen timeout: 30 [s]\n", + "2016-12-09 17:58:10,871 DEBUG : root : Workload [Jankbench 1 image_list_view] disabled\n", + "2016-12-09 17:58:10,871 DEBUG : root : Workload [Jankbench 1 shadow_grid] disabled\n", + "2016-12-09 17:58:10,872 DEBUG : root : Workload [Jankbench 1 low_hitrate_text] disabled\n", + "2016-12-09 17:58:10,873 DEBUG : root : Workload [Jankbench 1 high_hitrate_text] disabled\n", + "2016-12-09 17:58:10,873 DEBUG : root : Workload [Jankbench 1 edit_text] disabled\n", + "2016-12-09 17:58:10,874 DEBUG : root : Workload [Jankbench 3 list_view] disabled\n", + "2016-12-09 17:58:10,875 DEBUG : root : Workload [Jankbench 3 image_list_view] disabled\n", + "2016-12-09 17:58:10,875 DEBUG : root : Workload [Jankbench 3 shadow_grid] disabled\n", + "2016-12-09 17:58:10,876 DEBUG : root : Workload [Jankbench 3 low_hitrate_text] disabled\n", + "2016-12-09 17:58:10,877 DEBUG : root : Workload [Jankbench 3 high_hitrate_text] disabled\n", + "2016-12-09 17:58:10,877 DEBUG : root : Workload [Jankbench 3 edit_text] disabled\n", + "2016-12-09 17:58:10,878 DEBUG : root : Workload [UiBench 1 TrivialAnimation 10] disabled\n", + "2016-12-09 17:58:10,879 DEBUG : root : Workload [RTApp STAccount 6] disabled\n", + "2016-12-09 17:58:10,879 DEBUG : root : Workload [RTApp RAMP] disabled\n", + "2016-12-09 17:58:10,880 INFO : root : Enabling collection of: ftrace\n", + "2016-12-09 17:58:10,880 DEBUG : root : Workload [YouTube 1 https://youtu.be/XSGBVzeBUbk?t=45s 60] disabled\n", + "2016-12-09 17:58:10,881 INFO : root : ------------------------\n", + "2016-12-09 17:58:10,881 INFO : root : Test 1: JANKBENCH in EAS configuration\n", + "2016-12-09 17:58:10,881 INFO : root : Jankbench 1 list_view\n", + "2016-12-09 17:58:10,882 DEBUG : Jankbench : Workload created\n", + "2016-12-09 17:58:12,286 INFO : Screen : Set brightness: 100%\n", + "2016-12-09 17:58:12,701 INFO : Screen : Dim screen mode: OFF\n", + "2016-12-09 17:58:13,566 INFO : Screen : Screen timeout: 36000 [s]\n", + "2016-12-09 17:58:21,439 INFO : Screen : Force manual orientation\n", + "2016-12-09 17:58:21,440 INFO : Screen : Set orientation: PORTRAIT\n", + "2016-12-09 17:58:22,871 DEBUG : Jankbench : Start Jank Benchmark [0:list_view]\n", + "2016-12-09 17:58:22,874 INFO : Jankbench : am start -n \"com.android.benchmark/.app.RunLocalBenchmarksActivity\" --eia \"com.android.benchmark.EXTRA_ENABLED_BENCHMARK_IDS\" 0 --ei \"com.android.benchmark.EXTRA_RUN_COUNT\" 1\n", + "2016-12-09 17:58:23,476 INFO : Jankbench : adb -s HT6670300102 logcat ActivityManager:* System.out:I *:S BENCH:*\n", + "2016-12-09 17:58:23,477 DEBUG : Jankbench : Iterations:\n", + "2016-12-09 17:58:23,574 DEBUG : Jankbench : Benchmark started!\n", + "2016-12-09 17:58:24,622 DEBUG : Jankbench : Iteration 1:\n", + "2016-12-09 17:58:58,616 INFO : Jankbench : Mean: 40.867 JankP: 0.061 StdDev: 47.953 Count Bad: 3 Count Jank: 1\n", + "2016-12-09 17:58:59,668 DEBUG : Jankbench : Benchmark done!\n", + "2016-12-09 17:59:01,499 INFO : Screen : Set orientation: AUTO\n", + "2016-12-09 17:59:20,392 INFO : Screen : Set orientation: AUTO\n", + "2016-12-09 17:59:22,002 INFO : Screen : Set brightness: AUTO\n", + "2016-12-09 17:59:22,484 INFO : Screen : Dim screen mode: ON\n", + "2016-12-09 17:59:23,025 INFO : Screen : Screen timeout: 30 [s]\n", + "2016-12-09 17:59:23,026 DEBUG : root : Workload [Jankbench 1 image_list_view] disabled\n", + "2016-12-09 17:59:23,027 DEBUG : root : Workload [Jankbench 1 shadow_grid] disabled\n", + "2016-12-09 17:59:23,029 DEBUG : root : Workload [Jankbench 1 low_hitrate_text] disabled\n", + "2016-12-09 17:59:23,031 DEBUG : root : Workload [Jankbench 1 high_hitrate_text] disabled\n", + "2016-12-09 17:59:23,032 DEBUG : root : Workload [Jankbench 1 edit_text] disabled\n", + "2016-12-09 17:59:23,034 DEBUG : root : Workload [Jankbench 3 list_view] disabled\n", + "2016-12-09 17:59:23,035 DEBUG : root : Workload [Jankbench 3 image_list_view] disabled\n", + "2016-12-09 17:59:23,035 DEBUG : root : Workload [Jankbench 3 shadow_grid] disabled\n", + "2016-12-09 17:59:23,036 DEBUG : root : Workload [Jankbench 3 low_hitrate_text] disabled\n", + "2016-12-09 17:59:23,036 DEBUG : root : Workload [Jankbench 3 high_hitrate_text] disabled\n", + "2016-12-09 17:59:23,036 DEBUG : root : Workload [Jankbench 3 edit_text] disabled\n", + "2016-12-09 17:59:23,037 DEBUG : root : Workload [UiBench 1 TrivialAnimation 10] disabled\n", + "2016-12-09 17:59:23,037 DEBUG : root : Workload [RTApp STAccount 6] disabled\n", + "2016-12-09 17:59:23,037 DEBUG : root : Workload [RTApp RAMP] disabled\n" + ] + } + ], + "source": [ + "run_experiments(experiments, test_wloads, confs, True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/vagrant/lisa/results/Android_Multiple_Workloads\r\n", + "├── eas\r\n", + "│   ├── Jankbench_list_view\r\n", + "│   │   ├── BenchmarkResults\r\n", + "│   │   ├── platform.json\r\n", + "│   │   └── trace.dat\r\n", + "│   └── results.json\r\n", + "└── std\r\n", + " ├── Jankbench_list_view\r\n", + " │   ├── BenchmarkResults\r\n", + " │   ├── platform.json\r\n", + " │   └── trace.dat\r\n", + " └── results.json\r\n", + "\r\n", + "4 directories, 8 files\r\n" + ] + } + ], + "source": [ + "!tree {te.res_dir}" + ] + } + ], + "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": "2.7.6" + }, + "toc": { + "toc_cell": false, + "toc_number_sections": true, + "toc_threshold": 6, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ipynb/examples/android/workloads/Android_YouTube.ipynb b/ipynb/examples/android/workloads/Android_YouTube.ipynb new file mode 100644 index 00000000..fce6977b --- /dev/null +++ b/ipynb/examples/android/workloads/Android_YouTube.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Youtube on Android\n", + "\n", + "The goal of this experiment is to run Youtube videos on a Pixel device running Android and collect results." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-09 16:49:21,614 INFO : root : Using LISA logging configuration:\n", + "2016-12-09 16:49:21,614 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], + "source": [ + "from conf import LisaLogging\n", + "LisaLogging.setup()" + ] + }, + { + "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": [ + "%pylab inline\n", + "\n", + "import json\n", + "import os\n", + "\n", + "# Support to access the remote target\n", + "import devlib\n", + "from env import TestEnv\n", + "\n", + "# Import support for Android devices\n", + "from android import Screen, Workload\n", + "\n", + "# Support for trace events analysis\n", + "from trace import Trace\n", + "#from trace_analysis import TraceAnalysis\n", + "\n", + "# Suport for FTrace events parsing and visualization\n", + "import trappy\n", + "\n", + "import pandas as pd\n", + "import sqlite3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Support Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function helps us run our experiments:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def experiment():\n", + " # Unlock device screen (assume no password required)\n", + " target.execute('input keyevent 82')\n", + " \n", + " # Configure governor\n", + " target.cpufreq.set_all_governors('sched')\n", + " \n", + " # Configure screen to max brightness and no dimming\n", + " Screen.set_brightness(target, percent=100)\n", + " Screen.set_dim(target, auto=False)\n", + " Screen.set_timeout(target, 60*60*10) # 10 hours should be enought for an experiment\n", + " \n", + " wload = Workload(te).get(te, 'YouTube')\n", + " te.ftrace.start()\n", + " # Youtube\n", + " db_file, nrg_report = wload.run(te.res_dir, 'https://youtu.be/XSGBVzeBUbk?t=45s',\n", + " video_duration_s=60, collect='ftrace')\n", + "\n", + " # Stop the required trace command\n", + " te.ftrace.stop()\n", + " # Collect and keep track of the trace\n", + " trace_file = os.path.join(te.res_dir, 'trace.dat')\n", + " te.ftrace.get_trace(trace_file)\n", + "\n", + " # Reset screen brightness and auto dimming\n", + " Screen.set_defaults(target)\n", + " \n", + " # Dump platform descriptor\n", + " te.platform_dump(te.res_dir)\n", + "\n", + " # return all the experiment data\n", + " return {\n", + " 'dir' : te.res_dir,\n", + " 'db_file' : db_file,\n", + " 'nrg_report' : nrg_report,\n", + " 'trace_file' : trace_file,\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test environment setup\n", + "For more details on this please check out **examples/utils/testenv_example.ipynb**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**devlib** requires the ANDROID_HOME environment variable configured to point to your local installation of the Android SDK. If you have not this variable configured in the shell used to start the notebook server, you need to run a cell to define where your Android SDK is installed or specify the ANDROID_HOME in your target configuration." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case more than one Android device are conencted to the host, you must specify the ID of the device you want to target in **my_target_conf**. Run **adb devices** on your host to get the ID." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup target configuration\n", + "my_conf = {\n", + "\n", + " # Target platform and board\n", + " \"platform\" : 'android',\n", + " \"board\" : 'pixel',\n", + " \n", + " # Device\n", + " \"device\" : \"HT6670300102\",\n", + " \n", + " # Android home\n", + " \"ANDROID_HOME\" : \"/home/vagrant/lisa/tools/android-sdk-linux\",\n", + "\n", + " # Folder where all the results will be collected\n", + " \"results_dir\" : \"Youtube_example\",\n", + "\n", + " # Define devlib modules to load\n", + " \"modules\" : [\n", + " 'cpufreq' # enable CPUFreq support\n", + " ],\n", + "\n", + " # FTrace events to collect for all the tests configuration which have\n", + " # the \"ftrace\" flag enabled\n", + " \"ftrace\" : {\n", + " \"events\" : [\n", + " \"sched_switch\",\n", + " \"sched_wakeup\",\n", + " \"sched_wakeup_new\",\n", + " \"sched_overutilized\",\n", + " \"sched_load_avg_cpu\",\n", + " \"sched_load_avg_task\",\n", + " \"cpu_capacity\",\n", + " \"cpu_frequency\",\n", + " ],\n", + " \"buffsize\" : 100 * 1024,\n", + " },\n", + "\n", + " # Tools required by the experiments\n", + " \"tools\" : [ 'trace-cmd', 'taskset'],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-09 16:49:36,999 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-09 16:49:37,002 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-09 16:49:37,003 INFO : TestEnv : External tools using:\n", + "2016-12-09 16:49:37,003 INFO : TestEnv : ANDROID_HOME: /home/vagrant/lisa/tools/android-sdk-linux\n", + "2016-12-09 16:49:37,004 INFO : TestEnv : CATAPULT_HOME: /home/vagrant/lisa/tools/catapult\n", + "2016-12-09 16:49:37,004 INFO : TestEnv : Loading board:\n", + "2016-12-09 16:49:37,004 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/pixel.json\n", + "2016-12-09 16:49:37,005 INFO : TestEnv : Devlib modules to load: [u'bl', u'cpufreq']\n", + "2016-12-09 16:49:37,006 INFO : TestEnv : Connecting Android target [HT6670300102]\n", + "2016-12-09 16:49:37,006 INFO : TestEnv : Connection settings:\n", + "2016-12-09 16:49:37,006 INFO : TestEnv : {'device': 'HT6670300102'}\n", + "2016-12-09 16:49:37,153 INFO : android : ls command is set to ls -1\n", + "2016-12-09 16:49:38,334 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-09 16:49:38,337 INFO : TestEnv : /data/local/tmp/devlib-target\n", + "2016-12-09 16:49:43,018 INFO : TestEnv : Topology:\n", + "2016-12-09 16:49:43,021 INFO : TestEnv : [[0, 1], [2, 3]]\n", + "2016-12-09 16:49:43,419 INFO : TestEnv : Loading default EM:\n", + "2016-12-09 16:49:43,419 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/pixel.json\n", + "2016-12-09 16:49:44,756 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-09 16:49:44,759 INFO : TestEnv : sched_switch\n", + "2016-12-09 16:49:44,759 INFO : TestEnv : sched_wakeup\n", + "2016-12-09 16:49:44,760 INFO : TestEnv : sched_wakeup_new\n", + "2016-12-09 16:49:44,760 INFO : TestEnv : sched_overutilized\n", + "2016-12-09 16:49:44,761 INFO : TestEnv : sched_load_avg_cpu\n", + "2016-12-09 16:49:44,761 INFO : TestEnv : sched_load_avg_task\n", + "2016-12-09 16:49:44,762 INFO : TestEnv : cpu_capacity\n", + "2016-12-09 16:49:44,762 INFO : TestEnv : cpu_frequency\n", + "2016-12-09 16:49:44,763 INFO : TestEnv : Set results folder to:\n", + "2016-12-09 16:49:44,763 INFO : TestEnv : /home/vagrant/lisa/results/Youtube_example\n", + "2016-12-09 16:49:44,764 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-09 16:49:44,764 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" + ] + } + ], + "source": [ + "# Initialize a test environment using:\n", + "te = TestEnv(my_conf, wipe=False)\n", + "target = te.target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Workloads execution\n", + "\n", + "This is done using the **experiment** helper function defined above which is configured to run a **Youtube** experiment." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-09 16:49:57,206 INFO : Screen : Set brightness: 100%\n", + "2016-12-09 16:49:57,663 INFO : Screen : Dim screen mode: OFF\n", + "2016-12-09 16:49:58,236 INFO : Screen : Screen timeout: 36000 [s]\n", + "2016-12-09 16:50:00,130 INFO : Workload : Workloads available on target:\n", + "2016-12-09 16:50:00,131 INFO : Workload : ['YouTube', 'Jankbench', 'UiBench']\n", + "2016-12-09 16:50:00,132 INFO : Workload : Workloads available on target:\n", + "2016-12-09 16:50:00,132 INFO : Workload : ['YouTube', 'Jankbench', 'UiBench']\n", + "2016-12-09 16:50:03,780 INFO : Screen : Force manual orientation\n", + "2016-12-09 16:50:03,781 INFO : Screen : Set orientation: LANDSCAPE\n", + "2016-12-09 16:50:07,693 INFO : YouTube : Play video for 60 [s]\n", + "2016-12-09 16:51:09,082 INFO : Screen : Set orientation: AUTO\n", + "2016-12-09 16:52:46,576 INFO : Screen : Set orientation: AUTO\n", + "2016-12-09 16:52:48,419 INFO : Screen : Set brightness: AUTO\n", + "2016-12-09 16:52:48,953 INFO : Screen : Dim screen mode: ON\n", + "2016-12-09 16:52:49,579 INFO : Screen : Screen timeout: 30 [s]\n" + ] + } + ], + "source": [ + "# Intialize Workloads for this test environment\n", + "results = experiment()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmarks results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stats since: 105190547824392ns\r\n", + "Total frames rendered: 337\r\n", + "Janky frames: 18 (5.34%)\r\n", + "50th percentile: 6ms\r\n", + "90th percentile: 13ms\r\n", + "95th percentile: 17ms\r\n", + "99th percentile: 85ms\r\n" + ] + } + ], + "source": [ + "# Benchmark statistics\n", + "!sed '/Stats since/,/99th/!d;/99th/q' {results['db_file']}\n", + "\n", + "# For all results:\n", + "# !cat {results['db_file']}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Traces visualisation\n", + "\n", + "For more information on this please check **examples/trace_analysis/TraceAnalysis_TasksLatencies.ipynb**." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-09 16:55:08,986 INFO : Trace : Parsing FTrace format...\n", + "/home/vagrant/lisa/libs/trappy/trappy/base.py:206: UserWarning: TRAPpy: Appear to be low on memory. If errors arise, try providing more RAM\n", + " warnings.warn(\"TRAPpy: Appear to be low on memory. \"\n", + "2016-12-09 16:56:36,373 INFO : Trace : Platform clusters verified to be Frequency coherent\n", + "2016-12-09 16:57:16,324 INFO : Trace : Collected events spans a 68.598 [s] time interval\n", + "2016-12-09 16:57:16,326 INFO : Trace : Overutilized time: 7.455723 [s] (10.869% of trace time)\n", + "2016-12-09 16:57:16,327 INFO : Trace : Set plots time range to (0.000000, 68.598055)[s]\n", + "2016-12-09 16:57:16,327 INFO : Analysis : Registering trace analysis modules:\n", + "2016-12-09 16:57:16,329 INFO : Analysis : tasks\n", + "2016-12-09 16:57:16,330 INFO : Analysis : status\n", + "2016-12-09 16:57:16,331 INFO : Analysis : frequency\n", + "2016-12-09 16:57:16,332 INFO : Analysis : cpus\n", + "2016-12-09 16:57:16,333 INFO : Analysis : latency\n", + "2016-12-09 16:57:16,335 INFO : Analysis : idle\n", + "2016-12-09 16:57:16,335 INFO : Analysis : functions\n", + "2016-12-09 16:57:16,336 INFO : Analysis : eas\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + "\n", + "\n", + "\n", + " \n", + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Parse all traces\n", + "platform_file = os.path.join(te.res_dir, 'platform.json')\n", + "with open(platform_file, 'r') as fh:\n", + " platform = json.load(fh)\n", + "trace_file = os.path.join(te.res_dir, 'trace.dat')\n", + "trace = Trace(platform, trace_file, events=my_conf['ftrace']['events'])\n", + "\n", + "trappy.plotter.plot_trace(trace.ftrace)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-09 16:58:53,808 INFO : Analysis : LITTLE cluster average frequency: 0.717 GHz\n", + "2016-12-09 16:58:53,808 INFO : Analysis : big cluster average frequency: 0.872 GHz\n" + ] + }, + { + "data": { + "image/png": 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Bw3IU8HVgC0GT774uy/dlKbAPWHuCtfe2n67jPwcWAt8ENgGjgfdw7NqbXyZoYM4CLk/Y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\nl4i8Zmf7L3fjjcHzpFReLtoqo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r37LqZPn46x5eX9fjwMtmOPMT04OZ1OtJ88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h6NHG+xUVcPkyJCTg0bvnem6JAPmqpJ2+/BJycuCOO/zdElf5RT0BCAv1zftacSWCECDUR8v3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "try:\n", + " trace.analysis.frequency.plotClusterFrequencies();\n", + " logging.info('Plotting cluster frequencies for [sched]...')\n", + "except: pass" + ] + } + ], + "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": "2.7.6" + }, + "toc": { + "toc_cell": false, + "toc_number_sections": true, + "toc_threshold": 6, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ipynb/examples/devlib/cgroups_example.ipynb b/ipynb/examples/devlib/cgroups_example.ipynb new file mode 100644 index 00000000..1e1dbb43 --- /dev/null +++ b/ipynb/examples/devlib/cgroups_example.ipynb @@ -0,0 +1,1320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cgroups\n", + "\n", + "**cgroups** (abbreviated from control groups) is a Linux kernel feature that limits, accounts for, and isolates the resource usage (CPU, memory, disk I/O, network, etc.) of a collection of processes.\n", + "\n", + "A control group is a collection of processes that are bound by the same criteria and associated with a set of parameters or limits. These groups can be hierarchical, meaning that each group inherits limits from its parent group. The kernel provides access to multiple controllers (also called subsystems) through the cgroup interface, for example, the \"memory\" controller limits memory use, \"cpuacct\" accounts CPU usage, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 11:42:27,154 INFO : root : Using LISA logging configuration:\n", + "2016-12-08 11:42:27,155 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], + "source": [ + "import logging\n", + "from conf import LisaLogging\n", + "LisaLogging.setup()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import operator\n", + "\n", + "import devlib\n", + "import trappy\n", + "import bart\n", + "\n", + "from bart.sched.SchedMultiAssert import SchedMultiAssert\n", + "from wlgen import RTA, Periodic" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Target Configuration\n", + "The target configuration is used to describe and configure your test environment.\n", + "You can find more details in **examples/utils/testenv_example.ipynb**." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 11:42:29,845 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-08 11:42:29,845 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-08 11:42:29,845 INFO : TestEnv : External tools using:\n", + "2016-12-08 11:42:29,846 INFO : TestEnv : ANDROID_HOME: /home/vagrant/lisa/tools/android-sdk-linux/\n", + "2016-12-08 11:42:29,846 INFO : TestEnv : CATAPULT_HOME: /home/vagrant/lisa/tools/catapult\n", + "2016-12-08 11:42:29,847 INFO : TestEnv : Loading board:\n", + "2016-12-08 11:42:29,847 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/pixel.json\n", + "2016-12-08 11:42:29,848 INFO : TestEnv : Devlib modules to load: [u'bl', u'cpufreq', 'cgroups']\n", + "2016-12-08 11:42:29,848 INFO : TestEnv : Connecting Android target [HT6670300102]\n", + "2016-12-08 11:42:29,848 INFO : TestEnv : Connection settings:\n", + "2016-12-08 11:42:29,849 INFO : TestEnv : {'device': 'HT6670300102'}\n", + "2016-12-08 11:42:30,008 INFO : android : ls command is set to ls -1\n", + "2016-12-08 11:42:31,253 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-08 11:42:31,256 INFO : TestEnv : /data/local/tmp/devlib-target\n", + "2016-12-08 11:42:38,346 INFO : CGroups : Available controllers:\n", + "2016-12-08 11:42:39,117 INFO : CGroups : cpuset : /data/local/tmp/devlib-target/cgroups/devlib_cgh4\n", + "2016-12-08 11:42:39,840 INFO : CGroups : cpu : /data/local/tmp/devlib-target/cgroups/devlib_cgh3\n", + "2016-12-08 11:42:40,638 INFO : CGroups : cpuacct : /data/local/tmp/devlib-target/cgroups/devlib_cgh1\n", + "2016-12-08 11:42:41,416 INFO : CGroups : schedtune : /data/local/tmp/devlib-target/cgroups/devlib_cgh2\n", + "2016-12-08 11:42:42,169 INFO : CGroups : freezer : /data/local/tmp/devlib-target/cgroups/devlib_cgh0\n", + "2016-12-08 11:42:42,287 INFO : TestEnv : Topology:\n", + "2016-12-08 11:42:42,288 INFO : TestEnv : [[0, 1], [2, 3]]\n", + "2016-12-08 11:42:42,691 INFO : TestEnv : Loading default EM:\n", + "2016-12-08 11:42:42,693 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/pixel.json\n", + "2016-12-08 11:42:44,021 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-08 11:42:44,022 INFO : TestEnv : sched_switch\n", + "2016-12-08 11:42:44,022 INFO : TestEnv : Calibrating RTApp...\n", + "2016-12-08 11:42:44,259 INFO : RTApp : CPU0 calibration...\n", + "2016-12-08 11:42:44,328 INFO : Workload : Setup new workload rta_calib\n", + "2016-12-08 11:42:44,329 INFO : Workload : Workload duration defined by longest task\n", + "2016-12-08 11:42:44,330 INFO : Workload : Default policy: SCHED_OTHER\n", + "2016-12-08 11:42:44,330 INFO : Workload : ------------------------\n", + "2016-12-08 11:42:44,331 INFO : Workload : task [task1], sched: {'policy': 'FIFO', 'prio': 0}\n", + "2016-12-08 11:42:44,331 INFO : Workload : | calibration CPU: 0\n", + "2016-12-08 11:42:44,332 INFO : Workload : | loops count: 1\n", + "2016-12-08 11:42:44,334 INFO : Workload : + phase_000001: duration 1.000000 [s] (10 loops)\n", + "2016-12-08 11:42:44,335 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n", + "2016-12-08 11:42:44,335 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n", + "2016-12-08 11:42:44,466 INFO : Workload : Workload execution START:\n", + "2016-12-08 11:42:44,467 INFO : Workload : /data/local/tmp/bin/taskset 0x1 /data/local/tmp/bin/rt-app /data/local/tmp/devlib-target/rta_calib_00.json 2>&1\n", + "2016-12-08 11:42:46,114 INFO : RTApp : CPU1 calibration...\n", + "2016-12-08 11:42:46,183 INFO : Workload : Setup new workload rta_calib\n", + "2016-12-08 11:42:46,183 INFO : Workload : Workload duration defined by longest task\n", + "2016-12-08 11:42:46,184 INFO : Workload : Default policy: SCHED_OTHER\n", + "2016-12-08 11:42:46,184 INFO : Workload : ------------------------\n", + "2016-12-08 11:42:46,185 INFO : Workload : task [task1], sched: {'policy': 'FIFO', 'prio': 0}\n", + "2016-12-08 11:42:46,185 INFO : Workload : | calibration CPU: 1\n", + "2016-12-08 11:42:46,185 INFO : Workload : | loops count: 1\n", + "2016-12-08 11:42:46,186 INFO : Workload : + phase_000001: duration 1.000000 [s] (10 loops)\n", + "2016-12-08 11:42:46,186 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n", + "2016-12-08 11:42:46,186 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n", + "2016-12-08 11:42:46,320 INFO : Workload : Workload execution START:\n", + "2016-12-08 11:42:46,322 INFO : Workload : /data/local/tmp/bin/taskset 0x2 /data/local/tmp/bin/rt-app /data/local/tmp/devlib-target/rta_calib_00.json 2>&1\n", + "2016-12-08 11:42:48,012 INFO : RTApp : CPU2 calibration...\n", + "2016-12-08 11:42:48,084 INFO : Workload : Setup new workload rta_calib\n", + "2016-12-08 11:42:48,085 INFO : Workload : Workload duration defined by longest task\n", + "2016-12-08 11:42:48,086 INFO : Workload : Default policy: SCHED_OTHER\n", + "2016-12-08 11:42:48,086 INFO : Workload : ------------------------\n", + "2016-12-08 11:42:48,087 INFO : Workload : task [task1], sched: {'policy': 'FIFO', 'prio': 0}\n", + "2016-12-08 11:42:48,087 INFO : Workload : | calibration CPU: 2\n", + "2016-12-08 11:42:48,087 INFO : Workload : | loops count: 1\n", + "2016-12-08 11:42:48,088 INFO : Workload : + phase_000001: duration 1.000000 [s] (10 loops)\n", + "2016-12-08 11:42:48,088 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n", + "2016-12-08 11:42:48,088 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n", + "2016-12-08 11:42:48,220 INFO : Workload : Workload execution START:\n", + "2016-12-08 11:42:48,221 INFO : Workload : /data/local/tmp/bin/taskset 0x4 /data/local/tmp/bin/rt-app /data/local/tmp/devlib-target/rta_calib_00.json 2>&1\n", + "2016-12-08 11:42:49,900 INFO : RTApp : CPU3 calibration...\n", + "2016-12-08 11:42:49,968 INFO : Workload : Setup new workload rta_calib\n", + "2016-12-08 11:42:49,969 INFO : Workload : Workload duration defined by longest task\n", + "2016-12-08 11:42:49,969 INFO : Workload : Default policy: SCHED_OTHER\n", + "2016-12-08 11:42:49,969 INFO : Workload : ------------------------\n", + "2016-12-08 11:42:49,970 INFO : Workload : task [task1], sched: {'policy': 'FIFO', 'prio': 0}\n", + "2016-12-08 11:42:49,970 INFO : Workload : | calibration CPU: 3\n", + "2016-12-08 11:42:49,970 INFO : Workload : | loops count: 1\n", + "2016-12-08 11:42:49,971 INFO : Workload : + phase_000001: duration 1.000000 [s] (10 loops)\n", + "2016-12-08 11:42:49,971 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n", + "2016-12-08 11:42:49,971 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n", + "2016-12-08 11:42:50,103 INFO : Workload : Workload execution START:\n", + "2016-12-08 11:42:50,104 INFO : Workload : /data/local/tmp/bin/taskset 0x8 /data/local/tmp/bin/rt-app /data/local/tmp/devlib-target/rta_calib_00.json 2>&1\n", + "2016-12-08 11:42:51,757 INFO : RTApp : Target RT-App calibration:\n", + "2016-12-08 11:42:51,759 INFO : RTApp : {\"0\": 106, \"1\": 104, \"2\": 78, \"3\": 78}\n", + "2016-12-08 11:42:51,882 INFO : RTApp : big cores are ~36% more capable than LITTLE cores\n", + "2016-12-08 11:42:51,884 INFO : TestEnv : Using RT-App calibration values:\n", + "2016-12-08 11:42:51,886 INFO : TestEnv : {\"0\": 106, \"1\": 104, \"2\": 78, \"3\": 78}\n", + "2016-12-08 11:42:51,888 INFO : TestEnv : Set results folder to:\n", + "2016-12-08 11:42:51,889 INFO : TestEnv : /home/vagrant/lisa/results/20161208_114251\n", + "2016-12-08 11:42:51,891 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-08 11:42:51,893 INFO : TestEnv : /home/vagrant/lisa/results_latest\n", + "2016-12-08 11:42:51,895 INFO : root : Connected to arm64 target\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DONE\n" + ] + } + ], + "source": [ + "from env import TestEnv\n", + "\n", + "my_conf = {\n", + "\n", + " # Android Pixel\n", + " \"platform\" : \"android\",\n", + " \"board\" : \"pixel\",\n", + " \n", + " \"device\" : \"HT6670300102\",\n", + " \"ANDROID_HOME\" : \"/home/vagrant/lisa/tools/android-sdk-linux/\",\n", + " \n", + " \"exclude_modules\" : [ \"hwmon\" ],\n", + "\n", + " # List of additional devlib modules to install \n", + " \"modules\" : ['cgroups', 'bl', 'cpufreq'],\n", + " \n", + " # List of additional binary tools to install\n", + " \"tools\" : ['rt-app', 'trace-cmd'],\n", + " \n", + " # FTrace events to collect\n", + " \"ftrace\" : {\n", + " \"events\" : [\n", + " \"sched_switch\"\n", + " ],\n", + " \"buffsize\" : 10240\n", + " }\n", + "}\n", + "\n", + "te = TestEnv(my_conf, force_new=True)\n", + "target = te.target\n", + "\n", + "# Report target connection\n", + "logging.info('Connected to %s target', target.abi)\n", + "print \"DONE\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## List available Controllers\n", + "\n", + "Details on the available controllers (or subsystems) can be found at: https://www.kernel.org/doc/Documentation/cgroup-v1/." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 11:42:55,652 INFO : root : CGroup - Available controllers:\n", + "2016-12-08 11:42:55,715 INFO : root : CGroup - cpuset (hierarchy id: 4) has 7 cgroups\n", + "2016-12-08 11:42:55,717 INFO : root : CGroup - cpu (hierarchy id: 3) has 2 cgroups\n", + "2016-12-08 11:42:55,718 INFO : root : CGroup - cpuacct (hierarchy id: 1) has 87 cgroups\n", + "2016-12-08 11:42:55,718 INFO : root : CGroup - schedtune (hierarchy id: 2) has 4 cgroups\n", + "2016-12-08 11:42:55,719 INFO : root : CGroup - freezer (hierarchy id: 5) has 1 cgroups\n" + ] + } + ], + "source": [ + "logging.info('%14s - Available controllers:', 'CGroup')\n", + "ssys = target.cgroups.list_subsystems()\n", + "for (n,h,g,e) in ssys:\n", + " logging.info('%14s - %10s (hierarchy id: %d) has %d cgroups',\n", + " 'CGroup', n, h, g)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example of CPUSET controller usage\n", + "\n", + "Cpusets provide a mechanism for assigning a set of CPUs and memory nodes to a set of tasks. Cpusets constrain the CPU and memory placement of tasks to only the resources available within a task's current cpuset. They form a nested hierarchy visible in a virtual file system. These are the essential hooks, beyond what is already present, required to manage dynamic job placement on large systems.\n", + "\n", + "More information can be found in the kernel documentation: https://www.kernel.org/doc/Documentation/cgroup-v1/cpusets.txt." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Get a reference to the CPUSet controller\n", + "cpuset = target.cgroups.controller('cpuset')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 11:42:58,914 INFO : root : Existing CGropups:\n", + "2016-12-08 11:42:58,915 INFO : root : /\n", + "2016-12-08 11:42:58,916 INFO : root : /system-background\n", + "2016-12-08 11:42:58,917 INFO : root : /background\n", + "2016-12-08 11:42:58,918 INFO : root : /foreground\n", + "2016-12-08 11:42:58,918 INFO : root : /foreground/boost\n", + "2016-12-08 11:42:58,920 INFO : root : /top-app\n", + "2016-12-08 11:42:58,921 INFO : root : /camera-daemon\n" + ] + } + ], + "source": [ + "# Get the list of current configured CGroups for that controller\n", + "cgroups = cpuset.list_all()\n", + "logging.info('Existing CGropups:')\n", + "for cg in cgroups:\n", + " logging.info(' %s', cg)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 11:43:01,858 INFO : root : cpuset:/ cpus: 0-3\n", + "2016-12-08 11:43:02,054 INFO : root : cpuset:/system-background cpus: 0-2\n", + "2016-12-08 11:43:02,255 INFO : root : cpuset:/background cpus: 0\n", + "2016-12-08 11:43:02,450 INFO : root : cpuset:/foreground cpus: 0-2\n", + "2016-12-08 11:43:02,649 INFO : root : cpuset:/foreground/boost cpus: 0-2\n", + "2016-12-08 11:43:02,855 INFO : root : cpuset:/top-app cpus: 0-3\n", + "2016-12-08 11:43:03,053 INFO : root : cpuset:/camera-daemon cpus: 0-3\n" + ] + } + ], + "source": [ + "# Dump the configuraiton of each controller\n", + "for cgname in cgroups:\n", + " #print cgname\n", + " cgroup = cpuset.cgroup(cgname)\n", + " attrs = cgroup.get()\n", + " #print attrs\n", + " cpus = attrs['cpus']\n", + " logging.info('%s:%-15s cpus: %s', cpuset.kind, cgroup.name, cpus)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create a LITTLE partition\n", + "cpuset_littles = cpuset.cgroup('/LITTLE')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LITTLE:\n", + "{\n", + " \"memory_pressure\": \"0\", \n", + " \"memory_spread_page\": \"0\", \n", + " \"notify_on_release\": \"0\", \n", + " \"sched_load_balance\": \"1\", \n", + " \"cpus\": \"\", \n", + " \"effective_mems\": \"\", \n", + " \"memory_spread_slab\": \"0\", \n", + " \"mem_hardwall\": \"0\", \n", + " \"cpu_exclusive\": \"0\", \n", + " \"mem_exclusive\": \"0\", \n", + " \"ls\": \" /data/local/tmp/devlib-target/cgroups/devlib_cgh4/LITTLE/cpuset.*\", \n", + " \"mems\": \"\", \n", + " \"memory_migrate\": \"0\", \n", + " \"sched_relax_domain_level\": \"-1\", \n", + " \"effective_cpus\": \"\"\n", + "}\n" + ] + } + ], + "source": [ + "# Check the attributes available for this control group\n", + "print \"LITTLE:\\n\", json.dumps(cpuset_littles.get(), indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LITTLE:\n", + "{\n", + " \"memory_pressure\": \"0\", \n", + " \"memory_spread_page\": \"0\", \n", + " \"notify_on_release\": \"0\", \n", + " \"sched_load_balance\": \"1\", \n", + " \"cpus\": \"0-1\", \n", + " \"effective_mems\": \"0\", \n", + " \"memory_spread_slab\": \"0\", \n", + " \"mem_hardwall\": \"0\", \n", + " \"cpu_exclusive\": \"0\", \n", + " \"mem_exclusive\": \"0\", \n", + " \"ls\": \" /data/local/tmp/devlib-target/cgroups/devlib_cgh4/LITTLE/cpuset.*\", \n", + " \"mems\": \"0\", \n", + " \"memory_migrate\": \"0\", \n", + " \"sched_relax_domain_level\": \"-1\", \n", + " \"effective_cpus\": \"0-1\"\n", + "}\n" + ] + } + ], + "source": [ + "# Tune CPUs and MEMs attributes\n", + "# they must be initialize for the group to be usable\n", + "cpuset_littles.set(cpus=target.bl.littles, mems=0)\n", + "print \"LITTLE:\\n\", json.dumps(cpuset_littles.get(), indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 11:43:10,335 INFO : Workload : Setup new workload simple\n", + "2016-12-08 11:43:10,337 INFO : Workload : Workload duration defined by longest task\n", + "2016-12-08 11:43:10,338 INFO : Workload : Default policy: SCHED_OTHER\n", + "2016-12-08 11:43:10,340 INFO : Workload : ------------------------\n", + "2016-12-08 11:43:10,341 INFO : Workload : task [task0], sched: using default policy\n", + "2016-12-08 11:43:10,342 INFO : Workload : | calibration CPU: 2\n", + "2016-12-08 11:43:10,343 INFO : Workload : | loops count: 1\n", + "2016-12-08 11:43:10,343 INFO : Workload : + phase_000001: duration 5.000000 [s] (50 loops)\n", + "2016-12-08 11:43:10,344 INFO : Workload : | period 100000 [us], duty_cycle 80 %\n", + "2016-12-08 11:43:10,344 INFO : Workload : | run_time 80000 [us], sleep_time 20000 [us]\n", + "2016-12-08 11:43:10,344 INFO : Workload : ------------------------\n", + "2016-12-08 11:43:10,345 INFO : Workload : task [task1], sched: using default policy\n", + "2016-12-08 11:43:10,345 INFO : Workload : | calibration CPU: 2\n", + "2016-12-08 11:43:10,345 INFO : Workload : | loops count: 1\n", + "2016-12-08 11:43:10,346 INFO : Workload : + phase_000001: duration 5.000000 [s] (50 loops)\n", + "2016-12-08 11:43:10,346 INFO : Workload : | period 100000 [us], duty_cycle 80 %\n", + "2016-12-08 11:43:10,346 INFO : Workload : | run_time 80000 [us], sleep_time 20000 [us]\n", + "2016-12-08 11:43:10,347 INFO : Workload : ------------------------\n", + "2016-12-08 11:43:10,347 INFO : Workload : task [task2], sched: using default policy\n", + "2016-12-08 11:43:10,348 INFO : Workload : | calibration CPU: 2\n", + "2016-12-08 11:43:10,348 INFO : Workload : | loops count: 1\n", + "2016-12-08 11:43:10,348 INFO : Workload : + phase_000001: duration 5.000000 [s] (50 loops)\n", + "2016-12-08 11:43:10,349 INFO : Workload : | period 100000 [us], duty_cycle 80 %\n", + "2016-12-08 11:43:10,349 INFO : Workload : | run_time 80000 [us], sleep_time 20000 [us]\n", + "2016-12-08 11:43:10,349 INFO : Workload : ------------------------\n", + "2016-12-08 11:43:10,350 INFO : Workload : task [task3], sched: using default policy\n", + "2016-12-08 11:43:10,350 INFO : Workload : | calibration CPU: 2\n", + "2016-12-08 11:43:10,350 INFO : Workload : | loops count: 1\n", + "2016-12-08 11:43:10,351 INFO : Workload : + phase_000001: duration 5.000000 [s] (50 loops)\n", + "2016-12-08 11:43:10,351 INFO : Workload : | period 100000 [us], duty_cycle 80 %\n", + "2016-12-08 11:43:10,351 INFO : Workload : | run_time 80000 [us], sleep_time 20000 [us]\n" + ] + } + ], + "source": [ + "# Define a periodic big (80%) task\n", + "task = Periodic(\n", + " period_ms=100,\n", + " duty_cycle_pct=80,\n", + " duration_s=5).get()\n", + "\n", + "# Create one task per each CPU in the target\n", + "tasks={}\n", + "for tid in enumerate(target.core_names):\n", + " tasks['task{}'.format(tid[0])] = task\n", + "\n", + "# Configure RTA to run all these tasks\n", + "rtapp = RTA(target, 'simple', calibration=te.calibration())\n", + "rtapp.conf(kind='profile', params=tasks, run_dir=target.working_directory);" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 11:43:13,598 INFO : Workload : Workload execution START:\n", + "2016-12-08 11:43:13,601 INFO : Workload : /data/local/tmp/bin/shutils cgroups_run_into /LITTLE /data/local/tmp/bin/rt-app /data/local/tmp/devlib-target/simple_00.json 2>&1\n", + "2016-12-08 11:43:25,156 INFO : Workload : Pulling trace file into [/home/vagrant/lisa/results/20161208_114251/simple_00.dat]...\n" + ] + } + ], + "source": [ + "# Test execution of all these tasks into the LITTLE cluster\n", + "trace = rtapp.run(ftrace=te.ftrace, cgroup=cpuset_littles.name, out_dir=te.res_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "\n", + "\n", + "\n", + " \n", + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check tasks residency on little clsuter\n", + "trappy.plotter.plot_trace(trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"4659\": {\n", + " \"residency\": 100.0, \n", + " \"task_name\": \"rt-app\"\n", + " }, \n", + " \"4660\": {\n", + " \"residency\": 100.0, \n", + " \"task_name\": \"rt-app\"\n", + " }, \n", + " \"4661\": {\n", + " \"residency\": 100.0, \n", + " \"task_name\": \"rt-app\"\n", + " }, \n", + " \"4662\": {\n", + " \"residency\": 100.0, \n", + " \"task_name\": \"rt-app\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "# Compute and visualize tasks residencies on LITTLE clusterh CPUs\n", + "s = SchedMultiAssert(trappy.FTrace(trace), te.topology, execnames=tasks.keys())\n", + "residencies = s.getResidency('cluster', target.bl.littles, percent=True)\n", + "print json.dumps(residencies, indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Assert that ALL tasks have always executed only on LITTLE cluster\n", + "s.assertResidency('cluster', target.bl.littles,\n", + " 99.9, operator.ge, percent=True, rank=len(residencies))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Example of CPU controller usage\n", + "\n", + "While the CPUSET is a controller to assign CPUs and memory nodes for a set of tasks, the CPU controller is used to assign CPU bandwidth." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Get a reference to the CPU controller\n", + "cpu = target.cgroups.controller('cpu')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create a big partition on that CPUS\n", + "cpu_littles = cpu.cgroup('/LITTLE')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LITTLE:\n", + "{\n", + " \"rt_period_us\": \"1000000\", \n", + " \"shares\": \"1024\", \n", + " \"rt_runtime_us\": \"0\"\n", + "}\n" + ] + } + ], + "source": [ + "# Check the attributes available for this control group\n", + "print \"LITTLE:\\n\", json.dumps(cpu_littles.get(), indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LITTLE:\n", + "{\n", + " \"rt_period_us\": \"1000000\", \n", + " \"shares\": \"512\", \n", + " \"rt_runtime_us\": \"0\"\n", + "}\n" + ] + } + ], + "source": [ + "# Set a 1CPU equivalent bandwidth for that CGroup\n", + "# cpu_littles.set(cfs_period_us=100000, cfs_quota_us=50000)\n", + "cpu_littles.set(shares=512)\n", + "print \"LITTLE:\\n\", json.dumps(cpu_littles.get(), indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 11:44:14,920 INFO : Workload : Workload execution START:\n", + "2016-12-08 11:44:14,921 INFO : Workload : /data/local/tmp/bin/shutils cgroups_run_into /LITTLE /data/local/tmp/bin/rt-app /data/local/tmp/devlib-target/simple_00.json 2>&1\n", + "2016-12-08 11:44:26,513 INFO : Workload : Pulling trace file into [.//simple_00.dat]...\n" + ] + } + ], + "source": [ + "# Test execution of all these tasks into the LITTLE cluster\n", + "trace = rtapp.run(ftrace=te.ftrace, cgroup=cpu_littles.name)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "\n", + "\n", + "\n", + " \n", + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check tasks residency on little cluster\n", + "trappy.plotter.plot_trace(trace)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example of CPUs isolation" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Isolate CPU0\n", + "\n", + "# This works by moving all user-space tasks into a cpuset\n", + "# which does not include the specified list of CPUs to be\n", + "# isolated.\n", + "sandbox, isolated = target.cgroups.isolate(cpus=[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sandbox:\n", + "{\n", + " \"memory_pressure\": \"0\", \n", + " \"memory_spread_page\": \"0\", \n", + " \"notify_on_release\": \"0\", \n", + " \"sched_load_balance\": \"1\", \n", + " \"cpus\": \"1-3\", \n", + " \"effective_mems\": \"0\", \n", + " \"memory_spread_slab\": \"0\", \n", + " \"mem_hardwall\": \"0\", \n", + " \"cpu_exclusive\": \"0\", \n", + " \"mem_exclusive\": \"0\", \n", + " \"ls\": \" /data/local/tmp/devlib-target/cgroups/devlib_cgh4/DEVLIB_SBOX/cpuset.*\", \n", + " \"mems\": \"0\", \n", + " \"memory_migrate\": \"0\", \n", + " \"sched_relax_domain_level\": \"-1\", \n", + " \"effective_cpus\": \"1-3\"\n", + "}\n" + ] + } + ], + "source": [ + "# Check the attributes available for the SANDBOX group\n", + "print \"Sandbox:\\n\", json.dumps(sandbox.get(), indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isolated:\n", + "{\n", + " \"memory_pressure\": \"0\", \n", + " \"memory_spread_page\": \"0\", \n", + " \"notify_on_release\": \"0\", \n", + " \"sched_load_balance\": \"1\", \n", + " \"cpus\": \"0\", \n", + " \"effective_mems\": \"0\", \n", + " \"memory_spread_slab\": \"0\", \n", + " \"mem_hardwall\": \"0\", \n", + " \"cpu_exclusive\": \"0\", \n", + " \"mem_exclusive\": \"0\", \n", + " \"ls\": \" /data/local/tmp/devlib-target/cgroups/devlib_cgh4/DEVLIB_ISOL/cpuset.*\", \n", + " \"mems\": \"0\", \n", + " \"memory_migrate\": \"0\", \n", + " \"sched_relax_domain_level\": \"-1\", \n", + " \"effective_cpus\": \"0\"\n", + "}\n" + ] + } + ], + "source": [ + "# Check the attributes available for the ISOLATED group\n", + "print \"Isolated:\\n\", json.dumps(isolated.get(), indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 11:44:50,597 INFO : Workload : Workload execution START:\n", + "2016-12-08 11:44:50,601 INFO : Workload : /data/local/tmp/bin/rt-app /data/local/tmp/devlib-target/simple_00.json 2>&1\n", + "2016-12-08 11:44:57,468 INFO : Workload : Pulling trace file into [.//simple_00.dat]...\n" + ] + } + ], + "source": [ + "# Run some workload, which is expected to not run in the ISOLATED cpus:\n", + "trace = rtapp.run(ftrace=te.ftrace)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "\n", + "\n", + "\n", + " \n", + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check tasks was not running on ISOLATED CPUs\n", + "trappy.plotter.plot_trace(trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"4968\": {\n", + " \"residency\": 0.0, \n", + " \"task_name\": \"rt-app\"\n", + " }, \n", + " \"4969\": {\n", + " \"residency\": 0.0, \n", + " \"task_name\": \"rt-app\"\n", + " }, \n", + " \"4970\": {\n", + " \"residency\": 0.0, \n", + " \"task_name\": \"rt-app\"\n", + " }, \n", + " \"4971\": {\n", + " \"residency\": 0.0, \n", + " \"task_name\": \"rt-app\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "# Compute and visualize tasks residencies on ISOLATED CPUs\n", + "s = SchedMultiAssert(trappy.FTrace(trace), te.topology, execnames=tasks.keys())\n", + "residencies = s.getResidency('cpu', [0], percent=True)\n", + "print json.dumps(residencies, indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Assert that ISOLATED CPUs was not running workload tasks\n", + "s.assertResidency('cpu', [0], 0.0, operator.eq, percent=True, rank=len(residencies))" + ] + } + ], + "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": "2.7.6" + }, + "toc": { + "toc_cell": false, + "toc_number_sections": true, + "toc_threshold": 6, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ipynb/examples/energy_meter/EnergyMeter_ACME.ipynb b/ipynb/examples/energy_meter/EnergyMeter_ACME.ipynb index f80b1b47..585f91e1 100644 --- a/ipynb/examples/energy_meter/EnergyMeter_ACME.ipynb +++ b/ipynb/examples/energy_meter/EnergyMeter_ACME.ipynb @@ -4,33 +4,42 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Energy Meter Examples\n", - "
\n", - "BayLibre's ACME Cape and IIOCapture\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import Required Modules" + "# Energy Meter Examples\n", + "\n", + "## BayLibre's ACME Cape and IIOCapture\n", + "\n", + "More information can be found at https://github.com/ARM-software/lisa/wiki/Energy-Meters-Requirements#iiocapture---baylibre-acme-cape." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 12:39:45,242 INFO : root : Using LISA logging configuration:\n", + "2016-12-08 12:39:45,243 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], "source": [ "import logging\n", "from conf import LisaLogging\n", "LisaLogging.setup()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import required modules" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -56,7 +65,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Target Configuration" + "## Target Configuration\n", + "The target configuration is used to describe and configure your test environment.\n", + "You can find more details in **examples/utils/testenv_example.ipynb**." ] }, { @@ -155,7 +166,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Workload Execution and Power Consumptions Samping" + "## Workload Execution and Power Consumptions Samping\n", + "\n", + "Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**.\n", + "\n", + "Each **EnergyMeter** derived class has two main methods: **reset** and **report**.\n", + " - The **reset** method will reset the energy meter and start sampling from channels specified in the target configuration.
\n", + " - The **report** method will stop capture and will retrieve the energy consumption data. This returns an EnergyReport composed of the measured channels energy and the report file. Each of the samples can also be obtained, as you can see below." ] }, { @@ -273,7 +290,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Power Measurements Data" + "## Power Measurements Data" ] }, { @@ -469,7 +486,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.9" + "version": "2.7.6" }, "toc": { "toc_cell": false, diff --git a/ipynb/examples/energy_meter/EnergyMeter_AEP.ipynb b/ipynb/examples/energy_meter/EnergyMeter_AEP.ipynb index 64e752fb..8715550e 100644 --- a/ipynb/examples/energy_meter/EnergyMeter_AEP.ipynb +++ b/ipynb/examples/energy_meter/EnergyMeter_AEP.ipynb @@ -4,42 +4,42 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Energy Meter Examples\n", - "
\n", - "ARM Energy Probe\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook shows how to use the `AEP` energy meters.\n", + "# Energy Meter Examples\n", "\n", - "*NOTE*: `caiman` is required to collect data from the probe. Instructions on how to install it can be found here https://github.com/ARM-software/lisa/wiki/Energy-Meters-Requirements#arm-energy-probe-aep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import Required Modules" + "## ARM Energy Probe\n", + "\n", + "*NOTE*: `caiman` is required to collect data from the probe. Instructions on how to install it can be found here https://github.com/ARM-software/lisa/wiki/Energy-Meters-Requirements#arm-energy-probe-aep." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 12:40:07,000 INFO : root : Using LISA logging configuration:\n", + "2016-12-08 12:40:07,000 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], "source": [ "import logging\n", "from conf import LisaLogging\n", "LisaLogging.setup()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import required modules" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -65,7 +65,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Target Configuration" + "## Target Configuration\n", + "The target configuration is used to describe and configure your test environment.\n", + "You can find more details in **examples/utils/testenv_example.ipynb**." ] }, { @@ -163,7 +165,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Workload Execution and Power Consumptions Samping" + "## Workload Execution and Power Consumptions Samping\n", + "\n", + "Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**.\n", + "\n", + "Each **EnergyMeter** derived class has two main methods: **reset** and **report**.\n", + " - The **reset** method will reset the energy meter and start sampling from channels specified in the target configuration.
\n", + " - The **report** method will stop capture and will retrieve the energy consumption data. This returns an EnergyReport composed of the measured channels energy and the report file. Each of the samples can also be obtained, as you can see below." ] }, { @@ -275,7 +283,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Power Measurements Data" + "## Power Measurements Data" ] }, { diff --git a/ipynb/examples/energy_meter/EnergyMeter_HWMON.ipynb b/ipynb/examples/energy_meter/EnergyMeter_HWMON.ipynb index 08754ebe..dc669258 100644 --- a/ipynb/examples/energy_meter/EnergyMeter_HWMON.ipynb +++ b/ipynb/examples/energy_meter/EnergyMeter_HWMON.ipynb @@ -4,33 +4,42 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Energy Meter Examples\n", - "
\n", - "Linux Kernel HWMon\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import Required Modules" + "# Energy Meter Examples\n", + "\n", + "## Linux Kernel HWMon\n", + "\n", + "More details can be found at https://github.com/ARM-software/lisa/wiki/Energy-Meters-Requirements#linux-hwmon." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 12:40:25,090 INFO : root : Using LISA logging configuration:\n", + "2016-12-08 12:40:25,091 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], "source": [ "import logging\n", "from conf import LisaLogging\n", "LisaLogging.setup()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import required modules" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -56,7 +65,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Target Configuration" + "## Target Configuration\n", + "The target configuration is used to describe and configure your test environment.\n", + "You can find more details in **examples/utils/testenv_example.ipynb**." ] }, { @@ -152,7 +163,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Workload Execution and Power Consumptions Samping" + "## Workload Execution and Power Consumptions Samping\n", + "\n", + "Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**.\n", + "\n", + "Each **EnergyMeter** derived class has two main methods: **reset** and **report**.\n", + " - The **reset** method will reset the energy meter and start sampling from channels specified in the target configuration.
\n", + " - The **report** method will stop capture and will retrieve the energy consumption data. This returns an EnergyReport composed of the measured channels energy and the report file. Each of the samples can also be obtained, as you can see below." ] }, { @@ -266,7 +283,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Power Measurements Data" + "## Power Measurements Data" ] }, { diff --git a/ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb b/ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb index eb819d57..65ea2584 100644 --- a/ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb +++ b/ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb @@ -4,48 +4,48 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Trace Analysis Examples\n", - "
\n", - "Kernel Functions Profiling\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import Required Modules" + "# Trace Analysis Examples\n", + "\n", + "## Kernel Functions Profiling\n", + "Details on functions profiling are given in **Plot Functions Profiling Data** below." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:54:48,228 INFO : root : Using LISA logging configuration:\n", + "2016-12-12 12:54:48,229 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], "source": [ "import logging\n", "from conf import LisaLogging\n", "LisaLogging.setup()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import required modules" + ] + }, { "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" - ] - } - ], + "outputs": [], "source": [ "# Generate plots inline\n", "%matplotlib inline\n", @@ -56,6 +56,7 @@ "# Support to access the remote target\n", "import devlib\n", "from env import TestEnv\n", + "from executor import Executor\n", "\n", "# RTApp configurator for generation of PERIODIC tasks\n", "from wlgen import RTA, Ramp\n", @@ -68,7 +69,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Target Configuration" + "## Target Configuration\n", + "The target configuration is used to describe and configure your test environment.\n", + "You can find more details in **examples/utils/testenv_example.ipynb**." ] }, { @@ -86,6 +89,7 @@ " \"platform\" : 'linux',\n", " \"board\" : 'juno',\n", " \"host\" : '192.168.0.1',\n", + " \"password\" : 'juno',\n", "\n", " # Folder where all the results will be collected\n", " \"results_dir\" : \"TraceAnalysis_FunctionsProfiling\",\n", @@ -127,38 +131,38 @@ "name": "stderr", "output_type": "stream", "text": [ - "03:35:25 INFO : Target - Using base path: /home/derkling/Code/lisa\n", - "03:35:25 INFO : Target - Loading custom (inline) target configuration\n", - "03:35:25 INFO : Target - Devlib modules to load: ['bl', 'cpufreq']\n", - "03:35:25 INFO : Target - Connecting linux target:\n", - "03:35:25 INFO : Target - username : root\n", - "03:35:25 INFO : Target - host : 192.168.0.1\n", - "03:35:25 INFO : Target - password : \n", - "03:35:25 INFO : Target - Connection settings:\n", - "03:35:25 INFO : Target - {'username': 'root', 'host': '192.168.0.1', 'password': ''}\n", - "03:35:29 INFO : Target - Initializing target workdir:\n", - "03:35:29 INFO : Target - /root/devlib-target\n", - "03:35:34 INFO : Target - Topology:\n", - "03:35:34 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n", - "03:35:35 INFO : Platform - Loading default EM:\n", - "03:35:35 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/juno.json\n", - "03:35:38 INFO : FTrace - Enabled tracepoints:\n", - "03:35:38 INFO : FTrace - sched:*\n", - "03:35:38 INFO : FTrace - Kernel functions profiled:\n", - "03:35:38 INFO : FTrace - pick_next_task_fair\n", - "03:35:38 INFO : FTrace - select_task_rq_fair\n", - "03:35:38 INFO : FTrace - enqueue_task_fair\n", - "03:35:38 INFO : FTrace - update_curr_fair\n", - "03:35:38 INFO : FTrace - dequeue_task_fair\n", - "03:35:38 WARNING : Target - Using configuration provided RTApp calibration\n", - "03:35:38 INFO : Target - Using RT-App calibration values:\n", - "03:35:38 INFO : Target - {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n", - "03:35:38 INFO : EnergyMeter - HWMON module not enabled\n", - "03:35:38 WARNING : EnergyMeter - Energy sampling disabled by configuration\n", - "03:35:38 INFO : TestEnv - Set results folder to:\n", - "03:35:38 INFO : TestEnv - /home/derkling/Code/lisa/results/TraceAnalysis_FunctionsProfiling\n", - "03:35:38 INFO : TestEnv - Experiment results available also in:\n", - "03:35:38 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n" + "2016-12-07 13:11:43,327 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-07 13:11:43,328 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-07 13:11:43,329 INFO : TestEnv : Devlib modules to load: ['bl', 'cpufreq']\n", + "2016-12-07 13:11:43,329 INFO : TestEnv : Connecting linux target:\n", + "2016-12-07 13:11:43,329 INFO : TestEnv : username : root\n", + "2016-12-07 13:11:43,330 INFO : TestEnv : host : 192.168.0.1\n", + "2016-12-07 13:11:43,330 INFO : TestEnv : password : juno\n", + "2016-12-07 13:11:43,331 INFO : TestEnv : Connection settings:\n", + "2016-12-07 13:11:43,331 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n", + "2016-12-07 13:11:50,441 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-07 13:11:50,442 INFO : TestEnv : /root/devlib-target\n", + "2016-12-07 13:12:11,403 INFO : TestEnv : Topology:\n", + "2016-12-07 13:12:11,404 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n", + "2016-12-07 13:12:12,681 INFO : TestEnv : Loading default EM:\n", + "2016-12-07 13:12:12,682 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/juno.json\n", + "2016-12-07 13:12:18,266 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-07 13:12:18,267 INFO : TestEnv : sched:*\n", + "2016-12-07 13:12:18,267 INFO : TestEnv : Kernel functions profiled:\n", + "2016-12-07 13:12:18,267 INFO : TestEnv : pick_next_task_fair\n", + "2016-12-07 13:12:18,268 INFO : TestEnv : select_task_rq_fair\n", + "2016-12-07 13:12:18,268 INFO : TestEnv : enqueue_task_fair\n", + "2016-12-07 13:12:18,269 INFO : TestEnv : update_curr_fair\n", + "2016-12-07 13:12:18,269 INFO : TestEnv : dequeue_task_fair\n", + "2016-12-07 13:12:18,270 WARNING : TestEnv : Using configuration provided RTApp calibration\n", + "2016-12-07 13:12:18,270 INFO : TestEnv : Using RT-App calibration values:\n", + "2016-12-07 13:12:18,270 INFO : TestEnv : {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n", + "2016-12-07 13:12:18,272 INFO : EnergyMeter : HWMON module not enabled\n", + "2016-12-07 13:12:18,273 WARNING : EnergyMeter : Energy sampling disabled by configuration\n", + "2016-12-07 13:12:18,273 INFO : TestEnv : Set results folder to:\n", + "2016-12-07 13:12:18,274 INFO : TestEnv : /home/vagrant/lisa/results/TraceAnalysis_FunctionsProfiling\n", + "2016-12-07 13:12:18,274 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-07 13:12:18,274 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" ] } ], @@ -172,7 +176,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Workload Execution and Functions Profiling Data Collection" + "## Workload Execution and Functions Profiling Data Collection\n", + "\n", + "Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**." ] }, { @@ -224,42 +230,42 @@ "name": "stderr", "output_type": "stream", "text": [ - "03:35:38 INFO : WlGen - Setup new workload ramp\n", - "03:35:38 INFO : RTApp - Workload duration defined by longest task\n", - "03:35:38 INFO : RTApp - Default policy: SCHED_OTHER\n", - "03:35:38 INFO : RTApp - ------------------------\n", - "03:35:38 INFO : RTApp - task [ramp], sched: using default policy\n", - "03:35:38 INFO : RTApp - | calibration CPU: 1\n", - "03:35:38 INFO : RTApp - | loops count: 1\n", - "03:35:38 INFO : RTApp - + phase_000001: duration 0.500000 [s] (5 loops)\n", - "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 60 %\n", - "03:35:38 INFO : RTApp - | run_time 60000 [us], sleep_time 40000 [us]\n", - "03:35:38 INFO : RTApp - + phase_000002: duration 0.500000 [s] (5 loops)\n", - "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 55 %\n", - "03:35:38 INFO : RTApp - | run_time 55000 [us], sleep_time 45000 [us]\n", - "03:35:38 INFO : RTApp - + phase_000003: duration 0.500000 [s] (5 loops)\n", - "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 50 %\n", - "03:35:38 INFO : RTApp - | run_time 50000 [us], sleep_time 50000 [us]\n", - "03:35:38 INFO : RTApp - + phase_000004: duration 0.500000 [s] (5 loops)\n", - "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n", - "03:35:38 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n", - "03:35:38 INFO : RTApp - + phase_000005: duration 0.500000 [s] (5 loops)\n", - "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 40 %\n", - "03:35:38 INFO : RTApp - | run_time 40000 [us], sleep_time 60000 [us]\n", - "03:35:38 INFO : RTApp - + phase_000006: duration 0.500000 [s] (5 loops)\n", - "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 35 %\n", - "03:35:38 INFO : RTApp - | run_time 35000 [us], sleep_time 65000 [us]\n", - "03:35:38 INFO : RTApp - + phase_000007: duration 0.500000 [s] (5 loops)\n", - "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 30 %\n", - "03:35:38 INFO : RTApp - | run_time 30000 [us], sleep_time 70000 [us]\n", - "03:35:38 INFO : RTApp - + phase_000008: duration 0.500000 [s] (5 loops)\n", - "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n", - "03:35:38 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n", - "03:35:38 INFO : RTApp - + phase_000009: duration 0.500000 [s] (5 loops)\n", - "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n", - "03:35:38 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n", - "03:35:44 INFO : WlGen - Workload execution START:\n", - "03:35:44 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n" + "2016-12-07 13:12:22,250 INFO : Workload : Setup new workload ramp\n", + "2016-12-07 13:12:22,254 INFO : Workload : Workload duration defined by longest task\n", + "2016-12-07 13:12:22,255 INFO : Workload : Default policy: SCHED_OTHER\n", + "2016-12-07 13:12:22,256 INFO : Workload : ------------------------\n", + "2016-12-07 13:12:22,256 INFO : Workload : task [ramp], sched: using default policy\n", + "2016-12-07 13:12:22,257 INFO : Workload : | calibration CPU: 1\n", + "2016-12-07 13:12:22,257 INFO : Workload : | loops count: 1\n", + "2016-12-07 13:12:22,258 INFO : Workload : + phase_000001: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 13:12:22,258 INFO : Workload : | period 100000 [us], duty_cycle 60 %\n", + "2016-12-07 13:12:22,259 INFO : Workload : | run_time 60000 [us], sleep_time 40000 [us]\n", + "2016-12-07 13:12:22,259 INFO : Workload : + phase_000002: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 13:12:22,260 INFO : Workload : | period 100000 [us], duty_cycle 55 %\n", + "2016-12-07 13:12:22,260 INFO : Workload : | run_time 55000 [us], sleep_time 45000 [us]\n", + "2016-12-07 13:12:22,261 INFO : Workload : + phase_000003: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 13:12:22,262 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n", + "2016-12-07 13:12:22,263 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n", + "2016-12-07 13:12:22,263 INFO : Workload : + phase_000004: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 13:12:22,264 INFO : Workload : | period 100000 [us], duty_cycle 45 %\n", + "2016-12-07 13:12:22,265 INFO : Workload : | run_time 45000 [us], sleep_time 55000 [us]\n", + "2016-12-07 13:12:22,265 INFO : Workload : + phase_000005: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 13:12:22,266 INFO : Workload : | period 100000 [us], duty_cycle 40 %\n", + "2016-12-07 13:12:22,267 INFO : Workload : | run_time 40000 [us], sleep_time 60000 [us]\n", + "2016-12-07 13:12:22,267 INFO : Workload : + phase_000006: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 13:12:22,268 INFO : Workload : | period 100000 [us], duty_cycle 35 %\n", + "2016-12-07 13:12:22,268 INFO : Workload : | run_time 35000 [us], sleep_time 65000 [us]\n", + "2016-12-07 13:12:22,269 INFO : Workload : + phase_000007: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 13:12:22,269 INFO : Workload : | period 100000 [us], duty_cycle 30 %\n", + "2016-12-07 13:12:22,270 INFO : Workload : | run_time 30000 [us], sleep_time 70000 [us]\n", + "2016-12-07 13:12:22,270 INFO : Workload : + phase_000008: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 13:12:22,271 INFO : Workload : | period 100000 [us], duty_cycle 25 %\n", + "2016-12-07 13:12:22,271 INFO : Workload : | run_time 25000 [us], sleep_time 75000 [us]\n", + "2016-12-07 13:12:22,271 INFO : Workload : + phase_000009: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 13:12:22,272 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n", + "2016-12-07 13:12:22,272 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n", + "2016-12-07 13:12:35,923 INFO : Workload : Workload execution START:\n", + "2016-12-07 13:12:35,924 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n" ] } ], @@ -271,7 +277,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Parse Trace and Profiling Data" + "## Parse Trace and Profiling Data" ] }, { @@ -285,14 +291,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "03:35:53 INFO : Content of the output folder /home/derkling/Code/lisa/results/TraceAnalysis_FunctionsProfiling\n" + "2016-12-07 13:13:03,632 INFO : root : Content of the output folder /home/vagrant/lisa/results/TraceAnalysis_FunctionsProfiling\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[01;34m/home/derkling/Code/lisa/results/TraceAnalysis_FunctionsProfiling\u001b[00m\r\n", + "/home/vagrant/lisa/results/TraceAnalysis_FunctionsProfiling\r\n", "├── output.log\r\n", "├── platform.json\r\n", "├── ramp_00.json\r\n", @@ -324,14 +330,83 @@ "name": "stderr", "output_type": "stream", "text": [ - "03:35:53 INFO : LITTLE cluster max capacity: 447\n" + "2016-12-07 13:13:07,030 INFO : root : LITTLE cluster max capacity: 447\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"nrg_model\": {\n", + " \"big\": {\n", + " \"cluster\": {\n", + " \"nrg_max\": 64\n", + " }, \n", + " \"cpu\": {\n", + " \"cap_max\": 1024, \n", + " \"nrg_max\": 616\n", + " }\n", + " }, \n", + " \"little\": {\n", + " \"cluster\": {\n", + " \"nrg_max\": 57\n", + " }, \n", + " \"cpu\": {\n", + " \"cap_max\": 447, \n", + " \"nrg_max\": 93\n", + " }\n", + " }\n", + " }, \n", + " \"clusters\": {\n", + " \"big\": [\n", + " 1, \n", + " 2\n", + " ], \n", + " \"little\": [\n", + " 0, \n", + " 3, \n", + " 4, \n", + " 5\n", + " ]\n", + " }, \n", + " \"cpus_count\": 6, \n", + " \"freqs\": {\n", + " \"big\": [\n", + " 450000, \n", + " 625000, \n", + " 800000, \n", + " 950000, \n", + " 1100000\n", + " ], \n", + " \"little\": [\n", + " 450000, \n", + " 575000, \n", + " 700000, \n", + " 775000, \n", + " 850000\n", + " ]\n", + " }, \n", + " \"topology\": [\n", + " [\n", + " 0, \n", + " 3, \n", + " 4, \n", + " 5\n", + " ], \n", + " [\n", + " 1, \n", + " 2\n", + " ]\n", + " ]\n", + "}\n" ] } ], "source": [ "with open(os.path.join(res_dir, 'platform.json'), 'r') as fh:\n", " platform = json.load(fh)\n", - "#print json.dumps(platform, indent=4)\n", + "print json.dumps(platform, indent=4)\n", "logging.info('LITTLE cluster max capacity: %d',\n", " platform['nrg_model']['little']['cpu']['cap_max'])" ] @@ -347,17 +422,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "03:21:10 INFO : Parsing FTrace format...\n", - "03:21:10 INFO : Trace contains only functions stats\n", - "03:21:10 INFO : Collected events spans a 0.000 [s] time interval\n", - "03:21:10 INFO : Set plots time range to (0.000000, 0.000000)[s]\n", - "03:21:10 INFO : Registering trace analysis modules:\n", - "03:21:10 INFO : frequency\n", - "03:21:10 INFO : functions\n", - "03:21:10 INFO : tasks\n", - "03:21:10 INFO : eas\n", - "03:21:10 INFO : status\n", - "03:21:10 INFO : cpus\n" + "2016-12-07 13:13:08,084 INFO : Trace : Parsing FTrace format...\n", + "2016-12-07 13:13:08,456 INFO : Trace : Trace contains only functions stats\n", + "2016-12-07 13:13:08,457 INFO : Trace : Collected events spans a 0.000 [s] time interval\n", + "2016-12-07 13:13:08,457 INFO : Trace : Set plots time range to (0.000000, 0.000000)[s]\n", + "2016-12-07 13:13:08,461 INFO : Analysis : Registering trace analysis modules:\n", + "2016-12-07 13:13:08,465 INFO : Analysis : tasks\n", + "2016-12-07 13:13:08,468 INFO : Analysis : status\n", + "2016-12-07 13:13:08,471 INFO : Analysis : frequency\n", + "2016-12-07 13:13:08,473 INFO : Analysis : cpus\n", + "2016-12-07 13:13:08,476 INFO : Analysis : latency\n", + "2016-12-07 13:13:08,479 INFO : Analysis : idle\n", + "2016-12-07 13:13:08,481 INFO : Analysis : functions\n", + "2016-12-07 13:13:08,482 INFO : Analysis : eas\n" ] } ], @@ -369,7 +446,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Report Functions Profiling Data" + "## Report Functions Profiling Data\n", + "\n" ] }, { @@ -398,114 +476,114 @@ " \n", " 0\n", " dequeue_task_fair\n", - " 538\n", - " 3.372\n", - " 1814.54\n", - " 5.544\n", + " 2064\n", + " 9.994\n", + " 20629.08\n", + " 37.589\n", " \n", " \n", " enqueue_task_fair\n", - " 571\n", - " 3.214\n", - " 1835.56\n", - " 2.027\n", + " 701\n", + " 10.302\n", + " 7221.72\n", + " 21.210\n", " \n", " \n", " 1\n", " dequeue_task_fair\n", - " 12\n", - " 3.501\n", - " 42.02\n", - " 1.593\n", + " 570\n", + " 3.857\n", + " 2198.90\n", + " 9.192\n", " \n", " \n", " enqueue_task_fair\n", - " 17\n", - " 3.076\n", - " 52.30\n", - " 0.593\n", + " 208\n", + " 6.415\n", + " 1334.52\n", + " 15.595\n", " \n", " \n", " 2\n", " dequeue_task_fair\n", - " 1160\n", - " 2.469\n", - " 2864.78\n", - " 2.218\n", + " 148\n", + " 8.643\n", + " 1279.18\n", + " 13.554\n", " \n", " \n", " enqueue_task_fair\n", - " 1164\n", - " 2.177\n", - " 2535.18\n", - " 0.999\n", + " 433\n", + " 3.091\n", + " 1338.60\n", + " 2.320\n", " \n", " \n", " 3\n", " dequeue_task_fair\n", - " 304\n", - " 2.362\n", - " 718.34\n", - " 3.015\n", + " 171\n", + " 12.253\n", + " 2095.40\n", + " 33.150\n", " \n", " \n", " enqueue_task_fair\n", - " 279\n", - " 2.538\n", - " 708.18\n", - " 1.082\n", + " 45\n", + " 8.536\n", + " 384.14\n", + " 16.124\n", " \n", " \n", " 4\n", " dequeue_task_fair\n", - " 199\n", - " 2.646\n", - " 526.58\n", - " 2.827\n", + " 536\n", + " 6.805\n", + " 3647.66\n", + " 28.950\n", " \n", " \n", " enqueue_task_fair\n", - " 215\n", - " 2.571\n", - " 552.78\n", - " 0.903\n", + " 88\n", + " 4.474\n", + " 393.74\n", + " 8.697\n", " \n", " \n", " 5\n", " dequeue_task_fair\n", - " 88\n", - " 2.407\n", - " 211.82\n", - " 2.773\n", + " 139\n", + " 6.097\n", + " 847.56\n", + " 25.569\n", " \n", " \n", " enqueue_task_fair\n", - " 59\n", - " 2.774\n", - " 163.70\n", - " 1.491\n", + " 22\n", + " 6.029\n", + " 132.64\n", + " 15.115\n", " \n", " \n", "\n", "" ], "text/plain": [ - " hits avg time s_2\n", - "0 dequeue_task_fair 538 3.372 1814.54 5.544\n", - " enqueue_task_fair 571 3.214 1835.56 2.027\n", - "1 dequeue_task_fair 12 3.501 42.02 1.593\n", - " enqueue_task_fair 17 3.076 52.30 0.593\n", - "2 dequeue_task_fair 1160 2.469 2864.78 2.218\n", - " enqueue_task_fair 1164 2.177 2535.18 0.999\n", - "3 dequeue_task_fair 304 2.362 718.34 3.015\n", - " enqueue_task_fair 279 2.538 708.18 1.082\n", - "4 dequeue_task_fair 199 2.646 526.58 2.827\n", - " enqueue_task_fair 215 2.571 552.78 0.903\n", - "5 dequeue_task_fair 88 2.407 211.82 2.773\n", - " enqueue_task_fair 59 2.774 163.70 1.491" + " hits avg time s_2\n", + "0 dequeue_task_fair 2064 9.994 20629.08 37.589\n", + " enqueue_task_fair 701 10.302 7221.72 21.210\n", + "1 dequeue_task_fair 570 3.857 2198.90 9.192\n", + " enqueue_task_fair 208 6.415 1334.52 15.595\n", + "2 dequeue_task_fair 148 8.643 1279.18 13.554\n", + " enqueue_task_fair 433 3.091 1338.60 2.320\n", + "3 dequeue_task_fair 171 12.253 2095.40 33.150\n", + " enqueue_task_fair 45 8.536 384.14 16.124\n", + "4 dequeue_task_fair 536 6.805 3647.66 28.950\n", + " enqueue_task_fair 88 4.474 393.74 8.697\n", + "5 dequeue_task_fair 139 6.097 847.56 25.569\n", + " enqueue_task_fair 22 6.029 132.64 15.115" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -518,7 +596,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -542,66 +620,66 @@ " \n", " 0\n", " select_task_rq_fair\n", - " 783\n", - " 1.617\n", - " 1266.88\n", - " 0.896\n", + " 714\n", + " 4.641\n", + " 3314.34\n", + " 75.975\n", " \n", " \n", " 1\n", " select_task_rq_fair\n", - " 17\n", - " 0.782\n", - " 13.30\n", - " 0.031\n", + " 270\n", + " 11.346\n", + " 3063.56\n", + " 100.978\n", " \n", " \n", " 2\n", " select_task_rq_fair\n", - " 777\n", - " 1.042\n", - " 810.16\n", - " 3.248\n", + " 456\n", + " 4.223\n", + " 1925.96\n", + " 25.138\n", " \n", " \n", " 3\n", " select_task_rq_fair\n", - " 259\n", - " 1.575\n", - " 408.12\n", - " 8.924\n", + " 49\n", + " 13.006\n", + " 637.32\n", + " 89.897\n", " \n", " \n", " 4\n", " select_task_rq_fair\n", - " 186\n", - " 1.837\n", - " 341.72\n", - " 4.420\n", + " 96\n", + " 7.731\n", + " 742.18\n", + " 83.133\n", " \n", " \n", " 5\n", " select_task_rq_fair\n", - " 51\n", - " 2.557\n", - " 130.42\n", - " 13.227\n", + " 25\n", + " 11.571\n", + " 289.28\n", + " 172.983\n", " \n", " \n", "\n", "" ], "text/plain": [ - " hits avg time s_2\n", - "0 select_task_rq_fair 783 1.617 1266.88 0.896\n", - "1 select_task_rq_fair 17 0.782 13.30 0.031\n", - "2 select_task_rq_fair 777 1.042 810.16 3.248\n", - "3 select_task_rq_fair 259 1.575 408.12 8.924\n", - "4 select_task_rq_fair 186 1.837 341.72 4.420\n", - "5 select_task_rq_fair 51 2.557 130.42 13.227" + " hits avg time s_2\n", + "0 select_task_rq_fair 714 4.641 3314.34 75.975\n", + "1 select_task_rq_fair 270 11.346 3063.56 100.978\n", + "2 select_task_rq_fair 456 4.223 1925.96 25.138\n", + "3 select_task_rq_fair 49 13.006 637.32 89.897\n", + "4 select_task_rq_fair 96 7.731 742.18 83.133\n", + "5 select_task_rq_fair 25 11.571 289.28 172.983" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -616,7 +694,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Plot Functions Profiling Data" + "## Plot Functions Profiling Data\n", + "\n", + "The only method of the FunctionsAnalysis class that is used for functions profiling is **plotProfilingStats**. This method is used to plot functions profiling metrics for the specified kernel functions. For each speficied metric a barplot is generated which reports the value of the metric when the kernel function has been executed on each CPU.\n", + "The default metric is **avg** if not otherwise specified. A list of kernel functions to plot can also be passed to plotProfilingStats. Otherwise, by default, all the kernel functions are plotted." ] }, { @@ -628,9 +709,9 @@ "outputs": [ { "data": { - "image/png": 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IiIiIiIjIjDDRJyIiIiIiIjIjTPSJiIiIiIiIzAgTfSKi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\nTBw8eBDz58+Xy1Z/YEdWVhZWrlyJU6dO1fNtkLnIzc3FlStXEBgYCG9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AS0Pt88rbMSsri86doycaS5IkSZIkSaouBsM6EY2AjLDn7YHzgN3AdmBPVP/DwE4gv1aq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\nzAQaAHOApsBaghnE/6rVKiVJkiRJkiR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54IIL2mxfvHhxTjvttGy7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\nz8eoUaO49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/NAzlHzt2LJMnT244juacdNJJfOhDH6JLly507dp1q/psqvVLHJIkSZKkD5Q9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+ "image/png": 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MS5IkSZIkSVKcsZWEJEmSJEmSToj8/Hz27NkT6zKkU0pqaioZGRnHPY/BsCRJkiRJkqpd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\nCDOG60dTOg7qh8VN6AAAAACAHiQYrh+3J5nUrm1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xbmYvA7Bx1MsnVXxKBQDobYJhAOrQ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\nC63dd999Oeuss/LII4/k9ttvz5tvvpnx48dn6dKlq/tcdNFFmTJlSi6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YsGGDXD1CQ0Px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\ny5YAAA8PD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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -657,7 +738,9 @@ " 'dequeue_task_fair'\n", " ],\n", " metrics = [\n", + " # Average completion time per CPU\n", " 'avg',\n", + " # Total execution time per CPU\n", " 'time',\n", " ]\n", ")" @@ -665,16 +748,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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uof0PZtDmwo1eBQAAAEzBnPZNhDnt0A1z2gEYL+a0QxfMaQcAAIBNkNAOAAAA\nPSW0j6iqrbuuAQAAACYI7Umq6peq6ntJlg63n1ZV7+u4LAAAAMac0D7wV0mOSnJbkrTWvpnk8E4r\nAgAAYOwJ7UOttesn7Xqwk0IAAABgaNy/p33C9VX1S0laVW2R5HeSXNFxTQAAAIw5Pe0Dv53k/0my\na5IfJjlguA0AAACdqdZa1zUwA1XVvFcw+6oqif97AIyLir85YfZVVVprNdUxw+OTVNWTkrw2yRMz\n8pq01l7YVU0AAAAgtA98Jsk5ST6XZGXHtQAAAEASoX3Cva21d3ddBAAAAIwypz1JVf1mkj2TfCnJ\nvRP7W2uXdVbUJOa0QzfMaQdgvJjTDl0wp33t9k+yKMkReWh4fEvy7M4qAgAAYOzpaU9SVT9Ism9r\n7b6ua1kTPe3QDT3tAIwXPe3Qhel62n1P+8B3kmzbdREAAAAwyvD4gW2TLK2qS7P6nHZf+QYAAEBn\nhPaB07ouAAAAACYzp30TYU47dMOcdgDGiznt0AWrx69BVV3UWjusqlZk9b/KK0lrrc3rqDQAAAAY\n7572qtqitXZ/13XMhJ526IaedgDGi5526ILV49fsq10XAAAAAGsy7qF9yk8yAAAAoA/Gek57kh2r\n6nfXdLC1dtZsFgMAAACjxj20b5Zkm+hxBwAAoIfGfSG6y1prB3Vdx0xYiA66YSE6AMaLheigCxai\nWzM97AAAAPTWuIf252yIi1TV0VW1tKqurKo3TnH8xKr65vDnoqp66kzPBQAAYHyN9fD4DaGq5iS5\nMoMPAG5McmmSE1prS0faHJrkitbaT6rq6CSLW2uHzuTckWsYHg8dMDwegPFieDx0wfD4jeuQJFe1\n1pa11u5Pcm6SY0YbtNYubq39ZLh5cZJdZ3ouAAAA40toX3+7Jrl+ZPuGPBTKp/KKJF94hOcCAAAw\nRsb9K9+SJFX135OcmeQXMlicrpK01tq8Dfw8RyR5eZLDNuR1AQAAeHQS2gf+PMmvt9aueATn/jDJ\n7iPbTxjuW81w8bmzkxzdWlu+LudOWLx48arHCxcuzMKFCx9BuQAAAHRpyZIlWbJkyYzaWoguSVV9\npbX2y4/w3M2SfD+DxeRuSnJJkpeNfgBQVbsn+bcki1prF6/LuSNtLUQHHbAQHQDjxUJ00IXpFqLT\n0z7wtar6eJLPJLl3Ymdr7VNrO7G19mBVvSbJ+RmsEXBOa+2Kqjp1cLidneQtSbZP8r4aJID7W2uH\nrOncDX5MeprgAAAXJUlEQVR3AAAAbJL0tCepqg9Osbu11k6Z9WLWQE87dENPOwDjRU87dGG6nnah\nfRMhtEM3hHYAxovQDl3wPe1rUVVPqKpPV9WPhj//VFVP6LouAAAAxpvQPvDBJOcl2WX487nhPgAA\nAOiM4fFJqury1toBa9vXJcPjoRuGxwMwXgyPhy4YHr92t1XVb1bVZsOf30xyW9dFAQAAMN6E9oFT\nkrwkyc0ZfF/6cUle3mlFAAAAjD3D4zcRhsdDNwyPB2C8GB4PXZhuePzms11Mn1TVH7bW/ryq3pMp\n/ipvrb2ug7IAAAAgyZiH9iRXDP/9WqdVAAAAwBTGOrS31j43fHh3a+2To8eq6vgOSgIAAIBVzGlP\nUlWXtdYOWtu+LpnTDt0wpx2A8WJOO3TBnPY1qKpfS/K8JLtW1btHDs1L8kA3VQEAAMDAWIf2JDdm\nMJ/9hUm+PrJ/RZI3dFIRAAAADBken6SqtsjgA4zdW2vf77qeqRgeD90wPB6A8WJ4PHRhuuHxc2a7\nmJ46OsnlSb6YJFV1QFWd121JAAAAjDuhfWBxkkOS3JEkrbXLkzypy4IAAABAaB+4v7X2k0n7jAsC\nAACgU+O+EN2E71bViUk2q6q9krwuyX92XBMAAABjTk/7wGuT7Jfk3iQfS3Jnktd3WhEAAABjz+rx\nmwirx0M3rB4PwHixejx0YbrV48d6eHxVfS7T/DXeWnvhLJYDAAAAqxnr0J7kHV0XAAAAAGtiePxQ\nVW2ZZJ8Met6/31q7r+OSVmN4PHTD8HgAxovh8dAFw+PXoqqen+RvklydpJI8qapOba19odvKAAAA\nGGd62pNU1dIkL2it/WC4/eQk/9xa26fbyh6ipx26oacdgPGipx26MF1Pu698G1gxEdiHrkmyoqti\nAAAAINHTniSpqvcnmZ/kExl0qR2f5Lok/5okrbVPdVfdgJ526IaedgDGi5526MJ0Pe1Ce5Kq+uA0\nh1tr7ZRZK2YNhHbohtAOwHgR2qELQvujgNAO3RDaARgvQjt0werxa1FVT0ry2iRPzMhr0lp7YVc1\nAQAAgNA+8Jkk5yT5XJKVHdcCAAAASYT2Cfe21t7ddREAAAAwypz2JFX1m0n2TPKlJPdO7G+tXdZZ\nUZOY0w7dMKcdgPFiTjt0wZz2tds/yaIkR+Sh4fEtybM7qwgAAICxp6c9SVX9IMm+rbX7uq5lTfS0\nQzf0tAMwXvS0Qxem62mfM9vF9NR3kmzbdREAAAAwyvD4gW2TLK2qS7P6nHZf+QYAAEBnhPaB07ou\nAAAAACYzp32oqnZKcvBw85LW2o+6rGcyc9qhG+a0AzBezGmHLpjTvhZV9ZIklyQ5PslLkny1qo7r\ntioAAADGnZ72JFX1zSS/OtG7XlU7JvnX1trTuq3sIXraoRt62gEYL3raoQt62tduzqTh8LfFawMA\nAEDHLEQ38MWq+lKSjw23X5rkCx3WAwAAAIbHT6iq/57ksOHmha21T3dZz2SGx0M3DI8HYLwYHg9d\nmG54/FiH9qraM8lOrbWvTNp/WJKbWmtXd1PZwwnt0A2hHYDxIrRDF8xpX7N3Jrlziv0/GR4DAACA\nzox7aN+ptfbtyTuH+544++UAAADAQ8Y9tG87zbHHzPQiVXV0VS2tqiur6o1THP/FqvrPqvpZVf3u\npGPXVtU3q+obVXXJOtQOAADAo9y4h/avVdUrJ++sqlck+fpMLlBVc5K8N8lRSfZL8rKq2mdSs9uS\nvDbJX0xxiZVJFrbWDmytHbIuxQMAAPDoNu5f+fb6JJ+uqt/IQyH9GUm2THLsDK9xSJKrWmvLkqSq\nzk1yTJKlEw1aa7cmubWqXjDF+RUfngAAADCFsQ7trbVbkvxSVR2RZP/h7n9urf37Olxm1yTXj2zf\nkEGQn3EZSf6lqh5McnZr7e/W4VwAAAAexcY6tE9orf1Hkv/o6Ol/ubV2U1XtmEF4v6K1dtFUDRcv\nXrzq8cKFC7Nw4cLZqRAAAIANZsmSJVmyZMmM2o7197RvCFV1aJLFrbWjh9tvStJaa2dO0fa0JCta\na2et4VprPO572qEbvqcdgPHie9qhC76nfeO6NMmeVTW/qrZMckKS86Zpv+qNqKqtq2qb4eOfT3Jk\nku9szGIBAADYdBgev55aaw9W1WuSnJ/BhyDntNauqKpTB4fb2VW1U5KvJZmbZGVV/U6SfZPsmMFC\neC2D9+IjrbXzu7kTAAAA+sbw+E2E4fHQDcPjARgvhsdDFwyPBwAAgE2Q0A4AAAA9JbQDAABATwnt\nAAAA0FNCOwAAAPSU0A4AAAA9JbQDAABATwntAAAA0FNCOwAAAPSU0A4AAAA9JbQDAABATwntAAAA\n0FNCOwAAAPSU0A4AAAA9JbQDAABATwntAAAA0FNCOwAAAPSU0A4AAAA9JbQDAABATwntAAAA0FNC\nOwAAAPSU0A4AAAA9JbQDAABATwntAAAA0FNCOwAAAPSU0A4AAAA9JbQDAABATwntAAAA0FNCOwAA\nAPSU0A4AAAA9JbQDAABATwntAAAA0FNCOwAAAPSU0A4AAAA9JbQDAABATwntAAAA0FNCOwAAAPSU\n0A4AAAA9JbQDAABATwntAAAA0FNCOwAAAPSU0A4AAAA9JbQDAABATwntAAAA0FNCOwAAAPSU0A4A\nAAA9JbQDAABATwntG0BVHV1VS6vqyqp64xTHf7Gq/rOqflZVv7su5wIAADC+qrXWdQ2btKqak+TK\nJM9JcmOSS5Oc0FpbOtJmhyTzk7woyfLW2lkzPXfkGs17BbOvqpL4vwfAuKj4mxNmX1WltVZTHdPT\nvv4OSXJVa21Za+3+JOcmOWa0QWvt1tba15M8sK7nAgAAML6E9vW3a5LrR7ZvGO7b2OcCAADwKLd5\n1wUwc4sXL171eOHChVm4cGFntQAAAPDILFmyJEuWLJlRW3Pa11NVHZpkcWvt6OH2m5K01tqZU7Q9\nLcmKkTnt63KuOe3QAXPaARgv5rRDF8xp37guTbJnVc2vqi2TnJDkvGnaj74R63ouAAAAY8Tw+PXU\nWnuwql6T5PwMPgQ5p7V2RVWdOjjczq6qnZJ8LcncJCur6neS7Ntau2uqczu6FQAAAHrG8PhNhOHx\n0A3D4wEYL4bHQxcMjwcAAIBNkNAOAAAAPSW0AwAAQE8J7QAAANBTQjsAAAD0lNAOAAAAPSW0AwAA\nQE8J7QAAANBTQjsAAAD0lNAOAAAAPSW0AwAAQE8J7QAAANBTQjsAAAD0lNAOAAAAPSW0AwAAQE8J\n7QAAANBTQjsAAAD0lNAOAAAAPSW0AwAAQE8J7QAAANBTQjsAAAD0lNAOAAAAPSW0AwAAQE8J7QAA\nANBTQjsAAAD0lNAOAAAAPSW0AwAAQE8J7QAAANBTQjsAAAD0lNAOAAAAPSW0AwAAQE8J7QAAANBT\nQjsAAAD0lNAOAAAAPSW0AwAAQE8J7QAAANBTQjsAAAD0lNAOAAAAPSW0AwAAQE8J7QAAANBTQjsA\nAAD0lNAOAAAAPSW0AwAAQE8J7QAAANBTQjsAAAD0lNAOAAAAPSW0AwAAQE8J7RtAVR1dVUur6sqq\neuMa2ry7qq6qqsur6sCR/dd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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -687,15 +770,6 @@ " functions = 'update_curr_fair',\n", ")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/ipynb/examples/trace_analysis/TraceAnalysis_IdleStates.ipynb b/ipynb/examples/trace_analysis/TraceAnalysis_IdleStates.ipynb index f1aa6b0f..eaf5af3e 100644 --- a/ipynb/examples/trace_analysis/TraceAnalysis_IdleStates.ipynb +++ b/ipynb/examples/trace_analysis/TraceAnalysis_IdleStates.ipynb @@ -6,7 +6,9 @@ "collapsed": true }, "source": [ - "# Idle States Residency Analysis" + "# Trace Analysis Examples\n", + "\n", + "## Idle States Residency Analysis" ] }, { @@ -16,7 +18,9 @@ "This notebook shows the features provided by the idle state analysis module. It will be necessary to collect the following events:\n", "\n", " - `cpu_idle`, to filter out intervals of time in which the CPU is idle\n", - " - `sched_switch`, to recognise tasks on kernelshark" + " - `sched_switch`, to recognise tasks on kernelshark\n", + " \n", + "Details on idle states profiling ar given in **Per-CPU/Per-Cluster Idle State Residency Profiling** below." ] }, { @@ -28,7 +32,16 @@ "marked": false } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-07 18:19:51,925 INFO : root : Using LISA logging configuration:\n", + "2016-12-07 18:19:51,925 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], "source": [ "import logging\n", "from conf import LisaLogging\n", @@ -53,25 +66,28 @@ "# Support to access the remote target\n", "from env import TestEnv\n", "\n", + "# Support to access cpuidle information from the target\n", + "from devlib import *\n", + "\n", "# Support to configure and run RTApp based workloads\n", "from wlgen import RTA, Ramp\n", "\n", "# Support for trace events analysis\n", - "from trace import Trace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Target Configuration" + "from trace import Trace\n", + "\n", + "# DataFrame support\n", + "from pandas import DataFrame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Our target is a Juno R2 development board running Linux." + "## Target Configuration\n", + "The target configuration is used to describe and configure your test environment.\n", + "You can find more details in **examples/utils/testenv_example.ipynb**.\n", + "\n", + "Our target is a Juno R0 development board running Linux." ] }, { @@ -97,7 +113,7 @@ " \n", " # Login credentials\n", " \"username\" : 'root',\n", - " \"password\" : '',\n", + " \"password\" : 'juno',\n", " \n", " \"results_dir\" : \"IdleAnalysis\",\n", " \n", @@ -108,7 +124,7 @@ " \n", " # Tools required by the experiments\n", " \"tools\" : ['rt-app', 'trace-cmd'],\n", - " \"modules\" : ['bl', 'cpufreq'],\n", + " \"modules\" : ['bl', 'cpufreq', 'cpuidle'],\n", " \"exclude_modules\" : ['hwmon'],\n", " \n", " # FTrace events to collect for all the tests configuration which have\n", @@ -123,13 +139,6 @@ "}" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tests execution" - ] - }, { "cell_type": "code", "execution_count": 4, @@ -145,33 +154,33 @@ "name": "stderr", "output_type": "stream", "text": [ - "2016-09-02 09:25:41,477 INFO : Target - Using base path: /data/lisa\n", - "2016-09-02 09:25:41,478 INFO : Target - Loading custom (inline) target configuration\n", - "2016-09-02 09:25:41,479 INFO : Target - Devlib modules to load: ['bl', 'cpufreq']\n", - "2016-09-02 09:25:41,480 INFO : Target - Connecting linux target:\n", - "2016-09-02 09:25:41,481 INFO : Target - username : root\n", - "2016-09-02 09:25:41,482 INFO : Target - host : 192.168.0.1\n", - "2016-09-02 09:25:41,482 INFO : Target - password : \n", - "2016-09-02 09:25:41,483 INFO : Target - Connection settings:\n", - "2016-09-02 09:25:41,484 INFO : Target - {'username': 'root', 'host': '192.168.0.1', 'password': ''}\n", - "2016-09-02 09:25:45,648 INFO : Target - Initializing target workdir:\n", - "2016-09-02 09:25:45,650 INFO : Target - /root/devlib-target\n", - "2016-09-02 09:25:51,095 INFO : Target - Topology:\n", - "2016-09-02 09:25:51,097 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n", - "2016-09-02 09:25:52,729 INFO : Platform - Loading default EM:\n", - "2016-09-02 09:25:52,730 INFO : Platform - /data/lisa/libs/utils/platforms/juno.json\n", - "2016-09-02 09:25:58,291 INFO : FTrace - Enabled tracepoints:\n", - "2016-09-02 09:25:58,293 INFO : FTrace - cpu_idle\n", - "2016-09-02 09:25:58,294 INFO : FTrace - sched_switch\n", - "2016-09-02 09:25:58,295 WARNING : Target - Using configuration provided RTApp calibration\n", - "2016-09-02 09:25:58,296 INFO : Target - Using RT-App calibration values:\n", - "2016-09-02 09:25:58,298 INFO : Target - {\"0\": 318, \"1\": 125, \"2\": 124, \"3\": 318, \"4\": 318, \"5\": 319}\n", - "2016-09-02 09:25:58,299 INFO : EnergyMeter - HWMON module not enabled\n", - "2016-09-02 09:25:58,300 WARNING : EnergyMeter - Energy sampling disabled by configuration\n", - "2016-09-02 09:25:58,301 INFO : TestEnv - Set results folder to:\n", - "2016-09-02 09:25:58,302 INFO : TestEnv - /data/lisa/results/IdleAnalysis\n", - "2016-09-02 09:25:58,303 INFO : TestEnv - Experiment results available also in:\n", - "2016-09-02 09:25:58,304 INFO : TestEnv - /data/lisa/results_latest\n" + "2016-12-07 18:19:55,077 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-07 18:19:55,078 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-07 18:19:55,078 INFO : TestEnv : Devlib modules to load: ['bl', 'cpuidle', 'cpufreq']\n", + "2016-12-07 18:19:55,079 INFO : TestEnv : Connecting linux target:\n", + "2016-12-07 18:19:55,079 INFO : TestEnv : username : root\n", + "2016-12-07 18:19:55,080 INFO : TestEnv : host : 192.168.0.1\n", + "2016-12-07 18:19:55,080 INFO : TestEnv : password : juno\n", + "2016-12-07 18:19:55,081 INFO : TestEnv : Connection settings:\n", + "2016-12-07 18:19:55,081 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n", + "2016-12-07 18:20:04,495 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-07 18:20:04,497 INFO : TestEnv : /root/devlib-target\n", + "2016-12-07 18:20:28,575 INFO : TestEnv : Topology:\n", + "2016-12-07 18:20:28,576 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n", + "2016-12-07 18:20:30,241 INFO : TestEnv : Loading default EM:\n", + "2016-12-07 18:20:30,243 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/juno.json\n", + "2016-12-07 18:20:34,565 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-07 18:20:34,565 INFO : TestEnv : cpu_idle\n", + "2016-12-07 18:20:34,566 INFO : TestEnv : sched_switch\n", + "2016-12-07 18:20:34,566 WARNING : TestEnv : Using configuration provided RTApp calibration\n", + "2016-12-07 18:20:34,566 INFO : TestEnv : Using RT-App calibration values:\n", + "2016-12-07 18:20:34,567 INFO : TestEnv : {\"0\": 318, \"1\": 125, \"2\": 124, \"3\": 318, \"4\": 318, \"5\": 319}\n", + "2016-12-07 18:20:34,568 INFO : EnergyMeter : HWMON module not enabled\n", + "2016-12-07 18:20:34,570 WARNING : EnergyMeter : Energy sampling disabled by configuration\n", + "2016-12-07 18:20:34,571 INFO : TestEnv : Set results folder to:\n", + "2016-12-07 18:20:34,572 INFO : TestEnv : /home/vagrant/lisa/results/IdleAnalysis\n", + "2016-12-07 18:20:34,574 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-07 18:20:34,576 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" ] } ], @@ -185,7 +194,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Workload configuration and execution" + "## Workload configuration and execution\n", + "\n", + "Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**." ] }, { @@ -238,42 +249,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "2016-09-02 09:25:58,330 INFO : WlGen - Setup new workload ramp\n", - "2016-09-02 09:25:58,331 INFO : RTApp - Workload duration defined by longest task\n", - "2016-09-02 09:25:58,332 INFO : RTApp - Default policy: SCHED_OTHER\n", - "2016-09-02 09:25:58,333 INFO : RTApp - ------------------------\n", - "2016-09-02 09:25:58,334 INFO : RTApp - task [ramp], sched: using default policy\n", - "2016-09-02 09:25:58,335 INFO : RTApp - | calibration CPU: 1\n", - "2016-09-02 09:25:58,336 INFO : RTApp - | loops count: 1\n", - "2016-09-02 09:25:58,336 INFO : RTApp - + phase_000001: duration 0.500000 [s] (5 loops)\n", - "2016-09-02 09:25:58,337 INFO : RTApp - | period 100000 [us], duty_cycle 60 %\n", - "2016-09-02 09:25:58,338 INFO : RTApp - | run_time 60000 [us], sleep_time 40000 [us]\n", - "2016-09-02 09:25:58,339 INFO : RTApp - + phase_000002: duration 0.500000 [s] (5 loops)\n", - "2016-09-02 09:25:58,339 INFO : RTApp - | period 100000 [us], duty_cycle 55 %\n", - "2016-09-02 09:25:58,340 INFO : RTApp - | run_time 55000 [us], sleep_time 45000 [us]\n", - "2016-09-02 09:25:58,341 INFO : RTApp - + phase_000003: duration 0.500000 [s] (5 loops)\n", - "2016-09-02 09:25:58,342 INFO : RTApp - | period 100000 [us], duty_cycle 50 %\n", - "2016-09-02 09:25:58,343 INFO : RTApp - | run_time 50000 [us], sleep_time 50000 [us]\n", - "2016-09-02 09:25:58,344 INFO : RTApp - + phase_000004: duration 0.500000 [s] (5 loops)\n", - "2016-09-02 09:25:58,345 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n", - "2016-09-02 09:25:58,346 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n", - "2016-09-02 09:25:58,347 INFO : RTApp - + phase_000005: duration 0.500000 [s] (5 loops)\n", - "2016-09-02 09:25:58,347 INFO : RTApp - | period 100000 [us], duty_cycle 40 %\n", - "2016-09-02 09:25:58,348 INFO : RTApp - | run_time 40000 [us], sleep_time 60000 [us]\n", - "2016-09-02 09:25:58,349 INFO : RTApp - + phase_000006: duration 0.500000 [s] (5 loops)\n", - "2016-09-02 09:25:58,350 INFO : RTApp - | period 100000 [us], duty_cycle 35 %\n", - "2016-09-02 09:25:58,351 INFO : RTApp - | run_time 35000 [us], sleep_time 65000 [us]\n", - "2016-09-02 09:25:58,352 INFO : RTApp - + phase_000007: duration 0.500000 [s] (5 loops)\n", - "2016-09-02 09:25:58,352 INFO : RTApp - | period 100000 [us], duty_cycle 30 %\n", - "2016-09-02 09:25:58,353 INFO : RTApp - | run_time 30000 [us], sleep_time 70000 [us]\n", - "2016-09-02 09:25:58,353 INFO : RTApp - + phase_000008: duration 0.500000 [s] (5 loops)\n", - "2016-09-02 09:25:58,354 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n", - "2016-09-02 09:25:58,355 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n", - "2016-09-02 09:25:58,356 INFO : RTApp - + phase_000009: duration 0.500000 [s] (5 loops)\n", - "2016-09-02 09:25:58,356 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n", - "2016-09-02 09:25:58,357 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n", - "2016-09-02 09:26:03,072 INFO : WlGen - Workload execution START:\n", - "2016-09-02 09:26:03,074 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n" + "2016-12-07 18:20:39,906 INFO : Workload : Setup new workload ramp\n", + "2016-12-07 18:20:39,907 INFO : Workload : Workload duration defined by longest task\n", + "2016-12-07 18:20:39,908 INFO : Workload : Default policy: SCHED_OTHER\n", + "2016-12-07 18:20:39,908 INFO : Workload : ------------------------\n", + "2016-12-07 18:20:39,908 INFO : Workload : task [ramp], sched: using default policy\n", + "2016-12-07 18:20:39,909 INFO : Workload : | calibration CPU: 1\n", + "2016-12-07 18:20:39,909 INFO : Workload : | loops count: 1\n", + "2016-12-07 18:20:39,909 INFO : Workload : + phase_000001: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 18:20:39,910 INFO : Workload : | period 100000 [us], duty_cycle 60 %\n", + "2016-12-07 18:20:39,910 INFO : Workload : | run_time 60000 [us], sleep_time 40000 [us]\n", + "2016-12-07 18:20:39,910 INFO : Workload : + phase_000002: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 18:20:39,911 INFO : Workload : | period 100000 [us], duty_cycle 55 %\n", + "2016-12-07 18:20:39,911 INFO : Workload : | run_time 55000 [us], sleep_time 45000 [us]\n", + "2016-12-07 18:20:39,912 INFO : Workload : + phase_000003: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 18:20:39,912 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n", + "2016-12-07 18:20:39,913 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n", + "2016-12-07 18:20:39,913 INFO : Workload : + phase_000004: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 18:20:39,914 INFO : Workload : | period 100000 [us], duty_cycle 45 %\n", + "2016-12-07 18:20:39,914 INFO : Workload : | run_time 45000 [us], sleep_time 55000 [us]\n", + "2016-12-07 18:20:39,916 INFO : Workload : + phase_000005: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 18:20:39,916 INFO : Workload : | period 100000 [us], duty_cycle 40 %\n", + "2016-12-07 18:20:39,917 INFO : Workload : | run_time 40000 [us], sleep_time 60000 [us]\n", + "2016-12-07 18:20:39,918 INFO : Workload : + phase_000006: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 18:20:39,919 INFO : Workload : | period 100000 [us], duty_cycle 35 %\n", + "2016-12-07 18:20:39,919 INFO : Workload : | run_time 35000 [us], sleep_time 65000 [us]\n", + "2016-12-07 18:20:39,920 INFO : Workload : + phase_000007: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 18:20:39,920 INFO : Workload : | period 100000 [us], duty_cycle 30 %\n", + "2016-12-07 18:20:39,921 INFO : Workload : | run_time 30000 [us], sleep_time 70000 [us]\n", + "2016-12-07 18:20:39,921 INFO : Workload : + phase_000008: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 18:20:39,922 INFO : Workload : | period 100000 [us], duty_cycle 25 %\n", + "2016-12-07 18:20:39,923 INFO : Workload : | run_time 25000 [us], sleep_time 75000 [us]\n", + "2016-12-07 18:20:39,923 INFO : Workload : + phase_000009: duration 0.500000 [s] (5 loops)\n", + "2016-12-07 18:20:39,924 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n", + "2016-12-07 18:20:39,924 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n", + "2016-12-07 18:20:51,246 INFO : Workload : Workload execution START:\n", + "2016-12-07 18:20:51,247 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] } ], @@ -285,7 +303,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Parse trace and analyse data" + "## Parse trace and analyse data" ] }, { @@ -299,16 +317,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2016-09-02 09:26:11,282 INFO : Content of the output folder /data/lisa/results/IdleAnalysis\n" + "2016-12-07 18:21:31,546 INFO : root : Content of the output folder /home/vagrant/lisa/results/IdleAnalysis\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[01;34m/data/lisa/results/IdleAnalysis\u001b[00m\r\n", - "├── \u001b[01;35mcluster_idle_state_residency.png\u001b[00m\r\n", - "├── \u001b[01;35mcpu_idle_state_residency.png\u001b[00m\r\n", + "/home/vagrant/lisa/results/IdleAnalysis\r\n", + "├── cluster_idle_state_residency.png\r\n", + "├── cpu_idle_state_residency.png\r\n", "├── output.log\r\n", "├── platform.json\r\n", "├── ramp_00.json\r\n", @@ -342,18 +360,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "2016-09-02 09:26:11,404 INFO : Parsing FTrace format...\n", - "2016-09-02 09:26:11,766 INFO : Collected events spans a 6.927 [s] time interval\n", - "2016-09-02 09:26:11,767 INFO : Set plots time range to (0.000000, 6.927481)[s]\n", - "2016-09-02 09:26:11,768 INFO : Registering trace analysis modules:\n", - "2016-09-02 09:26:11,772 INFO : frequency\n", - "2016-09-02 09:26:11,773 INFO : functions\n", - "2016-09-02 09:26:11,774 INFO : tasks\n", - "2016-09-02 09:26:11,776 INFO : latency\n", - "2016-09-02 09:26:11,777 INFO : eas\n", - "2016-09-02 09:26:11,780 INFO : idle\n", - "2016-09-02 09:26:11,780 INFO : status\n", - "2016-09-02 09:26:11,781 INFO : cpus\n" + "2016-12-07 18:21:32,410 INFO : Trace : Parsing FTrace format...\n", + "2016-12-07 18:21:32,664 WARNING : Trace : Failed to load tasks names from trace events\n", + "2016-12-07 18:21:32,665 INFO : Trace : Collected events spans a 14.858 [s] time interval\n", + "2016-12-07 18:21:32,665 INFO : Trace : Set plots time range to (0.000000, 14.857782)[s]\n", + "2016-12-07 18:21:32,666 INFO : Analysis : Registering trace analysis modules:\n", + "2016-12-07 18:21:32,667 INFO : Analysis : tasks\n", + "2016-12-07 18:21:32,668 INFO : Analysis : status\n", + "2016-12-07 18:21:32,669 INFO : Analysis : frequency\n", + "2016-12-07 18:21:32,670 INFO : Analysis : cpus\n", + "2016-12-07 18:21:32,671 INFO : Analysis : latency\n", + "2016-12-07 18:21:32,673 INFO : Analysis : idle\n", + "2016-12-07 18:21:32,674 INFO : Analysis : functions\n", + "2016-12-07 18:21:32,675 INFO : Analysis : eas\n" ] } ], @@ -365,7 +384,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Per-CPU Idle State Residency" + "## Per-CPU Idle State Residency Profiling" ] }, { @@ -377,12 +396,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { - "collapsed": false, - "run_control": { - "marked": false - } + "collapsed": false }, "outputs": [ { @@ -403,43 +419,111 @@ " \n", " \n", " 0\n", - " 0.000000\n", + " 0.009255\n", " \n", " \n", " 1\n", - " 0.014078\n", + " 0.003381\n", " \n", " \n", " 2\n", - " 5.671125\n", + " 14.483522\n", " \n", " \n", "\n", "" ], "text/plain": [ - " time\n", - "idle_state \n", - "0 0.000000\n", - "1 0.014078\n", - "2 5.671125" + " time\n", + "idle_state \n", + "0 0.009255\n", + "1 0.003381\n", + "2 14.483522" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Idle state residency for CPU 3\n", - "trace.data_frame.cpu_idle_state_residency(3)" + "CPU=3\n", + "state_res = trace.data_frame.cpu_idle_state_residency(CPU)\n", + "state_res" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the translation between the idle value and its description:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "run_control": { + "marked": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " name value\n", + "0 CpuidleState(WFI, ARM64 WFI) 0\n", + "1 CpuidleState(cpu-sleep-0, cpu-sleep-0) 1\n", + "2 CpuidleState(cluster-sleep-0, cluster-sleep-0) 2" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DataFrame(data={'value': state_res.index.values,\n", + " 'name': [te.target.cpuidle.get_state(i, cpu=CPU) for i in state_res.index.values]})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `IdleAnalysis` module provide methods for plotting residency data." + "The **IdleAnalysis** module provide methods for plotting residency data:" ] }, { @@ -462,9 +546,9 @@ "outputs": [ { "data": { - "image/png": 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7JdlhgcL3g1X1/Dm23wLsNLohyeDf/JtnbX8rcD/gsdsYryRJY8mZXkmSxtMXgV+wdbO3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\nt6rOBK7eHvFLkjQ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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -488,9 +572,9 @@ "outputs": [ { "data": { - "image/png": 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gJ5OhA0APJkMHgDmhGAMAADBqijEskOnQAaAH06EDQE+mQweAHkyHDgBzQjEG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\n766FqvrFqnp3VV1aVWfN1h0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X2T5NwDO0I9bXrGqYn00bA2sQ/2BlvQUM6PrhSKqROmKp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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -534,15 +618,15 @@ " \n", " \n", " 0\n", - " 0.101647\n", + " 0.022823\n", " \n", " \n", " 1\n", - " 0.548901\n", + " 0.010507\n", " \n", " \n", " 2\n", - " 4.262977\n", + " 7.827988\n", " \n", " \n", "\n", @@ -551,9 +635,9 @@ "text/plain": [ " time\n", "idle_state \n", - "0 0.101647\n", - "1 0.548901\n", - "2 4.262977" + "0 0.022823\n", + "1 0.010507\n", + "2 7.827988" ] }, "execution_count": 14, @@ -562,7 +646,7 @@ } ], "source": [ - "# Idle state residency for the big cluster\n", + "# Idle state residency for CPUs in the big cluster\n", "trace.data_frame.cluster_idle_state_residency('big')" ] }, @@ -575,9 +659,9 @@ "outputs": [ { "data": { - "image/png": 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Mx7f774r5c/8E8OPAvdsZ458BvlFVbwbOAF5XVftV1TOTBHg/cBFw9zaO45M8ceS4zwDe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\npQ6G3u+g8Zgv6spcUVfmiro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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -585,6 +669,7 @@ } ], "source": [ + "# Actual time spent in each idle state for CPUs in the big and LITTLE clusters\n", "ia.plotClusterIdleStateResidency(['big', 'LITTLE'])" ] }, @@ -597,9 +682,9 @@ "outputs": [ { "data": { - "image/png": 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+zLKo2u+6O4nLzgEAAGB0im9gle3bt08dAQ6Yfsyy0JdZBvoxdBTfwCqz2WzqCHDA9GOW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\ncJJ3JTmmtXZ10hXoSb5uumS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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -607,6 +692,7 @@ } ], "source": [ + "# Percentage of time spent in each idle state for CPUs in the big and LITTLE clusters\n", "ia.plotClusterIdleStateResidency(['big', 'LITTLE'], pct=True)" ] } diff --git a/ipynb/examples/trace_analysis/TraceAnalysis_TasksLatencies.ipynb b/ipynb/examples/trace_analysis/TraceAnalysis_TasksLatencies.ipynb index 784ce93a..51e92a90 100644 --- a/ipynb/examples/trace_analysis/TraceAnalysis_TasksLatencies.ipynb +++ b/ipynb/examples/trace_analysis/TraceAnalysis_TasksLatencies.ipynb @@ -4,30 +4,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Trace Analysis Examples\n", - "
\n", - "Tasks Latencies\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import Required Modules" + "# Trace Analysis Examples\n", + "\n", + "## Tasks Latencies\n", + "\n", + "This notebook shows the features provided for task latency profiling. It will be necessary to collect the following events:\n", + " \n", + "Details on idle states profiling ar given in **Latency DataFrames and Latency Plots ** below." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true, + "collapsed": false, "run_control": { "marked": false } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:58:10,343 INFO : root : Using LISA logging configuration:\n", + "2016-12-12 12:58:10,344 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], "source": [ "import logging\n", "from conf import LisaLogging\n", @@ -60,7 +64,6 @@ "\n", "# Support for trace analysis\n", "from trace import Trace\n", - "from trace_analysis import TraceAnalysis\n", "\n", "# Support for plotting\n", "import numpy\n", @@ -73,7 +76,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Target Configuration" + "## Target Configuration\n", + "The target configuration is used to describe and configure your test environment.\n", + "You can find more details in **examples/utils/testenv_example.ipynb**." ] }, { @@ -94,6 +99,7 @@ " \"platform\" : 'linux',\n", " \"board\" : 'juno',\n", " \"host\" : '192.168.0.1',\n", + " \"password\" : 'juno',\n", "\n", " # Folder where all the results will be collected\n", " \"results_dir\" : \"TraceAnalysis_TaskLatencies\",\n", @@ -134,35 +140,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "10:23:42 INFO : Target - Using base path: /home/derkling/Code/lisa\n", - "10:23:42 INFO : Target - Loading custom (inline) target configuration\n", - "10:23:42 INFO : Target - Devlib modules to load: ['bl', 'cpufreq']\n", - "10:23:42 INFO : Target - Connecting linux target:\n", - "10:23:42 INFO : Target - username : root\n", - "10:23:42 INFO : Target - host : 192.168.0.1\n", - "10:23:42 INFO : Target - password : \n", - "10:23:42 INFO : Target - Connection settings:\n", - "10:23:42 INFO : Target - {'username': 'root', 'host': '192.168.0.1', 'password': ''}\n", - "10:23:46 INFO : Target - Initializing target workdir:\n", - "10:23:46 INFO : Target - /root/devlib-target\n", - "10:23:50 INFO : Target - Topology:\n", - "10:23:50 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n", - "10:23:52 INFO : Platform - Loading default EM:\n", - "10:23:52 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/juno.json\n", - "10:23:53 INFO : FTrace - Enabled tracepoints:\n", - "10:23:53 INFO : FTrace - sched_switch\n", - "10:23:53 INFO : FTrace - sched_wakeup\n", - "10:23:53 INFO : FTrace - sched_load_avg_cpu\n", - "10:23:53 INFO : FTrace - sched_load_avg_task\n", - "10:23:53 WARNING : Target - Using configuration provided RTApp calibration\n", - "10:23:53 INFO : Target - Using RT-App calibration values:\n", - "10:23:53 INFO : Target - {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n", - "10:23:53 INFO : EnergyMeter - HWMON module not enabled\n", - "10:23:53 WARNING : EnergyMeter - Energy sampling disabled by configuration\n", - "10:23:53 INFO : TestEnv - Set results folder to:\n", - "10:23:53 INFO : TestEnv - /home/derkling/Code/lisa/results/TraceAnalysis_TaskLatencies\n", - "10:23:53 INFO : TestEnv - Experiment results available also in:\n", - "10:23:53 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n" + "2016-12-12 12:58:17,443 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-12 12:58:17,444 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-12 12:58:17,444 INFO : TestEnv : Devlib modules to load: ['bl', 'cpufreq']\n", + "2016-12-12 12:58:17,445 INFO : TestEnv : Connecting linux target:\n", + "2016-12-12 12:58:17,445 INFO : TestEnv : username : root\n", + "2016-12-12 12:58:17,446 INFO : TestEnv : host : 192.168.0.1\n", + "2016-12-12 12:58:17,446 INFO : TestEnv : password : juno\n", + "2016-12-12 12:58:17,447 INFO : TestEnv : Connection settings:\n", + "2016-12-12 12:58:17,447 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n", + "2016-12-12 12:58:24,242 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-12 12:58:24,243 INFO : TestEnv : /root/devlib-target\n", + "2016-12-12 12:58:40,880 INFO : TestEnv : Topology:\n", + "2016-12-12 12:58:40,881 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n", + "2016-12-12 12:58:42,134 INFO : TestEnv : Loading default EM:\n", + "2016-12-12 12:58:42,135 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/juno.json\n", + "2016-12-12 12:58:45,386 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-12 12:58:45,387 INFO : TestEnv : sched_switch\n", + "2016-12-12 12:58:45,387 INFO : TestEnv : sched_wakeup\n", + "2016-12-12 12:58:45,388 INFO : TestEnv : sched_load_avg_cpu\n", + "2016-12-12 12:58:45,389 INFO : TestEnv : sched_load_avg_task\n", + "2016-12-12 12:58:45,389 WARNING : TestEnv : Using configuration provided RTApp calibration\n", + "2016-12-12 12:58:45,390 INFO : TestEnv : Using RT-App calibration values:\n", + "2016-12-12 12:58:45,390 INFO : TestEnv : {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n", + "2016-12-12 12:58:45,391 INFO : EnergyMeter : HWMON module not enabled\n", + "2016-12-12 12:58:45,391 WARNING : EnergyMeter : Energy sampling disabled by configuration\n", + "2016-12-12 12:58:45,392 INFO : TestEnv : Set results folder to:\n", + "2016-12-12 12:58:45,392 INFO : TestEnv : /home/vagrant/lisa/results/TraceAnalysis_TaskLatencies\n", + "2016-12-12 12:58:45,393 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-12 12:58:45,393 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" ] } ], @@ -176,7 +182,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Workload Execution" + "## Workload Configuration and Execution\n", + "\n", + "Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**." ] }, { @@ -228,42 +236,42 @@ "name": "stderr", "output_type": "stream", "text": [ - "10:23:53 INFO : WlGen - Setup new workload ramp\n", - "10:23:53 INFO : RTApp - Workload duration defined by longest task\n", - "10:23:53 INFO : RTApp - Default policy: SCHED_OTHER\n", - "10:23:53 INFO : RTApp - ------------------------\n", - "10:23:53 INFO : RTApp - task [ramp], sched: using default policy\n", - "10:23:53 INFO : RTApp - | calibration CPU: 1\n", - "10:23:53 INFO : RTApp - | loops count: 1\n", - "10:23:53 INFO : RTApp - + phase_000001: duration 0.500000 [s] (5 loops)\n", - "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 60 %\n", - "10:23:53 INFO : RTApp - | run_time 60000 [us], sleep_time 40000 [us]\n", - "10:23:53 INFO : RTApp - + phase_000002: duration 0.500000 [s] (5 loops)\n", - "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 55 %\n", - "10:23:53 INFO : RTApp - | run_time 55000 [us], sleep_time 45000 [us]\n", - "10:23:53 INFO : RTApp - + phase_000003: duration 0.500000 [s] (5 loops)\n", - "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 50 %\n", - "10:23:53 INFO : RTApp - | run_time 50000 [us], sleep_time 50000 [us]\n", - "10:23:53 INFO : RTApp - + phase_000004: duration 0.500000 [s] (5 loops)\n", - "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n", - "10:23:53 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n", - "10:23:53 INFO : RTApp - + phase_000005: duration 0.500000 [s] (5 loops)\n", - "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 40 %\n", - "10:23:53 INFO : RTApp - | run_time 40000 [us], sleep_time 60000 [us]\n", - "10:23:53 INFO : RTApp - + phase_000006: duration 0.500000 [s] (5 loops)\n", - "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 35 %\n", - "10:23:53 INFO : RTApp - | run_time 35000 [us], sleep_time 65000 [us]\n", - "10:23:53 INFO : RTApp - + phase_000007: duration 0.500000 [s] (5 loops)\n", - "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 30 %\n", - "10:23:53 INFO : RTApp - | run_time 30000 [us], sleep_time 70000 [us]\n", - "10:23:53 INFO : RTApp - + phase_000008: duration 0.500000 [s] (5 loops)\n", - "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n", - "10:23:53 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n", - "10:23:53 INFO : RTApp - + phase_000009: duration 0.500000 [s] (5 loops)\n", - "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n", - "10:23:53 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n", - "10:23:57 INFO : WlGen - Workload execution START:\n", - "10:23:57 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n" + "2016-12-12 12:58:52,280 INFO : Workload : Setup new workload ramp\n", + "2016-12-12 12:58:52,281 INFO : Workload : Workload duration defined by longest task\n", + "2016-12-12 12:58:52,282 INFO : Workload : Default policy: SCHED_OTHER\n", + "2016-12-12 12:58:52,282 INFO : Workload : ------------------------\n", + "2016-12-12 12:58:52,283 INFO : Workload : task [ramp], sched: using default policy\n", + "2016-12-12 12:58:52,283 INFO : Workload : | calibration CPU: 1\n", + "2016-12-12 12:58:52,284 INFO : Workload : | loops count: 1\n", + "2016-12-12 12:58:52,284 INFO : Workload : + phase_000001: duration 0.500000 [s] (5 loops)\n", + "2016-12-12 12:58:52,285 INFO : Workload : | period 100000 [us], duty_cycle 60 %\n", + "2016-12-12 12:58:52,285 INFO : Workload : | run_time 60000 [us], sleep_time 40000 [us]\n", + "2016-12-12 12:58:52,286 INFO : Workload : + phase_000002: duration 0.500000 [s] (5 loops)\n", + "2016-12-12 12:58:52,286 INFO : Workload : | period 100000 [us], duty_cycle 55 %\n", + "2016-12-12 12:58:52,287 INFO : Workload : | run_time 55000 [us], sleep_time 45000 [us]\n", + "2016-12-12 12:58:52,288 INFO : Workload : + phase_000003: duration 0.500000 [s] (5 loops)\n", + "2016-12-12 12:58:52,288 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n", + "2016-12-12 12:58:52,289 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n", + "2016-12-12 12:58:52,290 INFO : Workload : + phase_000004: duration 0.500000 [s] (5 loops)\n", + "2016-12-12 12:58:52,291 INFO : Workload : | period 100000 [us], duty_cycle 45 %\n", + "2016-12-12 12:58:52,291 INFO : Workload : | run_time 45000 [us], sleep_time 55000 [us]\n", + "2016-12-12 12:58:52,292 INFO : Workload : + phase_000005: duration 0.500000 [s] (5 loops)\n", + "2016-12-12 12:58:52,293 INFO : Workload : | period 100000 [us], duty_cycle 40 %\n", + "2016-12-12 12:58:52,293 INFO : Workload : | run_time 40000 [us], sleep_time 60000 [us]\n", + "2016-12-12 12:58:52,294 INFO : Workload : + phase_000006: duration 0.500000 [s] (5 loops)\n", + "2016-12-12 12:58:52,294 INFO : Workload : | period 100000 [us], duty_cycle 35 %\n", + "2016-12-12 12:58:52,295 INFO : Workload : | run_time 35000 [us], sleep_time 65000 [us]\n", + "2016-12-12 12:58:52,295 INFO : Workload : + phase_000007: duration 0.500000 [s] (5 loops)\n", + "2016-12-12 12:58:52,296 INFO : Workload : | period 100000 [us], duty_cycle 30 %\n", + "2016-12-12 12:58:52,296 INFO : Workload : | run_time 30000 [us], sleep_time 70000 [us]\n", + "2016-12-12 12:58:52,296 INFO : Workload : + phase_000008: duration 0.500000 [s] (5 loops)\n", + "2016-12-12 12:58:52,297 INFO : Workload : | period 100000 [us], duty_cycle 25 %\n", + "2016-12-12 12:58:52,297 INFO : Workload : | run_time 25000 [us], sleep_time 75000 [us]\n", + "2016-12-12 12:58:52,298 INFO : Workload : + phase_000009: duration 0.500000 [s] (5 loops)\n", + "2016-12-12 12:58:52,298 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n", + "2016-12-12 12:58:52,298 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n", + "2016-12-12 12:58:58,853 INFO : Workload : Workload execution START:\n", + "2016-12-12 12:58:58,853 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n" ] } ], @@ -275,7 +283,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Parse Trace and Profiling Data" + "## Parse Trace and Profiling Data" ] }, { @@ -292,26 +300,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "10:24:05 INFO : Content of the output folder /home/derkling/Code/lisa/results/TraceAnalysis_TaskLatencies\n" + "2016-12-12 12:59:36,883 INFO : root : Content of the output folder /home/vagrant/lisa/results/TraceAnalysis_TaskLatencies\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[01;34m/home/derkling/Code/lisa/results/TraceAnalysis_TaskLatencies\u001b[00m\r\n", + "/home/vagrant/lisa/results/TraceAnalysis_TaskLatencies\r\n", "├── output.log\r\n", "├── platform.json\r\n", "├── ramp_00.json\r\n", "├── rt-app-ramp-0.log\r\n", - "├── \u001b[01;35mtask_latencies_1236_1236__ramp,_rt-app.png\u001b[00m\r\n", - "├── \u001b[01;35mtask_latencies_1295_1295__ramp,_rt-app.png\u001b[00m\r\n", - "├── \u001b[01;35mtask_latencies_1644_1644__ramp,_rt-app.png\u001b[00m\r\n", - "├── trace.dat\r\n", - "├── trace.raw.txt\r\n", - "└── trace.txt\r\n", + "└── trace.dat\r\n", "\r\n", - "0 directories, 10 files\r\n" + "0 directories, 5 files\r\n" ] } ], @@ -336,14 +339,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "10:24:05 INFO : LITTLE cluster max capacity: 447\n" + "2016-12-12 12:59:38,100 INFO : root : LITTLE cluster max capacity: 447\n" ] } ], "source": [ "with open(os.path.join(res_dir, 'platform.json'), 'r') as fh:\n", " platform = json.load(fh)\n", - "#print json.dumps(platform, indent=4)\n", "logging.info('LITTLE cluster max capacity: %d',\n", " platform['nrg_model']['little']['cpu']['cap_max'])" ] @@ -362,20 +364,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "10:24:05 INFO : Parsing FTrace format...\n", - "10:24:06 INFO : Collected events spans a 6.641 [s] time interval\n", - "10:24:06 INFO : Set plots time range to (0.000000, 6.641493)[s]\n", - "10:24:06 INFO : Registering trace analysis modules:\n", - "10:24:06 WARNING : No performance data found in:\n", - "10:24:06 WARNING : /home/derkling/Code/lisa/results/TraceAnalysis_TaskLatencies\n", - "10:24:06 INFO : perf\n", - "10:24:06 INFO : latency\n", - "10:24:06 INFO : eas\n", - "10:24:06 INFO : tasks\n", - "10:24:06 INFO : cpus\n", - "10:24:06 INFO : functions\n", - "10:24:06 INFO : status\n", - "10:24:06 INFO : frequency\n" + "2016-12-12 12:59:39,175 INFO : Trace : Parsing FTrace format...\n", + "2016-12-12 12:59:39,536 INFO : Trace : Collected events spans a 13.065 [s] time interval\n", + "2016-12-12 12:59:39,536 INFO : Trace : Set plots time range to (0.000000, 13.064847)[s]\n", + "2016-12-12 12:59:39,537 INFO : Analysis : Registering trace analysis modules:\n", + "2016-12-12 12:59:39,538 INFO : Analysis : tasks\n", + "2016-12-12 12:59:39,539 INFO : Analysis : status\n", + "2016-12-12 12:59:39,539 INFO : Analysis : frequency\n", + "2016-12-12 12:59:39,540 INFO : Analysis : cpus\n", + "2016-12-12 12:59:39,541 INFO : Analysis : latency\n", + "2016-12-12 12:59:39,541 INFO : Analysis : idle\n", + "2016-12-12 12:59:39,542 INFO : Analysis : functions\n", + "2016-12-12 12:59:39,542 INFO : Analysis : eas\n" ] } ], @@ -390,7 +390,7 @@ "collapsed": true }, "source": [ - "# Trace visualization" + "## Trace visualization" ] }, { @@ -490,7 +490,7 @@ " shape-rendering: crispEdges;\n", "}\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n", @@ -521,7 +521,7 @@ " /* TRAPPY_PUBLISH_REMOVE_STOP */\n", " \n", " req([\"require\", \"EventPlot\"], function() { /* TRAPPY_PUBLISH_REMOVE_LINE */\n", - " EventPlot.generate('fig_be1eab72adb54d86845c6c51b363175a', '/nbextensions/', {\"lanes\": [{\"id\": 0, \"label\": \"CPU :0\"}, {\"id\": 1, \"label\": \"CPU :1\"}, {\"id\": 2, \"label\": \"CPU :2\"}, {\"id\": 3, \"label\": \"CPU :3\"}, {\"id\": 4, \"label\": \"CPU :4\"}, {\"id\": 5, \"label\": \"CPU :5\"}], \"keys\": [\"ramp-1423\", \"sudo-1420\", \"sudo-1427\", \"sh-1417\", \"sh-1429\", \"sh-1419\", \"sh-1426\", \"sh-1431\", \"sshd-1424\", \"sshd-1425\", \"sudo-1418\", \"sudo-1430\", \"shutils-1421\", \"shutils-1428\", \"sudo-1432\", \"sh-1422\", \"sh-1379\", \"sudo-1415\", \"sudo-1426\", \"sudo-1419\", \"sudo-1429\", \"sudo-1417\", \"sshd-1132\", \"rt-app-1422\", \"shutils-1420\", \"shutils-1427\", \"jbd2/sda2-8-875\", \"scp-1424\", \"scp-1425\", \"ksoftirqd/0-3\", \"syslogd-1104\", \"khugepaged-337\", \"usb-storage-853\", \"kworker/u12:2-1323\", \"kworker/u12:0-1320\", \"kworker/1:1-461\", \"init-1\", \"ksoftirqd/2-23\", \"ksoftirqd/1-17\", \"rcu_preempt-7\", \"rt-app-1423\", \"kworker/2:0-24\", \"rcu_sched-8\", \"kworker/1:1H-874\", \"kworker/2:1H-873\", \"sudo-1431\", \"watchdog/3-27\", \"watchdog/4-33\", \"watchdog/5-39\", \"watchdog/0-12\", \"watchdog/2-21\", \"watchdog/1-15\"], \"stride\": false, \"showSummary\": true, \"xDomain\": [3.0000010156072676e-06, 6.6414930000028107], \"data\": {\"shutils-1420\": [[0.63584999999875436, 0.63587199999892619, 2], [0.63592800000333227, 0.6359359999987646, 2], [0.63599700000486337, 0.63600600000063423, 2], [0.63606300000537885, 0.63607100000081118, 2], [0.63612999999895692, 0.6361380000016652, 2], [0.63619499999913387, 0.63620200000150362, 2], [0.63640500000474276, 0.6374950000026729, 2]], \"shutils-1421\": [[0.63326600000436883, 0.63448000000062166, 2], [0.63471800000115763, 0.63642600000457605, 1]], \"watchdog/4-33\": [[2.841762000003655, 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}); /* TRAPPY_PUBLISH_REMOVE_LINE */\n", " \n", "
" @@ -542,7 +542,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Latency DataFrames" + "## Latency DataFrames" ] }, { @@ -552,19 +552,6 @@ "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "10:24:06 WARNING : The [sched_switch] events contain 'prev_state' value [4096.0]\n", - "10:24:06 WARNING : The [sched_switch] events contain 'prev_state' value [1.0]\n", - "10:24:06 WARNING : The [sched_switch] events contain 'prev_state' value [0.0]\n", - "10:24:06 WARNING : The [sched_switch] events contain 'prev_state' value [64.0]\n", - "10:24:06 WARNING : which are not currently mapped into a task state.\n", - "10:24:06 WARNING : Check mappings in:\n", - "10:24:06 WARNING : /home/derkling/Code/lisa/libs/utils/analysis/latency_analysis.py::LatencyAnalysis _taskState()\n" - ] - }, { "data": { "text/html": [ @@ -592,49 +579,49 @@ " \n", " \n", " \n", - " 1.046629\n", + " 1.773515\n", " NaN\n", - " 2\n", + " 1.0\n", " A\n", - " 4096\n", - " 1.046629\n", - " 0.000035\n", + " S\n", + " 1.773515\n", + " 0.086343\n", " \n", " \n", - " 1.046664\n", + " 1.859858\n", " NaN\n", - " 2\n", - " 4096\n", - " A\n", - " 1.046664\n", - " 0.000020\n", + " 1.0\n", + " S\n", + " W\n", + " 1.859858\n", + " 0.013899\n", " \n", " \n", - " 1.046684\n", + " 1.873757\n", + " 1.0\n", " NaN\n", - " 2\n", + " W\n", " A\n", - " 4096\n", - " 1.046684\n", - " 0.000039\n", + " 1.873757\n", + " 0.000010\n", " \n", " \n", - " 1.046723\n", + " 1.873767\n", " NaN\n", - " 2\n", - " 4096\n", + " 1.0\n", " A\n", - " 1.046723\n", - " 0.000033\n", + " S\n", + " 1.873767\n", + " 0.059513\n", " \n", " \n", - " 1.046756\n", + " 1.933280\n", " NaN\n", - " 2\n", - " A\n", - " 1\n", - " 1.046756\n", - " 0.059737\n", + " 1.0\n", + " S\n", + " W\n", + " 1.933280\n", + " 0.040525\n", " \n", " \n", "\n", @@ -643,11 +630,11 @@ "text/plain": [ " target_cpu __cpu curr_state next_state t_start t_delta\n", "Time \n", - "1.046629 NaN 2 A 4096 1.046629 0.000035\n", - "1.046664 NaN 2 4096 A 1.046664 0.000020\n", - "1.046684 NaN 2 A 4096 1.046684 0.000039\n", - "1.046723 NaN 2 4096 A 1.046723 0.000033\n", - "1.046756 NaN 2 A 1 1.046756 0.059737" + "1.773515 NaN 1.0 A S 1.773515 0.086343\n", + "1.859858 NaN 1.0 S W 1.859858 0.013899\n", + "1.873757 1.0 NaN W A 1.873757 0.000010\n", + "1.873767 NaN 1.0 A S 1.873767 0.059513\n", + "1.933280 NaN 1.0 S W 1.933280 0.040525" ] }, "execution_count": 11, @@ -702,38 +689,25 @@ " \n", " \n", " \n", - " 0.000003\n", - " <idle>\n", + " 0.000007\n", + " trace-cmd\n", " 2\n", - " 0\n", - " sudo\n", - " 1415\n", - " 120\n", + " 935\n", " swapper/2\n", " 0\n", " 120\n", - " 0\n", - " \n", - " \n", - " 0.000004\n", - " trace-cmd\n", - " 1\n", - " 1416\n", - " swapper/1\n", - " 0\n", - " 120\n", " trace-cmd\n", - " 1416\n", + " 935\n", " 120\n", " 64\n", " \n", " \n", - " 0.000875\n", + " 0.000108\n", " <idle>\n", " 1\n", " 0\n", " sh\n", - " 1379\n", + " 934\n", " 120\n", " swapper/1\n", " 0\n", @@ -741,27 +715,40 @@ " 0\n", " \n", " \n", - " 0.000877\n", - " sudo\n", + " 0.000501\n", + " <idle>\n", " 2\n", - " 1415\n", + " 0\n", + " sudo\n", + " 933\n", + " 120\n", " swapper/2\n", " 0\n", " 120\n", - " sudo\n", - " 1415\n", + " 0\n", + " \n", + " \n", + " 0.000507\n", + " sh\n", + " 1\n", + " 934\n", + " swapper/1\n", + " 0\n", + " 120\n", + " sh\n", + " 934\n", " 120\n", " 64\n", " \n", " \n", - " 0.001094\n", + " 0.000691\n", " <idle>\n", - " 2\n", + " 1\n", " 0\n", - " kworker/u12:0\n", - " 1320\n", + " rcu_preempt\n", + " 7\n", " 120\n", - " swapper/2\n", + " swapper/1\n", " 0\n", " 120\n", " 0\n", @@ -771,21 +758,21 @@ "
" ], "text/plain": [ - " __comm __cpu __pid next_comm next_pid next_prio \\\n", - "Time \n", - "0.000003 2 0 sudo 1415 120 \n", - "0.000004 trace-cmd 1 1416 swapper/1 0 120 \n", - "0.000875 1 0 sh 1379 120 \n", - "0.000877 sudo 2 1415 swapper/2 0 120 \n", - "0.001094 2 0 kworker/u12:0 1320 120 \n", + " __comm __cpu __pid next_comm next_pid next_prio \\\n", + "Time \n", + "0.000007 trace-cmd 2 935 swapper/2 0 120 \n", + "0.000108 1 0 sh 934 120 \n", + "0.000501 2 0 sudo 933 120 \n", + "0.000507 sh 1 934 swapper/1 0 120 \n", + "0.000691 1 0 rcu_preempt 7 120 \n", "\n", " prev_comm prev_pid prev_prio prev_state \n", "Time \n", - "0.000003 swapper/2 0 120 0 \n", - "0.000004 trace-cmd 1416 120 64 \n", - "0.000875 swapper/1 0 120 0 \n", - "0.000877 sudo 1415 120 64 \n", - "0.001094 swapper/2 0 120 0 " + "0.000007 trace-cmd 935 120 64 \n", + "0.000108 swapper/1 0 120 0 \n", + "0.000501 swapper/2 0 120 0 \n", + "0.000507 sh 934 120 64 \n", + "0.000691 swapper/1 0 120 0 " ] }, "execution_count": 12, @@ -794,6 +781,7 @@ } ], "source": [ + "# Report information on sched_switch events\n", "df = trace.data_frame.trace_event('sched_switch')\n", "df.head()" ] @@ -822,24 +810,24 @@ " \n", " \n", " \n", - " 1.147042\n", - " 0.000251\n", + " 1.873757\n", + " 0.000010\n", " \n", " \n", - " 1.246761\n", - " 0.000007\n", + " 1.973805\n", + " 0.000020\n", " \n", " \n", - " 1.346992\n", - " 0.000013\n", + " 2.073804\n", + " 0.000023\n", " \n", " \n", - " 1.447048\n", - " 0.000016\n", + " 2.173801\n", + " 0.000020\n", " \n", " \n", - " 1.547064\n", - " 0.000022\n", + " 2.273804\n", + " 0.000020\n", " \n", " \n", "\n", @@ -848,11 +836,11 @@ "text/plain": [ " wakeup_latency\n", "Time \n", - "1.147042 0.000251\n", - "1.246761 0.000007\n", - "1.346992 0.000013\n", - "1.447048 0.000016\n", - "1.547064 0.000022" + "1.873757 0.000010\n", + "1.973805 0.000020\n", + "2.073804 0.000023\n", + "2.173801 0.000020\n", + "2.273804 0.000020" ] }, "execution_count": 13, @@ -889,24 +877,24 @@ " \n", " \n", " \n", - " 1.349451\n", - " 0.000167\n", + " 2.512645\n", + " 0.000019\n", " \n", " \n", - " 1.449459\n", - " 0.000027\n", + " 2.516644\n", + " 0.000052\n", " \n", " \n", - " 3.449462\n", - " 0.000023\n", + " 3.512646\n", + " 0.000019\n", " \n", " \n", - " 4.449462\n", - " 0.000033\n", + " 4.512646\n", + " 0.000017\n", " \n", " \n", - " 5.449460\n", - " 0.000028\n", + " 4.516644\n", + " 0.000088\n", " \n", " \n", "\n", @@ -915,11 +903,11 @@ "text/plain": [ " preempt_latency\n", "Time \n", - "1.349451 0.000167\n", - "1.449459 0.000027\n", - "3.449462 0.000023\n", - "4.449462 0.000033\n", - "5.449460 0.000028" + "2.512645 0.000019\n", + "2.516644 0.000052\n", + "3.512646 0.000019\n", + "4.512646 0.000017\n", + "4.516644 0.000088" ] }, "execution_count": 14, @@ -950,24 +938,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "10:24:07 WARNING : The [sched_switch] events contain 'prev_state' value [4096.0]\n", - "10:24:07 WARNING : The [sched_switch] events contain 'prev_state' value [1.0]\n", - "10:24:07 WARNING : The [sched_switch] events contain 'prev_state' value [0.0]\n", - "10:24:07 WARNING : The [sched_switch] events contain 'prev_state' value [64.0]\n", - "10:24:07 WARNING : which are not currently mapped into a task state.\n", - "10:24:07 WARNING : Check mappings in:\n", - "10:24:07 WARNING : /home/derkling/Code/lisa/libs/utils/analysis/latency_analysis.py::LatencyAnalysis _taskState()\n", - "10:24:07 INFO : Found: 45 WAKEUP latencies\n", - "10:24:07 INFO : Found: 5 PREEMPT latencies\n", - "10:24:07 INFO : Total: 50 latency events\n", - "10:24:07 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n" + "2016-12-12 12:59:56,078 INFO : Analysis : Found: 46 WAKEUP latencies\n", + "2016-12-12 12:59:56,104 INFO : Analysis : Found: 5 PREEMPT latencies\n", + "2016-12-12 12:59:56,105 INFO : Analysis : Total: 51 latency events\n", + "2016-12-12 12:59:56,180 WARNING : Analysis : Event [sched_overutilized] not found, plot DISABLED!\n" ] }, { "data": { - "image/png": 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33FGHHXaY5s+fry222EIzZ85cIq4LL7xQf/nLX/TXv/5VP/vZz3T66adriy22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\nozsb63JMMvN35eo0ij+Sv8K6HJOImENxA/oSMDjiqXWpkTR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Dg4M577zz8sQnPjE33HBDLrzwwtx+++255JJLkiRr1qzJiSeemK99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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1007,25 +988,25 @@ " \n", " \n", " latency\n", - " 50\n", - " 0.000029\n", - " 0.000038\n", - " 0.000007\n", - " 0.000022\n", - " 0.000031\n", - " 0.00021\n", - " 0.000251\n", + " 51.0\n", + " 0.00002\n", + " 0.000012\n", + " 0.000006\n", + " 0.00002\n", + " 0.000024\n", + " 0.00007\n", + " 0.000088\n", " \n", " \n", "\n", "" ], "text/plain": [ - " count mean std min 50% 95% 99% \\\n", - "latency 50 0.000029 0.000038 0.000007 0.000022 0.000031 0.00021 \n", + " count mean std min 50% 95% 99% \\\n", + "latency 51.0 0.00002 0.000012 0.000006 0.00002 0.000024 0.00007 \n", "\n", " max \n", - "latency 0.000251 " + "latency 0.000088 " ] }, "execution_count": 16, @@ -1047,9 +1028,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1063,7 +1044,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -1072,14 +1053,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "10:25:42 INFO : Set plots time range to (3.445000, 3.450000)[s]\n" + "2016-12-12 13:00:07,509 INFO : Trace : Set plots time range to (3.445000, 3.450000)[s]\n" ] }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1109,7 +1090,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.9" + "version": "2.7.6" }, "toc": { "toc_cell": false, diff --git a/ipynb/examples/trappy/custom_events_example.ipynb b/ipynb/examples/trappy/custom_events_example.ipynb new file mode 100644 index 00000000..d6b4ef24 --- /dev/null +++ b/ipynb/examples/trappy/custom_events_example.ipynb @@ -0,0 +1,687 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TRAPpy custom events\n", + "\n", + "Detailed information on Trappy can be found at **examples/trappy/trappy_example.ipynb**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:37:47,136 INFO : root : Using LISA logging configuration:\n", + "2016-12-12 12:37:47,136 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], + "source": [ + "import logging\n", + "from conf import LisaLogging\n", + "LisaLogging.setup()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Generate plots inline\n", + "%matplotlib inline\n", + "\n", + "import copy\n", + "import json\n", + "import os\n", + "import time\n", + "import math\n", + "import logging\n", + "\n", + "# Support to access the remote target\n", + "import devlib\n", + "from env import TestEnv\n", + "\n", + "# Support to configure and run RTApp based workloads\n", + "from wlgen import RTA\n", + "\n", + "# Support for performance analysis of RTApp workloads\n", + "from perf_analysis import PerfAnalysis\n", + "\n", + "# Support for trace events analysis\n", + "from trace import Trace\n", + "\n", + "# Suport for FTrace events parsing and visualization\n", + "import trappy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test environment setup\n", + "\n", + "For more details on this please check out **examples/utils/testenv_example.ipynb**." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a target configuration\n", + "my_target_conf = {\n", + " \n", + " # Define the kind of target platform to use for the experiments\n", + " \"platform\" : 'linux', # Linux system, valid other options are:\n", + " # android - access via ADB\n", + " # linux - access via SSH\n", + " # host - direct access\n", + " \n", + " # Preload settings for a specific target\n", + " \"board\" : 'juno', # juno - JUNO board with mainline hwmon\n", + " \n", + " # Define devlib module to load\n", + " \"modules\" : [\n", + " 'bl', # enable big.LITTLE support\n", + " 'cpufreq' # enable CPUFreq support\n", + " ],\n", + "\n", + " # Account to access the remote target\n", + " \"host\" : '192.168.0.1',\n", + " \"username\" : 'root',\n", + " \"password\" : 'juno',\n", + "\n", + " # Comment the following line to force rt-app calibration on your target\n", + " \"rtapp-calib\" : {\n", + " '0': 361, '1': 138, '2': 138, '3': 352, '4': 360, '5': 353\n", + " }\n", + "\n", + "}\n", + "\n", + "# Setup the required Test Environment supports\n", + "my_tests_conf = {\n", + " \n", + " # Binary tools required to run this experiment\n", + " # These tools must be present in the tools/ folder for the architecture\n", + " \"tools\" : ['trace-cmd'],\n", + " \n", + " # FTrace events buffer configuration\n", + " # events listed here MUST be \n", + " \"ftrace\" : {\n", + " \n", + " \n", + "##############################################################################\n", + "# EVENTS SPECIFICATIPON\n", + "##############################################################################\n", + "# Here is where we specify the list of events we are interested into:\n", + "# Events are of two types:\n", + "# 1. FTrace tracepoints that _must_ be supported by the target's kernel in use.\n", + "# These events will be enabled at ftrace start time, thus if the kernel does\n", + "# not support one of them, ftrace starting will fails.\n", + "\n", + " \"events\" : [\n", + " \"sched_switch\",\n", + " \"cpu_frequency\",\n", + " ],\n", + "\n", + "# 2. FTrace events generated via trace_printk, from either kernel or user\n", + "# space. These events are different from the previous because they do not\n", + "# need to be explicitely enabled at ftrace start time.\n", + "# It's up to the user to ensure that the generated events satisfies these\n", + "# formatting requirements:\n", + "# a) the name must be a unique word into the trace\n", + "# b) values must be reported as a sequence of key=value paires\n", + "# For example, a valid custom event string is:\n", + "# my_math_event: kay1=val1 key2=val2 key3=val3\n", + "\n", + " \"custom\" : [\n", + " \"my_math_event\",\n", + " ],\n", + " \n", + "# For each of these events, TRAPpy will generate a Pandas dataframe accessible\n", + "# via a TRAPpy::FTrace object, whith the same name of the event.\n", + "# Thus for example, ftrace.my_math_event will be the object exposing the\n", + "# dataframe with all the event matching the \"my_math_event\" unique word.\n", + " \n", + "##############################################################################\n", + " \n", + " \"buffsize\" : 10240,\n", + " },\n", + "\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:37:50,978 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-12 12:37:50,980 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-12 12:37:50,981 INFO : TestEnv : Loading custom (inline) test configuration\n", + "2016-12-12 12:37:50,982 INFO : TestEnv : Devlib modules to load: ['bl', 'cpufreq', 'hwmon']\n", + "2016-12-12 12:37:50,983 INFO : TestEnv : Connecting linux target:\n", + "2016-12-12 12:37:50,983 INFO : TestEnv : username : root\n", + "2016-12-12 12:37:50,984 INFO : TestEnv : host : 192.168.0.1\n", + "2016-12-12 12:37:50,984 INFO : TestEnv : password : juno\n", + "2016-12-12 12:37:50,985 INFO : TestEnv : Connection settings:\n", + "2016-12-12 12:37:50,985 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n", + "2016-12-12 12:38:07,171 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-12 12:38:07,171 INFO : TestEnv : /root/devlib-target\n", + "2016-12-12 12:38:16,815 INFO : TestEnv : Topology:\n", + "2016-12-12 12:38:16,816 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n", + "2016-12-12 12:38:18,066 INFO : TestEnv : Loading default EM:\n", + "2016-12-12 12:38:18,068 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/juno.json\n", + "2016-12-12 12:38:21,639 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-12 12:38:21,640 INFO : TestEnv : sched_switch\n", + "2016-12-12 12:38:21,641 INFO : TestEnv : cpu_frequency\n", + "2016-12-12 12:38:21,642 INFO : EnergyMeter : Scanning for HWMON channels, may take some time...\n", + "2016-12-12 12:38:21,644 INFO : EnergyMeter : Channels selected for energy sampling:\n", + "2016-12-12 12:38:21,645 INFO : EnergyMeter : BOARDBIG_energy\n", + "2016-12-12 12:38:21,645 INFO : EnergyMeter : BOARDLITTLE_energy\n", + "2016-12-12 12:38:21,646 INFO : TestEnv : Set results folder to:\n", + "2016-12-12 12:38:21,647 INFO : TestEnv : /home/vagrant/lisa/results/20161212_123821\n", + "2016-12-12 12:38:21,647 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-12 12:38:21,648 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" + ] + } + ], + "source": [ + "# Initialize a test environment using:\n", + "# - the provided target configuration (my_target_conf)\n", + "# - the provided test configuration (my_test_conf)\n", + "te = TestEnv(target_conf=my_target_conf, test_conf=my_tests_conf)\n", + "target = te.target" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:38:25,875 INFO : root : Target ABI: arm64, CPus: ['A53', 'A57', 'A57', 'A53', 'A53', 'A53']\n" + ] + } + ], + "source": [ + "logging.info(\"Target ABI: %s, CPus: %s\",\n", + " target.abi,\n", + " target.cpuinfo.cpu_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example of custom event definition" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:38:33,871 INFO : root : Generating events from user-space (will take ~140[s])...\n" + ] + } + ], + "source": [ + "# Define the format string for the custom events we will inject from user-space\n", + "my_math_event_fmt = \"my_math_event: sin={} cos={}\"\n", + "\n", + "# Start FTrace\n", + "te.ftrace.start()\n", + "\n", + "# Let's generate some interesting \"custom\" events from userspace\n", + "logging.info('Generating events from user-space (will take ~140[s])...')\n", + "for angle in range(360):\n", + " v_sin = int(1e6 * math.sin(math.radians(angle)))\n", + " v_cos = int(1e6 * math.cos(math.radians(angle)))\n", + " my_math_event = my_math_event_fmt.format(v_sin, v_cos)\n", + " # custom events can be generated either from userspace, like in this\n", + " # example, or also from kernelspace (using a trace_printk call)\n", + " target.execute('echo {} > /sys/kernel/debug/tracing/trace_marker'\\\n", + " .format(my_math_event))\n", + "\n", + "# Stop FTrace\n", + "te.ftrace.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Collect the generate trace\n", + "trace_file = '/tmp/trace.dat'\n", + "te.ftrace.get_trace(trace_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:44:50,076 INFO : Trace : Parsing FTrace format...\n", + "2016-12-12 12:44:51,500 INFO : Trace : Platform clusters verified to be Frequency coherent\n", + "2016-12-12 12:44:51,682 INFO : Trace : Collected events spans a 113.228 [s] time interval\n", + "2016-12-12 12:44:51,682 INFO : Trace : Set plots time range to (0.000000, 113.227578)[s]\n", + "2016-12-12 12:44:51,683 INFO : Analysis : Registering trace analysis modules:\n", + "2016-12-12 12:44:51,684 INFO : Analysis : tasks\n", + "2016-12-12 12:44:51,684 INFO : Analysis : status\n", + "2016-12-12 12:44:51,687 INFO : Analysis : frequency\n", + "2016-12-12 12:44:51,688 INFO : Analysis : cpus\n", + "2016-12-12 12:44:51,690 INFO : Analysis : latency\n", + "2016-12-12 12:44:51,693 INFO : Analysis : idle\n", + "2016-12-12 12:44:51,694 INFO : Analysis : functions\n", + "2016-12-12 12:44:51,695 INFO : Analysis : eas\n" + ] + } + ], + "source": [ + "# Parse trace\n", + "events_to_parse = my_tests_conf['ftrace']['events'] + my_tests_conf['ftrace']['custom']\n", + "trace = Trace(te.platform, '/tmp', events_to_parse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspection of the generated TRAPpy FTrace object" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Get the TRAPpy FTrace object which has been generated from the trace parsing\n", + "ftrace = trace.ftrace" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:44:55,642 INFO : root : List of events identified in the trace:\n", + "['cpu_frequency', 'my_math_event', 'sched_switch', 'cpu_idle']\n" + ] + } + ], + "source": [ + "# The FTrace object allows to verify which (of the registered) events have been\n", + "# identified into the trace\n", + "logging.info(\"List of events identified in the trace:\\n%s\",\n", + " ftrace.class_definitions.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:44:57,476 INFO : root : First 10 events of our 'my_math_event' custom event:\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " __comm __cpu __pid cpu frequency\n", + "Time \n", + "111.728239 cfinteractive 2 44 1 625000\n", + "111.728245 cfinteractive 2 44 2 625000\n", + "111.868680 cfinteractive 2 44 1 450000\n", + "111.868690 cfinteractive 2 44 2 450000" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logging.info(\"First 10 events of our 'cpu_frequency' tracepoint:\")\n", + "ftrace.cpu_frequency.data_frame.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting tracepoint and/or custom events" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "code_folding": [], + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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XW7aanyzF6YSurq6uuBdR7Q4dOsT27duZNWsWY8eOjXs5GoDcgQCQjcMIozDa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\nmPNXwvmwWPQa4D+GygljrlQ2TDDfem1goIxYyGazrkuoChP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vzzz7suAYiMWu9Xpt8AO5Lca+xXro3MyeI3IdmhnJfkfgNsWm6vnVCm6E1IiT3KgG03uS4AAKpp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\nzDHH8Je//CWhf0eszz77jEmTJjFo0CDat29Pjx49+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# It is possible to mix in the same plot tracepoints and custom events\n", + "\n", + "# The LinePlot module requires to specify a list of signals to plot.\n", + "# Each signal is defined as:\n", + "# :\n", + "# where:\n", + "# is one of the events collected from the trace by the FTrace object\n", + "# is one of the column of the previously defined event\n", + "my_signals = [\n", + " 'cpu_frequency:frequency',\n", + " 'my_math_event:sin',\n", + " 'my_math_event:cos'\n", + "]\n", + "\n", + "# These two paramatere are passed to the LinePlot call as long with the\n", + "# TRAPpy FTrace object\n", + "trappy.LinePlot(\n", + " ftrace, # FTrace object\n", + " signals=my_signals, # Signals to be plotted\n", + " drawstyle='steps-post', # Plot style options\n", + " marker = '+'\n", + ").view()" + ] + } + ], + "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": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ipynb/examples/trappy/trappy_example.ipynb b/ipynb/examples/trappy/trappy_example.ipynb new file mode 100644 index 00000000..c1d9497c --- /dev/null +++ b/ipynb/examples/trappy/trappy_example.ipynb @@ -0,0 +1,537 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TRAPpy\n", + "\n", + "TRAPpy (Trace Analysis and Plotting in Python) is a visualization tool to help analyze data generated on a device. It parses ftrace-like logs and creates in-memory data structures to be used for plotting and data analysis.\n", + "More information can be found at https://github.com/ARM-software/trappy and https://pythonhosted.org/TRAPpy/.\n", + "\n", + "A big part of the notebook below is target and test environment confituration as well as workload configuration, detailed in **examples/utils/** and **examples/wlgen/**. For this reason those cells won't be documented in detail here in order to focus more on TRAPpy." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:31:07,128 INFO : root : Using LISA logging configuration:\n", + "2016-12-12 12:31:07,128 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], + "source": [ + "import logging\n", + "from conf import LisaLogging\n", + "LisaLogging.setup()" + ] + }, + { + "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", + "# Support to initialise and configure your test environment\n", + "import devlib\n", + "from env import TestEnv\n", + "\n", + "# Support to configure and run RTApp based workloads\n", + "from wlgen import RTA, Periodic, Ramp, Step, Pulse\n", + "\n", + "# Suport for FTrace events parsing and visualization\n", + "import trappy\n", + "from trappy.ftrace import FTrace\n", + "from trace import Trace" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test environment setup\n", + "\n", + "For more details on this please check out **examples/utils/testenv_example.ipynb**." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:31:10,081 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-12 12:31:10,082 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-12 12:31:10,083 INFO : TestEnv : Loading custom (inline) test configuration\n", + "2016-12-12 12:31:10,083 INFO : TestEnv : Devlib modules to load: ['bl', 'cpufreq', 'hwmon']\n", + "2016-12-12 12:31:10,084 INFO : TestEnv : Connecting linux target:\n", + "2016-12-12 12:31:10,084 INFO : TestEnv : username : root\n", + "2016-12-12 12:31:10,085 INFO : TestEnv : host : 192.168.0.1\n", + "2016-12-12 12:31:10,085 INFO : TestEnv : password : juno\n", + "2016-12-12 12:31:10,086 INFO : TestEnv : Connection settings:\n", + "2016-12-12 12:31:10,086 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n", + "2016-12-12 12:31:26,882 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-12 12:31:26,883 INFO : TestEnv : /root/devlib-target\n", + "2016-12-12 12:31:44,132 INFO : TestEnv : Topology:\n", + "2016-12-12 12:31:44,133 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n", + "2016-12-12 12:31:45,383 INFO : TestEnv : Loading default EM:\n", + "2016-12-12 12:31:45,384 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/juno.json\n", + "2016-12-12 12:31:49,435 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-12 12:31:49,436 INFO : TestEnv : sched_migrate_task\n", + "2016-12-12 12:31:49,437 INFO : TestEnv : sched_process_exec\n", + "2016-12-12 12:31:49,438 INFO : TestEnv : sched_process_fork\n", + "2016-12-12 12:31:49,439 INFO : TestEnv : sched_stat_iowait\n", + "2016-12-12 12:31:49,440 INFO : TestEnv : sched_switch\n", + "2016-12-12 12:31:49,440 INFO : TestEnv : sched_wakeup\n", + "2016-12-12 12:31:49,441 INFO : TestEnv : sched_wakeup_new\n", + "2016-12-12 12:31:49,442 INFO : TestEnv : sched_overutilized\n", + "2016-12-12 12:31:49,443 INFO : TestEnv : cpu_capacity\n", + "2016-12-12 12:31:49,443 INFO : TestEnv : sched_load_avg_cpu\n", + "2016-12-12 12:31:49,444 INFO : TestEnv : sched_load_avg_task\n", + "2016-12-12 12:31:49,445 INFO : TestEnv : sched_boost_cpu\n", + "2016-12-12 12:31:49,446 INFO : TestEnv : sched_boost_task\n", + "2016-12-12 12:31:49,446 INFO : TestEnv : sched_energy_diff\n", + "2016-12-12 12:31:49,447 INFO : TestEnv : cpu_frequency\n", + "2016-12-12 12:31:49,448 INFO : TestEnv : cpu_idle\n", + "2016-12-12 12:31:49,448 INFO : TestEnv : sched_tune_config\n", + "2016-12-12 12:31:49,448 WARNING : TestEnv : Using configuration provided RTApp calibration\n", + "2016-12-12 12:31:49,449 INFO : TestEnv : Using RT-App calibration values:\n", + "2016-12-12 12:31:49,449 INFO : TestEnv : {\"0\": 361, \"1\": 138, \"2\": 138, \"3\": 352, \"4\": 360, \"5\": 353}\n", + "2016-12-12 12:31:49,450 INFO : EnergyMeter : Scanning for HWMON channels, may take some time...\n", + "2016-12-12 12:31:49,452 INFO : EnergyMeter : Channels selected for energy sampling:\n", + "2016-12-12 12:31:49,452 INFO : EnergyMeter : BOARDBIG_energy\n", + "2016-12-12 12:31:49,452 INFO : EnergyMeter : BOARDLITTLE_energy\n", + "2016-12-12 12:31:49,453 INFO : TestEnv : Set results folder to:\n", + "2016-12-12 12:31:49,453 INFO : TestEnv : /home/vagrant/lisa/results/20161212_123149\n", + "2016-12-12 12:31:49,454 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-12 12:31:49,454 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" + ] + } + ], + "source": [ + "# Setup a target configuration\n", + "my_target_conf = {\n", + " \n", + " \"platform\" : 'linux',\n", + " \"board\" : 'juno',\n", + "\n", + " \"modules\" : [\n", + " 'bl',\n", + " 'cpufreq'\n", + " ],\n", + "\n", + " \"host\" : '192.168.0.1',\n", + " \"username\" : 'root',\n", + " \"password\" : 'juno',\n", + "## Workload execution\n", + " \"rtapp-calib\" : {\n", + " '0': 361, '1': 138, '2': 138, '3': 352, '4': 360, '5': 353\n", + " }\n", + "\n", + "}\n", + "\n", + "my_tests_conf = {\n", + "\n", + " \"tools\" : ['rt-app', 'taskset', 'trace-cmd'],\n", + "\n", + " \"ftrace\" : {\n", + " \"events\" : [\n", + " 'sched_migrate_task',\n", + " 'sched_process_exec',\n", + " 'sched_process_fork',\n", + " 'sched_stat_iowait',\n", + " 'sched_switch',\n", + " 'sched_wakeup',\n", + " 'sched_wakeup_new',\n", + " 'sched_overutilized',\n", + " 'cpu_capacity',\n", + " 'sched_load_avg_cpu',\n", + " 'sched_load_avg_task',\n", + " 'sched_boost_cpu',\n", + " 'sched_boost_task',\n", + " 'sched_energy_diff',\n", + " 'cpu_frequency',\n", + " 'cpu_idle',\n", + " 'sched_tune_config',\n", + " ],\n", + " \"buffsize\" : 10240\n", + " },\n", + "\n", + "}\n", + "\n", + "te = TestEnv(target_conf=my_target_conf, test_conf=my_tests_conf)\n", + "target = te.target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Workload configuration and execution\n", + "\n", + "For more details on this please check out **examples/wlgen/rtapp_example.ipynb**." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:32:22,824 INFO : Workload : Setup new workload trappy\n", + "2016-12-12 12:32:22,825 INFO : Workload : Workload duration defined by longest task\n", + "2016-12-12 12:32:22,826 INFO : Workload : Default policy: SCHED_OTHER\n", + "2016-12-12 12:32:22,826 INFO : Workload : ------------------------\n", + "2016-12-12 12:32:22,827 INFO : Workload : task [task_per20], sched: {'policy': 'FIFO'}\n", + "2016-12-12 12:32:22,827 INFO : Workload : | calibration CPU: 1\n", + "2016-12-12 12:32:22,828 INFO : Workload : | loops count: 1\n", + "2016-12-12 12:32:22,828 INFO : Workload : + phase_000001: duration 5.000000 [s] (50 loops)\n", + "2016-12-12 12:32:22,829 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n", + "2016-12-12 12:32:22,829 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n", + "2016-12-12 12:32:22,830 INFO : Workload : ------------------------\n", + "2016-12-12 12:32:22,830 INFO : Workload : task [task_pls5-80], sched: using default policy\n", + "2016-12-12 12:32:22,831 INFO : Workload : | start delay: 0.500000 [s]\n", + "2016-12-12 12:32:22,831 INFO : Workload : | calibration CPU: 1\n", + "2016-12-12 12:32:22,832 INFO : Workload : | loops count: 1\n", + "2016-12-12 12:32:22,832 INFO : Workload : + phase_000001: duration 1.000000 [s] (10 loops)\n", + "2016-12-12 12:32:22,833 INFO : Workload : | period 100000 [us], duty_cycle 65 %\n", + "2016-12-12 12:32:22,833 INFO : Workload : | run_time 65000 [us], sleep_time 35000 [us]\n", + "2016-12-12 12:32:22,834 INFO : Workload : + phase_000002: duration 1.000000 [s] (10 loops)\n", + "2016-12-12 12:32:22,834 INFO : Workload : | period 100000 [us], duty_cycle 5 %\n", + "2016-12-12 12:32:22,834 INFO : Workload : | run_time 5000 [us], sleep_time 95000 [us]\n", + "2016-12-12 12:32:22,835 INFO : Workload : ------------------------\n", + "2016-12-12 12:32:22,836 INFO : Workload : task [task_rmp20_5-60], sched: using default policy\n", + "2016-12-12 12:32:22,836 INFO : Workload : | calibration CPU: 1\n", + "2016-12-12 12:32:22,837 INFO : Workload : | loops count: 1\n", + "2016-12-12 12:32:22,837 INFO : Workload : | CPUs affinity: 0\n", + "2016-12-12 12:32:22,838 INFO : Workload : + phase_000001: duration 1.000000 [s] (10 loops)\n", + "2016-12-12 12:32:22,838 INFO : Workload : | period 100000 [us], duty_cycle 5 %\n", + "2016-12-12 12:32:22,839 INFO : Workload : | run_time 5000 [us], sleep_time 95000 [us]\n", + "2016-12-12 12:32:22,839 INFO : Workload : + phase_000002: duration 1.000000 [s] (10 loops)\n", + "2016-12-12 12:32:22,840 INFO : Workload : | period 100000 [us], duty_cycle 25 %\n", + "2016-12-12 12:32:22,840 INFO : Workload : | run_time 25000 [us], sleep_time 75000 [us]\n", + "2016-12-12 12:32:22,841 INFO : Workload : + phase_000003: duration 1.000000 [s] (10 loops)\n", + "2016-12-12 12:32:22,841 INFO : Workload : | period 100000 [us], duty_cycle 45 %\n", + "2016-12-12 12:32:22,841 INFO : Workload : | run_time 45000 [us], sleep_time 55000 [us]\n", + "2016-12-12 12:32:22,842 INFO : Workload : + phase_000004: duration 1.000000 [s] (10 loops)\n", + "2016-12-12 12:32:22,842 INFO : Workload : | period 100000 [us], duty_cycle 65 %\n", + "2016-12-12 12:32:22,843 INFO : Workload : | run_time 65000 [us], sleep_time 35000 [us]\n", + "2016-12-12 12:32:22,843 INFO : Workload : ------------------------\n", + "2016-12-12 12:32:22,844 INFO : Workload : task [task_stp10-50], sched: using default policy\n", + "2016-12-12 12:32:22,844 INFO : Workload : | start delay: 0.500000 [s]\n", + "2016-12-12 12:32:22,844 INFO : Workload : | calibration CPU: 1\n", + "2016-12-12 12:32:22,845 INFO : Workload : | loops count: 1\n", + "2016-12-12 12:32:22,845 INFO : Workload : + phase_000001: sleep 1.000000 [s]\n", + "2016-12-12 12:32:22,845 INFO : Workload : + phase_000002: duration 1.000000 [s] (10 loops)\n", + "2016-12-12 12:32:22,846 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n", + "2016-12-12 12:32:22,846 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n" + ] + } + ], + "source": [ + "# Create a new RTApp workload generator using the calibration values\n", + "rtapp = RTA(target, 'trappy', calibration=te.calibration())\n", + "\n", + "# Configure this RTApp instance to:\n", + "rtapp.conf(\n", + " kind='profile',\n", + " \n", + " params={\n", + " 'task_per20': Periodic(\n", + " period_ms=100,\n", + " duty_cycle_pct=20,\n", + " duration_s=5,\n", + " cpus=None,\n", + " sched={\n", + " \"policy\": \"FIFO\",\n", + " },\n", + " delay_s=0\n", + " ).get(),\n", + "\n", + " 'task_rmp20_5-60': Ramp(\n", + " period_ms=100,\n", + " start_pct=5,\n", + " end_pct=65,\n", + " delta_pct=20,\n", + " time_s=1,\n", + " cpus=\"0\"\n", + " ).get(),\n", + "\n", + " 'task_stp10-50': Step(\n", + " period_ms=100,\n", + " start_pct=0,\n", + " end_pct=50,\n", + " time_s=1,\n", + " delay_s=0.5\n", + " ).get(),\n", + "\n", + " 'task_pls5-80': Pulse(\n", + " period_ms=100,\n", + " start_pct=65,\n", + " end_pct=5,\n", + " time_s=1,\n", + " delay_s=0.5\n", + " ).get(),\n", + " },\n", + "\n", + " run_dir=target.working_directory\n", + " \n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 12:32:26,010 INFO : root : #### Setup FTrace\n", + "2016-12-12 12:32:30,739 INFO : root : #### Start energy sampling\n", + "2016-12-12 12:32:31,368 INFO : root : #### Start RTApp execution\n", + "2016-12-12 12:32:31,370 INFO : Workload : Workload execution START:\n", + "2016-12-12 12:32:31,372 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/trappy_00.json 2>&1\n", + "2016-12-12 12:32:48,588 INFO : root : #### Read energy consumption: /home/vagrant/lisa/results/20161212_123149/energy.json\n", + "2016-12-12 12:32:49,213 INFO : root : #### Stop FTrace\n", + "2016-12-12 12:32:51,475 INFO : root : #### Save FTrace: /home/vagrant/lisa/results/20161212_123149/trace.dat\n", + "2016-12-12 12:32:55,529 INFO : root : #### Save platform description: /home/vagrant/lisa/results/20161212_123149/platform.json\n" + ] + } + ], + "source": [ + "logging.info('#### Setup FTrace')\n", + "te.ftrace.start()\n", + "\n", + "logging.info('#### Start energy sampling')\n", + "te.emeter.reset()\n", + "\n", + "logging.info('#### Start RTApp execution')\n", + "rtapp.run(out_dir=te.res_dir, cgroup=\"\")\n", + "\n", + "logging.info('#### Read energy consumption: %s/energy.json', te.res_dir)\n", + "nrg_report = te.emeter.report(out_dir=te.res_dir)\n", + "\n", + "logging.info('#### Stop FTrace')\n", + "te.ftrace.stop()\n", + "\n", + "trace_file = os.path.join(te.res_dir, 'trace.dat')\n", + "logging.info('#### Save FTrace: %s', trace_file)\n", + "te.ftrace.get_trace(trace_file)\n", + "\n", + "logging.info('#### Save platform description: %s/platform.json', te.res_dir)\n", + "(plt, plt_file) = te.platform_dump(te.res_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Trace inspection" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "\n", + "\n", + "\n", + " \n", + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NOTE: The interactive trace visualization is available only if you run\n", + "# the workload to generate a new trace-file\n", + "trappy.plotter.plot_trace(te.res_dir)" + ] + } + ], + "metadata": { + "celltoolbar": "Raw Cell Format", + "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": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ipynb/examples/utils/executor_example.ipynb b/ipynb/examples/utils/executor_example.ipynb new file mode 100644 index 00000000..15d2ab49 --- /dev/null +++ b/ipynb/examples/utils/executor_example.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Executor API - Executor\n", + "\n", + " A tests executor is a module which supports the execution of a configured set of experiments.

\n", + " Each experiment is composed by:\n", + " - a target configuration\n", + " - a workload to execute\n", + "\n", + "The executor module can be configured to run a set of workloads (wloads) in each different target configuration of a specified set (confs). These wloads and confs can be specified by the \"experiments_conf\" input dictionary which is described below at **Experiments Configuration**.

\n", + "All the results generated by each experiment will be collected in a results folder. The format and content fo the results forlder is detailed in the last cell of **Tests execution** below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 11:58:22,693 INFO : root : Using LISA logging configuration:\n", + "2016-12-08 11:58:22,694 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], + "source": [ + "import logging\n", + "\n", + "from conf import LisaLogging\n", + "LisaLogging.setup()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "\n", + "from env import TestEnv\n", + "from executor import Executor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Target Configuration\n", + "The target configuration it's used to describe and configure your test environment.\n", + "You can find more details in **examples/utils/testenv_example.ipynb**." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a target configuration\n", + "my_target_conf = {\n", + " \n", + " # Target platform and board\n", + " \"platform\" : 'linux',\n", + " \"board\" : 'juno',\n", + " \n", + " # Target board IP/MAC address\n", + " \"host\" : '192.168.0.1',\n", + " \n", + " # Login credentials\n", + " \"username\" : 'root',\n", + " \"password\" : 'juno',\n", + "\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Experiments Configuration\n", + "\n", + "The experiments configuration defines the software setups that we need on our hardware target.
\n", + "This can be given as an argument to an Executor instance or to a TestEnv one.

\n", + "Elements of the experiments configuration:\n", + " - **confs**: **mandatory** platform configurations to be tested.\n", + " - tag: relevant string to identify your configuration.\n", + " - flags: ftrace (to enable ftrace events) is the only one supported at the moment.\n", + " - sched_features: features to be added to /sys/kernel/debug/sched_features.\n", + " - cpufreq: CpuFreq governor and tunables.\n", + " - cgroups: CGroups configuration (controller). The default CGroup will be used otherwise.\n", + " - **wloads**: **mandatory** workloads to run on each platform configuration.\n", + " - **iterations**: number of iterations for each workload.\n", + " - **tools**: binary tools (available under ./tools/$ARCH/) to install by default; these will be merged with the ones in the target configuration.\n", + " - **ftrace**: FTrace events to collect for all the experiments configurations which have the \"ftrace\" flag enabled.\n", + " - **modules**: modules required by the experiments resulted from the experiments configurations.\n", + " - **exclude_modules** - modules to be disabled.\n", + " - **results_dir**: results directory - experiments configuration results directory overrides target one." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "my_experiments_conf = {\n", + "\n", + " # Folder where all the results will be collected\n", + " \"results_dir\" : \"ExecutorExample\",\n", + "\n", + " # Platform configurations to test: you can specify any number of configurations\n", + " \"confs\" : [\n", + " {\n", + " \"tag\" : \"base\", # Relevant string to identify configuration\n", + " \"flags\" : \"ftrace\", # Enable FTrace events\n", + " \"sched_features\" : \"NO_ENERGY_AWARE\", # Disable EAS\n", + " \"cpufreq\" : { # Use PERFORMANCE CpuFreq\n", + " \"governor\" : \"performance\",\n", + " },\n", + " },\n", + " {\n", + " \"tag\" : \"eas\", # Relevant string to identify configuration\n", + " \"flags\" : \"ftrace\", # Enable FTrace events\n", + " \"sched_features\" : \"ENERGY_AWARE\", # Enable EAS\n", + " \"cpufreq\" : { # Use PERFORMANCE CpuFreq\n", + " \"governor\" : \"performance\",\n", + " },\n", + " },\n", + " ],\n", + " \n", + " # Workloads to run (on each platform configuration)\n", + " \"wloads\" : {\n", + " # Run hackbench with 1 group using pipes\n", + " \"perf\" : {\n", + " \"type\" : \"perf_bench\",\n", + " \"conf\" : {\n", + " \"class\" : \"messaging\",\n", + " \"params\" : {\n", + " \"group\" : 1,\n", + " \"loop\" : 10,\n", + " \"pipe\" : True,\n", + " \"thread\": True,\n", + " }\n", + " }\n", + " },\n", + " # Run a 20% duty-cycle periodic task\n", + " \"rta\" : {\n", + " \"type\" : \"rt-app\",\n", + " \"loadref\" : \"big\",\n", + " \"conf\" : {\n", + " \"class\" : \"profile\",\n", + " \"params\" : {\n", + " \"p20\" : {\n", + " \"kind\" : \"Periodic\",\n", + " \"params\" : {\n", + " \"duty_cycle_pct\" : 20,\n", + " },\n", + " },\n", + " },\n", + " },\n", + " },\n", + " },\n", + " \n", + " # Number of iterations for each workloaditerations\n", + " \"iterations\" : 1,\n", + " \n", + " # FTrace events to collect for all the tests configuration which have\n", + " # the \"ftrace\" flag enabled\n", + " \"ftrace\" : {\n", + " \"events\" : [\n", + " \"sched_switch\",\n", + " \"sched_wakeup\",\n", + " \"sched_wakeup_new\",\n", + " \"cpu_frequency\",\n", + " ],\n", + " \"buffsize\" : 80 * 1024,\n", + " },\n", + " \n", + " # Tools required by the experiments\n", + " \"tools\" : [ 'trace-cmd', 'perf' ],\n", + " \n", + " # Modules required by these experiments\n", + " \"modules\" : [ 'bl', 'cpufreq' ],\n", + "\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tests execution" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-07 10:17:28,037 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-07 10:17:28,039 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-07 10:17:28,039 INFO : TestEnv : Devlib modules to load: ['bl', 'hwmon', 'cpufreq']\n", + "2016-12-07 10:17:28,040 INFO : TestEnv : Connecting linux target:\n", + "2016-12-07 10:17:28,040 INFO : TestEnv : username : root\n", + "2016-12-07 10:17:28,041 INFO : TestEnv : host : 192.168.0.1\n", + "2016-12-07 10:17:28,041 INFO : TestEnv : password : juno\n", + "2016-12-07 10:17:28,041 INFO : TestEnv : Connection settings:\n", + "2016-12-07 10:17:28,042 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n", + "2016-12-07 10:17:45,282 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-07 10:17:45,283 INFO : TestEnv : /root/devlib-target\n", + "2016-12-07 10:17:51,006 INFO : TestEnv : Topology:\n", + "2016-12-07 10:17:51,007 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n", + "2016-12-07 10:17:52,248 INFO : EnergyMeter : Scanning for HWMON channels, may take some time...\n", + "2016-12-07 10:17:52,250 INFO : EnergyMeter : Channels selected for energy sampling:\n", + "2016-12-07 10:17:52,250 INFO : EnergyMeter : BOARDBIG_energy\n", + "2016-12-07 10:17:52,251 INFO : EnergyMeter : BOARDLITTLE_energy\n", + "2016-12-07 10:17:52,251 INFO : TestEnv : Set results folder to:\n", + "2016-12-07 10:17:52,251 INFO : TestEnv : /home/vagrant/lisa/results/20161207_101752\n", + "2016-12-07 10:17:52,252 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-07 10:17:52,252 INFO : TestEnv : /home/vagrant/lisa/results_latest\n", + "2016-12-07 10:17:52,253 INFO : Executor : Loading custom (inline) test configuration\n", + "2016-12-07 10:17:52,253 INFO : Executor : \n", + "2016-12-07 10:17:52,254 INFO : Executor : ################################################################################\n", + "2016-12-07 10:17:52,254 INFO : Executor : Experiments configuration\n", + "2016-12-07 10:17:52,254 INFO : Executor : ################################################################################\n", + "2016-12-07 10:17:52,255 INFO : Executor : Configured to run:\n", + "2016-12-07 10:17:52,255 INFO : Executor : 2 target configurations:\n", + "2016-12-07 10:17:52,256 INFO : Executor : base, eas\n", + "2016-12-07 10:17:52,256 INFO : Executor : 2 workloads (1 iterations each)\n", + "2016-12-07 10:17:52,257 INFO : Executor : rta, perf\n", + "2016-12-07 10:17:52,257 INFO : Executor : Total: 4 experiments\n", + "2016-12-07 10:17:52,257 INFO : Executor : Results will be collected under:\n", + "2016-12-07 10:17:52,258 INFO : Executor : /home/vagrant/lisa/results/20161207_101752\n" + ] + } + ], + "source": [ + "executor = Executor(TestEnv(my_target_conf), my_experiments_conf)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-07 10:17:59,239 INFO : Executor : \n", + "2016-12-07 10:17:59,239 INFO : Executor : ################################################################################\n", + "2016-12-07 10:17:59,240 INFO : Executor : Experiments execution\n", + "2016-12-07 10:17:59,240 INFO : Executor : ################################################################################\n", + "2016-12-07 10:17:59,241 INFO : Executor : \n", + "2016-12-07 10:17:59,241 INFO : Executor : ================================================================================\n", + "2016-12-07 10:17:59,241 INFO : Executor : configuring target for [base] experiments\n", + "2016-12-07 10:18:00,663 INFO : Executor : Set scheduler feature: NO_ENERGY_AWARE\n", + "2016-12-07 10:18:01,469 INFO : Executor : Configuring all CPUs to use [performance] cpufreq governor\n", + "2016-12-07 10:18:02,274 INFO : Workload : Setup new workload rta\n", + "2016-12-07 10:18:02,274 INFO : Workload : Workload duration defined by longest task\n", + "2016-12-07 10:18:02,275 INFO : Workload : Default policy: SCHED_OTHER\n", + "2016-12-07 10:18:02,275 INFO : Workload : ------------------------\n", + "2016-12-07 10:18:02,276 INFO : Workload : task [task_p200], sched: using default policy\n", + "2016-12-07 10:18:02,276 INFO : Workload : | calibration CPU: 1\n", + "2016-12-07 10:18:02,277 INFO : Workload : | loops count: 1\n", + "2016-12-07 10:18:02,277 INFO : Workload : + phase_000001: duration 1.000000 [s] (10 loops)\n", + "2016-12-07 10:18:02,278 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n", + "2016-12-07 10:18:02,278 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n", + "2016-12-07 10:18:05,732 INFO : Executor : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "2016-12-07 10:18:05,734 INFO : Executor : Experiment 0/4, [base:rta] 1/1\n", + "2016-12-07 10:18:06,361 INFO : Workload : Workload execution START:\n", + "2016-12-07 10:18:06,363 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json 2>&1\n", + "2016-12-07 10:18:13,384 INFO : Executor : --------------------------------------------------------------------------------\n", + "2016-12-07 10:18:13,386 INFO : Workload : Setup new workload perf\n", + "2016-12-07 10:18:13,712 INFO : Executor : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "2016-12-07 10:18:13,712 INFO : Executor : Experiment 1/4, [base:perf] 1/1\n", + "2016-12-07 10:18:14,337 INFO : Workload : Workload execution START:\n", + "2016-12-07 10:18:14,338 INFO : Workload : /root/devlib-target/bin/perf bench sched messaging --pipe --thread --group 1 --loop 10\n", + "2016-12-07 10:18:14,664 INFO : perf_bench : PerfBench - Completion time: 0.010000, Performance 100.000000\n", + "2016-12-07 10:18:15,286 INFO : Executor : --------------------------------------------------------------------------------\n", + "2016-12-07 10:18:15,288 INFO : Executor : \n", + "2016-12-07 10:18:15,290 INFO : Executor : ================================================================================\n", + "2016-12-07 10:18:15,292 INFO : Executor : configuring target for [eas] experiments\n", + "2016-12-07 10:18:16,713 INFO : Executor : Set scheduler feature: ENERGY_AWARE\n", + "2016-12-07 10:18:17,519 INFO : Executor : Configuring all CPUs to use [performance] cpufreq governor\n", + "2016-12-07 10:18:18,325 INFO : Workload : Setup new workload rta\n", + "2016-12-07 10:18:18,326 INFO : Workload : Workload duration defined by longest task\n", + "2016-12-07 10:18:18,327 INFO : Workload : Default policy: SCHED_OTHER\n", + "2016-12-07 10:18:18,329 INFO : Workload : ------------------------\n", + "2016-12-07 10:18:18,330 INFO : Workload : task [task_p200], sched: using default policy\n", + "2016-12-07 10:18:18,331 INFO : Workload : | calibration CPU: 1\n", + "2016-12-07 10:18:18,332 INFO : Workload : | loops count: 1\n", + "2016-12-07 10:18:18,332 INFO : Workload : + phase_000001: duration 1.000000 [s] (10 loops)\n", + "2016-12-07 10:18:18,333 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n", + "2016-12-07 10:18:18,333 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n", + "2016-12-07 10:18:21,414 INFO : Executor : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "2016-12-07 10:18:21,415 INFO : Executor : Experiment 2/4, [eas:rta] 1/1\n", + "2016-12-07 10:18:22,043 INFO : Workload : Workload execution START:\n", + "2016-12-07 10:18:22,045 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json 2>&1\n", + "2016-12-07 10:18:28,802 INFO : Executor : --------------------------------------------------------------------------------\n", + "2016-12-07 10:18:28,806 INFO : Workload : Setup new workload perf\n", + "2016-12-07 10:18:29,134 INFO : Executor : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "2016-12-07 10:18:29,135 INFO : Executor : Experiment 3/4, [eas:perf] 1/1\n", + "2016-12-07 10:18:29,760 INFO : Workload : Workload execution START:\n", + "2016-12-07 10:18:29,761 INFO : Workload : /root/devlib-target/bin/perf bench sched messaging --pipe --thread --group 1 --loop 10\n", + "2016-12-07 10:18:30,089 INFO : perf_bench : PerfBench - Completion time: 0.009000, Performance 111.111111\n", + "2016-12-07 10:18:30,710 INFO : Executor : --------------------------------------------------------------------------------\n", + "2016-12-07 10:18:30,712 INFO : Executor : \n", + "2016-12-07 10:18:30,714 INFO : Executor : ################################################################################\n", + "2016-12-07 10:18:30,715 INFO : Executor : Experiments execution completed\n", + "2016-12-07 10:18:30,716 INFO : Executor : ################################################################################\n", + "2016-12-07 10:18:30,716 INFO : Executor : Results available in:\n", + "2016-12-07 10:18:30,717 INFO : Executor : /home/vagrant/lisa/results/20161207_101752\n" + ] + } + ], + "source": [ + "executor.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/vagrant/lisa/results/20161207_101752\r\n", + "├── perf_bench_messaging:base:perf\r\n", + "│   ├── 1\r\n", + "│   │   ├── energy.json\r\n", + "│   │   ├── output.log\r\n", + "│   │   └── performance.json\r\n", + "│   ├── kernel.config\r\n", + "│   ├── kernel.version\r\n", + "│   └── platform.json\r\n", + "├── perf_bench_messaging:eas:perf\r\n", + "│   ├── 1\r\n", + "│   │   ├── energy.json\r\n", + "│   │   ├── output.log\r\n", + "│   │   └── performance.json\r\n", + "│   ├── kernel.config\r\n", + "│   ├── kernel.version\r\n", + "│   └── platform.json\r\n", + "├── rtapp:base:rta\r\n", + "│   ├── 1\r\n", + "│   │   ├── energy.json\r\n", + "│   │   ├── output.log\r\n", + "│   │   ├── rta_00.json\r\n", + "│   │   └── rt-app-task_p200-0.log\r\n", + "│   ├── kernel.config\r\n", + "│   ├── kernel.version\r\n", + "│   └── platform.json\r\n", + "└── rtapp:eas:rta\r\n", + " ├── 1\r\n", + " │   ├── energy.json\r\n", + " │   ├── output.log\r\n", + " │   ├── rta_00.json\r\n", + " │   └── rt-app-task_p200-0.log\r\n", + " ├── kernel.config\r\n", + " ├── kernel.version\r\n", + " └── platform.json\r\n", + "\r\n", + "8 directories, 26 files\r\n" + ] + } + ], + "source": [ + "!tree {executor.te.res_dir}" + ] + } + ], + "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": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ipynb/examples/utils/testenv_example.ipynb b/ipynb/examples/utils/testenv_example.ipynb new file mode 100644 index 00000000..f32a5f07 --- /dev/null +++ b/ipynb/examples/utils/testenv_example.ipynb @@ -0,0 +1,899 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test environment API - TestEnv\n", + "\n", + "The test environment is primarily defined by the target configuration (see **conf** below).\n", + "\n", + "One can also pass the test configuration - definining which software setups are needed on the hardware target, and a location for the results of the experiments.\n", + "\n", + "Parameters:\n", + " - target configuration\n", + " - test configuration - more information on this can be found in **examples/utils/executor_example.ipynb**\n", + " - wipe - if to clean up all previous content from the results folder\n", + " - force new - if to create a new TestEnv object even if there is one available" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# One initial cell for imports\n", + "import json\n", + "import time\n", + "import os\n", + "import logging" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 11:51:14,376 INFO : root : Using LISA logging configuration:\n", + "2016-12-12 11:51:14,376 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], + "source": [ + "from conf import LisaLogging\n", + "LisaLogging.setup()\n", + "# For debug information use:\n", + "# LisaLogging.setup(level=logging.DEBUG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test environment setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Target configuration**:\n", + "\n", + " - **platform** - the currently supported boards are:\n", + " - linux - accessed via SSH connection\n", + " - android - accessed via ADB connection\n", + " - host - ran on local host\n", + " \n", + " - **board** - the currently supported boards are:\n", + " - juno - target is a JUNO board\n", + " - tc2 - target is a TC2 board\n", + " - oak - target is MT8173 platform model\n", + " - pixel - target is a Pixel device\n", + " - hikey - target is a Hikey development platform\n", + " - nexus5x - target is a Nexus 5X device\n", + " \n", + " - **host** - target IP or MAC address\n", + " \n", + " - **device** - target Android device ID\n", + " \n", + " - **port** - port for Android connection - default port is 5555\n", + " \n", + " - **ANDROID_HOME** - path to android-sdk-linux\n", + " \n", + " - **username**\n", + " \n", + " - **password**\n", + " \n", + " - **keyfile** - you can either specify a password or a keyfile\n", + " \n", + " - **rtapp-calib** - these values are not supposed to be specified at the first run on a target.\n", + " After the first run, it's best to fill this array with values reported in the log messges for\n", + " your specific target, for these not to be obtained again.\n", + " \n", + " - **tftp** - tftp server from where the target gets kernel/dtb images at each boot\n", + " \n", + " - **modules** - devlib modules to be enabled\n", + " \n", + " - **exclude_modules** - devlib modules to be disabled\n", + " \n", + " - **tools** - binary tools (available under ./tools/$ARCH/) to install by default\n", + " \n", + " - **ping_time** - wait time before trying to access the target after reboot\n", + " \n", + " - **reboot_time** - maximum time to wait after rebooting the target\n", + " \n", + " - **__features__** - list of test environment features to enable\n", + " - no-kernel - do not deploy kernel/dtb images\n", + " - no-reboot - do not force reboot the target at each configuration change\n", + " - debug - enable debugging messages\n", + " \n", + " - **ftrace** - ftrace configuration\n", + " - events\n", + " - functions\n", + " - buffsize\n", + " \n", + " - **results_dir** - location of results of the experiments\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Setup a target configuration\n", + "conf = {\n", + "\n", + " # Platform\n", + " \"platform\" : \"linux\",\n", + " # Board\n", + " \"board\" : \"juno\",\n", + "\n", + " # Login credentials\n", + " \"host\" : \"192.168.0.1\",\n", + " \"username\" : \"root\",\n", + " # You can specify either a password or keyfile\n", + " \"password\" : \"juno\",\n", + " # \"keyfile\" : \"/complete/path/of/your/keyfile\",\n", + "\n", + " # Tools to deploy\n", + " \"tools\" : [ \"rt-app\", \"taskset\" ],\n", + "\n", + " \"tftp\" : {\n", + " \"folder\" : \"/var/lib/tftpboot/\",\n", + " \"kernel\" : \"Image\",\n", + " \"dtb\" : \"juno.dtb\"\n", + " },\n", + "\n", + " #\"ping_time\" : \"15\",\n", + " #\"reboot_time\" : \"180\",\n", + "\n", + " # RTApp calibration values (comment to let LISA do a calibration run)\n", + " \"rtapp-calib\" : {\n", + " \"0\": 358, \"1\": 138, \"2\": 138, \"3\": 357, \"4\": 359, \"5\": 355\n", + " },\n", + "\n", + " # FTrace configuration\n", + " \"ftrace\" : {\n", + " \"events\" : [\n", + " \"cpu_idle\",\n", + " \"sched_switch\",\n", + " ],\n", + " \"buffsize\" : 10240,\n", + " },\n", + " \n", + " # Where results are collected\n", + " \"results_dir\" : \"TestEnvExample\",\n", + "\n", + " #\"__features__\" : \"no-kernel no-reboot\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test environment initialisation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "from env import TestEnv\n", + "\n", + "# Initialize a test environment using the provided configuration\n", + "te = TestEnv(conf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Attributes\n", + "\n", + "The initialisation of the test environment pre-initialises some useful environment variables.\n", + "\n", + "This is some of the information available via the TestEnv object:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"username\": \"root\", \n", + " \"ftrace\": {\n", + " \"buffsize\": 10240, \n", + " \"events\": [\n", + " \"cpu_idle\", \n", + " \"sched_switch\"\n", + " ]\n", + " }, \n", + " \"rtapp-calib\": {\n", + " \"1\": 138, \n", + " \"0\": 358, \n", + " \"3\": 357, \n", + " \"2\": 138, \n", + " \"5\": 355, \n", + " \"4\": 359\n", + " }, \n", + " \"host\": \"192.168.0.1\", \n", + " \"password\": \"juno\", \n", + " \"tools\": [\n", + " \"rt-app\", \n", + " \"taskset\", \n", + " \"trace-cmd\", \n", + " \"taskset\", \n", + " \"trace-cmd\", \n", + " \"perf\", \n", + " \"cgroup_run_into.sh\"\n", + " ], \n", + " \"results_dir\": \"TestEnvExample\", \n", + " \"platform\": \"linux\", \n", + " \"board\": \"juno\", \n", + " \"__features__\": [], \n", + " \"tftp\": {\n", + " \"kernel\": \"Image\", \n", + " \"folder\": \"/var/lib/tftpboot/\", \n", + " \"dtb\": \"juno.dtb\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "# The complete configuration of the target we have configured\n", + "print json.dumps(te.conf, indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n", + "None\n" + ] + } + ], + "source": [ + "# Last configured kernel and DTB image\n", + "print te.kernel\n", + "print te.dtb" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "192.168.0.1\n", + "None\n" + ] + } + ], + "source": [ + "# The IP and MAC address of the target\n", + "print te.ip\n", + "print te.mac" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"nrg_model\": {\n", + " \"big\": {\n", + " \"cluster\": {\n", + " \"nrg_max\": 64\n", + " }, \n", + " \"cpu\": {\n", + " \"cap_max\": 1024, \n", + " \"nrg_max\": 616\n", + " }\n", + " }, \n", + " \"little\": {\n", + " \"cluster\": {\n", + " \"nrg_max\": 57\n", + " }, \n", + " \"cpu\": {\n", + " \"cap_max\": 447, \n", + " \"nrg_max\": 93\n", + " }\n", + " }\n", + " }, \n", + " \"clusters\": {\n", + " \"big\": [\n", + " 1, \n", + " 2\n", + " ], \n", + " \"little\": [\n", + " 0, \n", + " 3, \n", + " 4, \n", + " 5\n", + " ]\n", + " }, \n", + " \"cpus_count\": 6, \n", + " \"freqs\": {\n", + " \"big\": [\n", + " 450000, \n", + " 625000, \n", + " 800000, \n", + " 950000, \n", + " 1100000\n", + " ], \n", + " \"little\": [\n", + " 450000, \n", + " 575000, \n", + " 700000, \n", + " 775000, \n", + " 850000\n", + " ]\n", + " }, \n", + " \"topology\": [\n", + " [\n", + " 0, \n", + " 3, \n", + " 4, \n", + " 5\n", + " ], \n", + " [\n", + " 1, \n", + " 2\n", + " ]\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "# A full platform descriptor\n", + "print json.dumps(te.platform, indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/vagrant/lisa/results/TestEnvExample'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This is a pre-created folder to host the tests results generated using this\n", + "# test environment. Notice that the suite could add additional information\n", + "# in this folder, like for example a copy of the target configuration\n", + "# and other target specific collected information.\n", + "te.res_dir" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'/data/local/schedtest'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The working directory on the target\n", + "te.workdir" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "cluster [[0, 3, 4, 5], [1, 2]]\n", + "cpu [[0], [1], [2], [3], [4], [5]]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The target topology, which can be used to build BART assertions\n", + "te.topology" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functions\n", + "\n", + "Some methods are also exposed to test developers which could be used to ease the creation of tests.\n", + "\n", + "These are some of the methods available:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 358, 1: 138, 2: 138, 3: 357, 4: 359, 5: 355}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calibrate RT-App (if required) and get the most updated calibration value\n", + "te.calibration()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "({'clusters': {'big': [1, 2], 'little': [0, 3, 4, 5]},\n", + " 'cpus_count': 6,\n", + " 'freqs': {'big': [450000, 625000, 800000, 950000, 1100000],\n", + " 'little': [450000, 575000, 700000, 775000, 850000]},\n", + " 'nrg_model': {u'big': {u'cluster': {u'nrg_max': 64},\n", + " u'cpu': {u'cap_max': 1024, u'nrg_max': 616}},\n", + " u'little': {u'cluster': {u'nrg_max': 57},\n", + " u'cpu': {u'cap_max': 447, u'nrg_max': 93}}},\n", + " 'topology': [[0, 3, 4, 5], [1, 2]]},\n", + " '/tmp/platform.json')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Generate a JSON file with the complete platform description\n", + "te.platform_dump(dest_dir='/tmp')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 11:56:15,281 INFO : TestEnv : Target (00:02:f7:00:5d:d7) at IP address: 192.168.0.1\n", + "2016-12-12 11:56:16,087 INFO : TestEnv : Waiting up to 360[s] for target [192.168.0.1] to reboot...\n", + "2016-12-12 11:57:21,143 INFO : TestEnv : Devlib modules to load: ['bl', 'hwmon', 'cpufreq']\n", + "2016-12-12 11:57:21,144 INFO : TestEnv : Connecting linux target:\n", + "2016-12-12 11:57:21,145 INFO : TestEnv : username : root\n", + "2016-12-12 11:57:21,146 INFO : TestEnv : host : 192.168.0.1\n", + "2016-12-12 11:57:21,146 INFO : TestEnv : password : juno\n", + "2016-12-12 11:57:21,147 INFO : TestEnv : Connection settings:\n", + "2016-12-12 11:57:21,147 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n", + "2016-12-12 11:57:37,176 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-12 11:57:37,177 INFO : TestEnv : /root/devlib-target\n", + "2016-12-12 11:57:43,908 INFO : TestEnv : Topology:\n", + "2016-12-12 11:57:43,908 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n", + "2016-12-12 11:57:45,155 INFO : TestEnv : Loading default EM:\n", + "2016-12-12 11:57:45,156 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/juno.json\n", + "2016-12-12 11:57:48,681 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-12 11:57:48,684 INFO : TestEnv : cpu_idle\n", + "2016-12-12 11:57:48,685 INFO : TestEnv : sched_switch\n", + "2016-12-12 11:57:48,688 INFO : EnergyMeter : Scanning for HWMON channels, may take some time...\n", + "2016-12-12 11:57:48,691 INFO : EnergyMeter : Channels selected for energy sampling:\n", + "2016-12-12 11:57:48,691 INFO : EnergyMeter : BOARDBIG_energy\n", + "2016-12-12 11:57:48,692 INFO : EnergyMeter : BOARDLITTLE_energy\n" + ] + } + ], + "source": [ + "# Force a reboot of the target (and wait specified [s] before reconnect)\n", + "# Keep in mind that a reboot can be disabled from __features__ in the target configuration\n", + "te.reboot(reboot_time=360, ping_time=15)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "06:03:00 INFO : HostResolver - Target (00:02:F7:00:5A:5B) at IP address: 192.168.0.1\n" + ] + }, + { + "data": { + "text/plain": [ + "('00:02:F7:00:5A:5B', '192.168.0.1')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Resolve a MAC address into an IP address\n", + "te.resolv_host(host='00:02:F7:00:5A:5B')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "06:03:00 INFO : TFTP - Deploy /etc/group into /var/lib/tftpboot/group\n" + ] + } + ], + "source": [ + "# Copy the specified file into the TFTP server folder defined by configuration\n", + "te.tftp_deploy('/etc/group')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Attributes: target\n", + "### Access to the devlib API\n", + "\n", + "A special attribute of TestEnv is **target**, which represents a devlib instance. Using the target attribute we have access to the full set of devlib provided functionalities. A small set of these are exemplified below. For a more extensive set check the **examples/devlib** notebooks." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello Test Environment'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run a command on the target\n", + "te.target.execute(\"echo -n 'Hello Test Environment'\", as_root=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "''" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Spawn a command in background on the target\n", + "te.target.kick_off(\"sleep 10\", as_root=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ABI : arm64\n", + "big Core Family : A57\n", + "LITTLE Core Family : A53\n", + "CPU's Clusters IDs : [0, 1, 1, 0, 0, 0]\n", + "CPUs type : ['A53', 'A57', 'A57', 'A53', 'A53', 'A53']\n" + ] + } + ], + "source": [ + "# Acces to many target specific information\n", + "print \"ABI : \", te.target.abi\n", + "print \"big Core Family : \", te.target.big_core\n", + "print \"LITTLE Core Family : \", te.target.little_core\n", + "print \"CPU's Clusters IDs : \", te.target.core_clusters\n", + "print \"CPUs type : \", te.target.core_names" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "big CPUs IDs : [1, 2]\n", + "LITTLE CPUs IDs : [0, 3, 4, 5]\n", + "big CPUs freqs : 450000\n", + "big CPUs governor : interactive\n" + ] + } + ], + "source": [ + "# Access to big.LITTLE specific information\n", + "print \"big CPUs IDs : \", te.target.bl.bigs\n", + "print \"LITTLE CPUs IDs : \", te.target.bl.littles\n", + "print \"big CPUs freqs : {}\".format(te.target.bl.get_bigs_frequency())\n", + "print \"big CPUs governor : {}\".format(te.target.bl.get_bigs_governor())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Attributes: emeter (energy meter)\n", + "\n", + "In order to sample energy from the target:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First read: {\n", + " \"BOARDBIG\": {\n", + " \"total\": 0.03712299999999935, \n", + " \"last\": 5.61159, \n", + " \"delta\": 0.019406000000000034\n", + " }, \n", + " \"BOARDLITTLE\": {\n", + " \"total\": 0.017602000000000118, \n", + " \"last\": 4.954883, \n", + " \"delta\": 0.008766999999999747\n", + " }\n", + "}\n", + "Second read: {\n", + " \"BOARDBIG\": {\n", + " \"total\": 0.11209199999999964, \n", + " \"last\": 5.686559, \n", + " \"delta\": 0.018203999999999887\n", + " }, \n", + " \"BOARDLITTLE\": {\n", + " \"total\": 0.06513600000000075, \n", + " \"last\": 5.002417, \n", + " \"delta\": 0.009789000000000492\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "# Reset and sample energy counters\n", + "te.emeter.reset()\n", + "nrg = te.emeter.sample()\n", + "nrg = json.dumps(te.emeter.sample(), indent=4)\n", + "print \"First read: \", nrg\n", + "time.sleep(2)\n", + "nrg = te.emeter.sample()\n", + "nrg = json.dumps(te.emeter.sample(), indent=4)\n", + "print \"Second read: \", nrg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Attribute: ftrace\n", + "\n", + "You can configure FTrace for a specific experiment using the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-12 11:58:44,190 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-12 11:58:44,192 INFO : TestEnv : cpu_idle\n", + "2016-12-12 11:58:44,193 INFO : TestEnv : cpu_capacity\n", + "2016-12-12 11:58:44,194 INFO : TestEnv : cpu_frequency\n", + "2016-12-12 11:58:44,194 INFO : TestEnv : sched_switch\n" + ] + } + ], + "source": [ + "# Configure a specific set of events to trace\n", + "te.ftrace_conf(\n", + " { \n", + " \"events\" : [ \n", + " \"cpu_idle\", \n", + " \"cpu_capacity\",\n", + " \"cpu_frequency\",\n", + " \"sched_switch\",\n", + " ], \n", + " \"buffsize\" : 10240 \n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Start/Stop a FTrace session\n", + "te.ftrace.start()\n", + "te.target.execute(\"uname -a\")\n", + "te.ftrace.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Collect and visualize the trace\n", + "trace_file = os.path.join(te.res_dir, 'trace.dat')\n", + "te.ftrace.get_trace(trace_file)\n", + "\n", + "# There might be a different display value on your machine\n", + "# Check by issuing \"echo $DISPLAY\" in the LISA VM\n", + "output = os.popen(\"DISPLAY=:10.0 kernelshark {}\".format(trace_file))" + ] + } + ], + "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": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ipynb/examples/wlgen/rtapp_example.ipynb b/ipynb/examples/wlgen/rtapp_example.ipynb new file mode 100644 index 00000000..51d4f549 --- /dev/null +++ b/ipynb/examples/wlgen/rtapp_example.ipynb @@ -0,0 +1,1083 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RTA workload\n", + "\n", + "The RTA or RTApp workload represents a type of workload obtained using the rt-app test application.\n", + "More details on the test application can be found at https://github.com/scheduler-tools/rt-app." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-08 12:02:29,102 INFO : root : Using LISA logging configuration:\n", + "2016-12-08 12:02:29,103 INFO : root : /home/vagrant/lisa/logging.conf\n" + ] + } + ], + "source": [ + "import logging\n", + "from conf import LisaLogging\n", + "LisaLogging.setup()" + ] + }, + { + "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", + "# Support to initialise and configure your test environment\n", + "import devlib\n", + "from env import TestEnv\n", + "\n", + "# Support to configure and run RTApp based workloads\n", + "from wlgen import RTA, Periodic, Ramp, Step, Pulse\n", + "\n", + "# Suport for FTrace events parsing and visualization\n", + "import trappy\n", + "\n", + "# Support for performance analysis of RTApp workloads\n", + "from perf_analysis import PerfAnalysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test environment setup\n", + "\n", + "For more details on this please check out **examples/utils/testenv_example.ipynb**." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Setup a target configuration\n", + "my_target_conf = {\n", + " \n", + " # Define the kind of target platform to use for the experiments\n", + " \"platform\" : 'linux', # Linux system, valid other options are:\n", + " # android - access via ADB\n", + " # linux - access via SSH\n", + " # host - direct access\n", + " \n", + " # Preload settings for a specific target\n", + " \"board\" : 'juno', # juno - JUNO board with mainline hwmon\n", + " \n", + " # Define devlib module to load\n", + " \"modules\" : [\n", + " 'bl', # enable big.LITTLE support\n", + " 'cpufreq' # enable CPUFreq support\n", + " ],\n", + "\n", + " # Account to access the remote target\n", + " \"host\" : '192.168.0.1',\n", + " \"username\" : 'root',\n", + " \"password\" : 'juno',\n", + "\n", + " # Comment the following line to force rt-app calibration on your target\n", + " \"rtapp-calib\" : {\n", + " '0': 361, '1': 138, '2': 138, '3': 352, '4': 360, '5': 353\n", + " }\n", + "\n", + "}\n", + "\n", + "# Setup the required Test Environment supports\n", + "my_tests_conf = {\n", + " \n", + " # Binary tools required to run this experiment\n", + " # These tools must be present in the tools/ folder for the architecture\n", + " \"tools\" : ['rt-app', 'taskset', 'trace-cmd'],\n", + " \n", + " # FTrace events end buffer configuration\n", + " \"ftrace\" : {\n", + " \"events\" : [\n", + " \"sched_switch\",\n", + " \"cpu_frequency\"\n", + " ],\n", + " \"buffsize\" : 10240\n", + " },\n", + "\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-07 10:25:45,152 INFO : TestEnv : Using base path: /home/vagrant/lisa\n", + "2016-12-07 10:25:45,153 INFO : TestEnv : Loading custom (inline) target configuration\n", + "2016-12-07 10:25:45,153 INFO : TestEnv : Loading custom (inline) test configuration\n", + "2016-12-07 10:25:45,153 INFO : TestEnv : Devlib modules to load: ['bl', 'cpufreq', 'hwmon']\n", + "2016-12-07 10:25:45,154 INFO : TestEnv : Connecting linux target:\n", + "2016-12-07 10:25:45,154 INFO : TestEnv : username : root\n", + "2016-12-07 10:25:45,155 INFO : TestEnv : host : 192.168.0.1\n", + "2016-12-07 10:25:45,155 INFO : TestEnv : password : juno\n", + "2016-12-07 10:25:45,156 INFO : TestEnv : Connection settings:\n", + "2016-12-07 10:25:45,156 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n", + "2016-12-07 10:26:02,855 INFO : TestEnv : Initializing target workdir:\n", + "2016-12-07 10:26:02,856 INFO : TestEnv : /root/devlib-target\n", + "2016-12-07 10:26:23,640 INFO : TestEnv : Topology:\n", + "2016-12-07 10:26:23,643 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n", + "2016-12-07 10:26:28,585 INFO : TestEnv : Enabled tracepoints:\n", + "2016-12-07 10:26:28,587 INFO : TestEnv : sched_switch\n", + "2016-12-07 10:26:28,589 INFO : TestEnv : cpu_frequency\n", + "2016-12-07 10:26:28,591 WARNING : TestEnv : Using configuration provided RTApp calibration\n", + "2016-12-07 10:26:28,591 INFO : TestEnv : Using RT-App calibration values:\n", + "2016-12-07 10:26:28,592 INFO : TestEnv : {\"0\": 361, \"1\": 138, \"2\": 138, \"3\": 352, \"4\": 360, \"5\": 353}\n", + "2016-12-07 10:26:28,592 INFO : EnergyMeter : Scanning for HWMON channels, may take some time...\n", + "2016-12-07 10:26:28,594 INFO : EnergyMeter : Channels selected for energy sampling:\n", + "2016-12-07 10:26:28,595 INFO : EnergyMeter : BOARDBIG_energy\n", + "2016-12-07 10:26:28,595 INFO : EnergyMeter : BOARDLITTLE_energy\n", + "2016-12-07 10:26:28,596 INFO : TestEnv : Set results folder to:\n", + "2016-12-07 10:26:28,596 INFO : TestEnv : /home/vagrant/lisa/results/20161207_102628\n", + "2016-12-07 10:26:28,597 INFO : TestEnv : Experiment results available also in:\n", + "2016-12-07 10:26:28,597 INFO : TestEnv : /home/vagrant/lisa/results_latest\n" + ] + } + ], + "source": [ + "# Initialize a test environment using\n", + "# - the provided target configuration (my_target_conf)\n", + "# - the provided test configuration (my_test_conf)\n", + "te = TestEnv(target_conf=my_target_conf, test_conf=my_tests_conf)\n", + "target = te.target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Workload configuration\n", + "\n", + "To create an instance of an RTApp workload generator you need to provide the following:\n", + "- target: target device configuration\n", + "- name: name of workload. This is the name of the JSON configuration file reporting the generated RTApp configuration.\n", + "- calibration: CPU load calibration values, measured on each core.\n", + "\n", + "An RTApp workload is defined by specifying a **kind**, provided below through **rtapp.conf**, which represents the way we want to define the behavior of each task.\n", + "The possible kinds of workloads are **profile** and **custom**. It's very important to notice that **periodic** is no longer considered a \"kind\" of workload but a \"class\" within the **profile** kind.\n", + "

\n", + "As you see below, when \"kind\" is \"profile\", the tasks generated by this workload have a profile which is defined by a sequence of phases. These phases are defined according to the following grammar:
\n", + " - params := {task, ...}
\n", + " - task := NAME : {SCLASS, PRIO, [phase, ...]}
\n", + " - phase := (PTIME, PERIOD, DCYCLE)

\n", + " \n", + "There are some pre-defined task classes for the **profile** kind:\n", + " - **Step**: the load of this task is a step with a configured initial and final load. \n", + " - **Pulse**: the load of this task is a pulse with a configured initial and final load.The main difference with the 'step' class is that a pulse workload is by definition a 'step down', i.e. the workload switches from an initial load to a final one which is always lower than the initial one. Moreover, a pulse load does not generate a sleep phase in case of 0[%] load, i.e. the task ends as soon as the non null initial load has completed.\n", + " - **Ramp**: the load of this task is a ramp with a configured number of steps determined by the input parameters.\n", + " - **Periodic**: the load of this task is periodic with a configured period and duty-cycle.

\n", + "The one below is a workload mix having all types of workloads described above, but each of them can also be specified serapately in the RTApp parameters. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-07 10:27:16,946 INFO : Workload : Setup new workload simple\n", + "2016-12-07 10:27:16,947 INFO : Workload : Workload duration defined by longest task\n", + "2016-12-07 10:27:16,947 INFO : Workload : Default policy: SCHED_OTHER\n", + "2016-12-07 10:27:16,948 INFO : Workload : ------------------------\n", + "2016-12-07 10:27:16,948 INFO : Workload : task [task_per20], sched: {'policy': 'FIFO'}\n", + "2016-12-07 10:27:16,949 INFO : Workload : | calibration CPU: 1\n", + "2016-12-07 10:27:16,949 INFO : Workload : | loops count: 1\n", + "2016-12-07 10:27:16,949 INFO : Workload : + phase_000001: duration 5.000000 [s] (50 loops)\n", + "2016-12-07 10:27:16,950 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n", + "2016-12-07 10:27:16,950 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n", + "2016-12-07 10:27:16,951 INFO : Workload : ------------------------\n", + "2016-12-07 10:27:16,951 INFO : Workload : task [task_pls5-80], sched: using default policy\n", + "2016-12-07 10:27:16,952 INFO : Workload : | start delay: 0.500000 [s]\n", + "2016-12-07 10:27:16,952 INFO : Workload : | calibration CPU: 1\n", + "2016-12-07 10:27:16,952 INFO : Workload : | loops count: 1\n", + "2016-12-07 10:27:16,953 INFO : Workload : + phase_000001: duration 1.000000 [s] (10 loops)\n", + "2016-12-07 10:27:16,953 INFO : Workload : | period 100000 [us], duty_cycle 65 %\n", + "2016-12-07 10:27:16,954 INFO : Workload : | run_time 65000 [us], sleep_time 35000 [us]\n", + "2016-12-07 10:27:16,954 INFO : Workload : + phase_000002: duration 1.000000 [s] (10 loops)\n", + "2016-12-07 10:27:16,955 INFO : Workload : | period 100000 [us], duty_cycle 5 %\n", + "2016-12-07 10:27:16,955 INFO : Workload : | run_time 5000 [us], sleep_time 95000 [us]\n", + "2016-12-07 10:27:16,955 INFO : Workload : ------------------------\n", + "2016-12-07 10:27:16,956 INFO : Workload : task [task_rmp20_5-60], sched: using default policy\n", + "2016-12-07 10:27:16,956 INFO : Workload : | calibration CPU: 1\n", + "2016-12-07 10:27:16,957 INFO : Workload : | loops count: 1\n", + "2016-12-07 10:27:16,957 INFO : Workload : | CPUs affinity: 0\n", + "2016-12-07 10:27:16,958 INFO : Workload : + phase_000001: duration 1.000000 [s] (10 loops)\n", + "2016-12-07 10:27:16,958 INFO : Workload : | period 100000 [us], duty_cycle 5 %\n", + "2016-12-07 10:27:16,958 INFO : Workload : | run_time 5000 [us], sleep_time 95000 [us]\n", + "2016-12-07 10:27:16,959 INFO : Workload : + phase_000002: duration 1.000000 [s] (10 loops)\n", + "2016-12-07 10:27:16,959 INFO : Workload : | period 100000 [us], duty_cycle 25 %\n", + "2016-12-07 10:27:16,960 INFO : Workload : | run_time 25000 [us], sleep_time 75000 [us]\n", + "2016-12-07 10:27:16,960 INFO : Workload : + phase_000003: duration 1.000000 [s] (10 loops)\n", + "2016-12-07 10:27:16,961 INFO : Workload : | period 100000 [us], duty_cycle 45 %\n", + "2016-12-07 10:27:16,961 INFO : Workload : | run_time 45000 [us], sleep_time 55000 [us]\n", + "2016-12-07 10:27:16,962 INFO : Workload : + phase_000004: duration 1.000000 [s] (10 loops)\n", + "2016-12-07 10:27:16,962 INFO : Workload : | period 100000 [us], duty_cycle 65 %\n", + "2016-12-07 10:27:16,963 INFO : Workload : | run_time 65000 [us], sleep_time 35000 [us]\n", + "2016-12-07 10:27:16,963 INFO : Workload : ------------------------\n", + "2016-12-07 10:27:16,964 INFO : Workload : task [task_stp10-50], sched: using default policy\n", + "2016-12-07 10:27:16,964 INFO : Workload : | start delay: 0.500000 [s]\n", + "2016-12-07 10:27:16,964 INFO : Workload : | calibration CPU: 1\n", + "2016-12-07 10:27:16,965 INFO : Workload : | loops count: 1\n", + "2016-12-07 10:27:16,965 INFO : Workload : + phase_000001: sleep 1.000000 [s]\n", + "2016-12-07 10:27:16,966 INFO : Workload : + phase_000002: duration 1.000000 [s] (10 loops)\n", + "2016-12-07 10:27:16,966 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n", + "2016-12-07 10:27:16,967 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n" + ] + } + ], + "source": [ + "# Create a new RTApp workload generator using the calibration values\n", + "# reported by the TestEnv module\n", + "rtapp = RTA(target, 'simple', calibration=te.calibration())\n", + "\n", + "# Configure this RTApp instance to:\n", + "rtapp.conf(\n", + "# 1. generate a \"profile based\" set of tasks\n", + " kind='profile',\n", + " \n", + " # 2. define the \"profile\" of each task\n", + " params={\n", + "\n", + " # 3. PERIODIC task\n", + " # \n", + " # This class defines a task which load is periodic with a configured\n", + " # period and duty-cycle.\n", + " # \n", + " # This class is a specialization of the 'pulse' class since a periodic\n", + " # load is generated as a sequence of pulse loads.\n", + " # \n", + " # Args:\n", + " # cuty_cycle_pct (int, [0-100]): the pulses load [%]\n", + " # default: 50[%]\n", + " # duration_s (float): the duration in [s] of the entire workload\n", + " # default: 1.0[s]\n", + " # period_ms (float): the period used to define the load in [ms]\n", + " # default: 100.0[ms]\n", + " # delay_s (float): the delay in [s] before ramp start\n", + " # default: 0[s]\n", + " # sched (dict): the scheduler configuration for this task\n", + " # cpus (list): the list of CPUs on which task can run\n", + " 'task_per20': Periodic(\n", + " period_ms=100, # period\n", + " duty_cycle_pct=20, # duty cycle\n", + " duration_s=5, # duration\n", + " cpus=None, # run on all CPUS\n", + " sched={\n", + " \"policy\": \"FIFO\", # Run this task as a SCHED_FIFO task\n", + " },\n", + " delay_s=0 # start at the start of RTApp\n", + " ).get(),\n", + "\n", + " # 4. RAMP task\n", + " #\n", + " # This class defines a task which load is a ramp with a configured number\n", + " # of steps according to the input parameters.\n", + " # \n", + " # Args:\n", + " # start_pct (int, [0-100]): the initial load [%], (default 0[%])\n", + " # end_pct (int, [0-100]): the final load [%], (default 100[%])\n", + " # delta_pct (int, [0-100]): the load increase/decrease [%],\n", + " # default: 10[%]\n", + " # increase if start_prc < end_prc\n", + " # decrease if start_prc > end_prc\n", + " # time_s (float): the duration in [s] of each load step\n", + " # default: 1.0[s]\n", + " # period_ms (float): the period used to define the load in [ms]\n", + " # default: 100.0[ms]\n", + " # delay_s (float): the delay in [s] before ramp start\n", + " # default: 0[s]\n", + " # loops (int): number of time to repeat the ramp, with the\n", + " # specified delay in between\n", + " # default: 0\n", + " # sched (dict): the scheduler configuration for this task\n", + " # cpus (list): the list of CPUs on which task can run\n", + " 'task_rmp20_5-60': Ramp(\n", + " period_ms=100, # period\n", + " start_pct=5, # intial load\n", + " end_pct=65, # end load\n", + " delta_pct=20, # load % increase...\n", + " time_s=1, # ... every 1[s]\n", + " cpus=\"0\" # run just on first CPU\n", + " ).get(),\n", + " \n", + " # 5. STEP task\n", + " # \n", + " # This class defines a task which load is a step with a configured\n", + " # initial and final load.\n", + " # \n", + " # Args:\n", + " # start_pct (int, [0-100]): the initial load [%]\n", + " # default 0[%])\n", + " # end_pct (int, [0-100]): the final load [%]\n", + " # default 100[%]\n", + " # time_s (float): the duration in [s] of the start and end load\n", + " # default: 1.0[s]\n", + " # period_ms (float): the period used to define the load in [ms]\n", + " # default 100.0[ms]\n", + " # delay_s (float): the delay in [s] before ramp start\n", + " # default 0[s]\n", + " # loops (int): number of time to repeat the ramp, with the\n", + " # specified delay in between\n", + " # default: 0\n", + " # sched (dict): the scheduler configuration for this task\n", + " # cpus (list): the list of CPUs on which task can run\n", + " 'task_stp10-50': Step(\n", + " period_ms=100, # period\n", + " start_pct=0, # intial load\n", + " end_pct=50, # end load\n", + " time_s=1, # ... every 1[s]\n", + " delay_s=0.5 # start .5[s] after the start of RTApp\n", + " ).get(),\n", + " \n", + " # 6. PULSE task\n", + " #\n", + " # This class defines a task which load is a pulse with a configured\n", + " # initial and final load.\n", + " # \n", + " # The main difference with the 'step' class is that a pulse workload is\n", + " # by definition a 'step down', i.e. the workload switch from an finial\n", + " # load to a final one which is always lower than the initial one.\n", + " # Moreover, a pulse load does not generate a sleep phase in case of 0[%]\n", + " # load, i.e. the task ends as soon as the non null initial load has\n", + " # completed.\n", + " # \n", + " # Args:\n", + " # start_pct (int, [0-100]): the initial load [%]\n", + " # default: 0[%]\n", + " # end_pct (int, [0-100]): the final load [%]\n", + " # default: 100[%]\n", + " # NOTE: must be lower than start_pct value\n", + " # time_s (float): the duration in [s] of the start and end load\n", + " # default: 1.0[s]\n", + " # NOTE: if end_pct is 0, the task end after the\n", + " # start_pct period completed\n", + " # period_ms (float): the period used to define the load in [ms]\n", + " # default: 100.0[ms]\n", + " # delay_s (float): the delay in [s] before ramp start\n", + " # default: 0[s]\n", + " # loops (int): number of time to repeat the ramp, with the\n", + " # specified delay in between\n", + " # default: 0\n", + " # sched (dict): the scheduler configuration for this task\n", + " # cpus (list): the list of CPUs on which task can run\n", + " 'task_pls5-80': Pulse(\n", + " period_ms=100, # period\n", + " start_pct=65, # intial load\n", + " end_pct=5, # end load\n", + " time_s=1, # ... every 1[s]\n", + " delay_s=0.5 # start .5[s] after the start of RTApp\n", + " ).get(),\n", + " \n", + " \n", + " },\n", + " \n", + " # 7. use this folder for task logfiles\n", + " run_dir=target.working_directory\n", + " \n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output of the previous cell reports the main properties of the generated\n", + "tasks. Thus for example we see that the first task is configure to be:\n", + " - named **task_per20**\n", + " - executed as a **SCHED_FIFO** task\n", + " - generating a load which is **calibrated** with respect to the **CPU 1**\n", + " - with one single \"phase\" which defines a peripodic load for the **duration** of **5[s]**\n", + " - that periodic load consistes of **50 cycles**\n", + " - each cycle has a **period** of **100[ms]** and a **duty-cycle** of **20%**,\n", + " which means that the task, for every cycle, will **run** for **20[ms]** and then sleep for **80[ms]** \n", + "\n", + "All these properties are translated into a JSON configuration file for RTApp which you can see in **Collected results** below.
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Workload composition\n", + "\n", + "Another way of specifying the phases of a task is through workload composition, described in the next cell.
\n", + "**NOTE:** We are just giving this as an example of specifying a workload, but this configuration won't be the one used for the following execution and analysis cells. You need to uncomment these lines if you want to use the composed workload." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initial phase and pinning parameters\n", + "ramp = Ramp(period_ms=100, start_pct=5, end_pct=65, delta_pct=20, time_s=1, cpus=\"0\")\n", + "\n", + "# Following phases\n", + "medium_slow = Periodic(duty_cycle_pct=10, duration_s=5, period_ms=100)\n", + "high_fast = Periodic(duty_cycle_pct=60, duration_s=5, period_ms=10)\n", + "medium_fast = Periodic(duty_cycle_pct=10, duration_s=5, period_ms=1)\n", + "high_slow = Periodic(duty_cycle_pct=60, duration_s=5, period_ms=100)\n", + "\n", + "#Compose the task\n", + "complex_task = ramp + medium_slow + high_fast + medium_fast + high_slow\n", + "\n", + "# Configure this RTApp instance to:\n", + "# rtapp.conf(\n", + "# # 1. generate a \"profile based\" set of tasks\n", + "# kind='profile',\n", + "# \n", + "# # 2. define the \"profile\" of each task\n", + "# params={\n", + "# 'complex' : complex_task.get()\n", + "# },\n", + "#\n", + "# # 6. use this folder for task logfiles\n", + "# run_dir='/tmp'\n", + "#)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Workload execution" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-07 10:27:30,511 INFO : root : #### Setup FTrace\n", + "2016-12-07 10:27:38,104 INFO : root : #### Start energy sampling\n", + "2016-12-07 10:27:38,723 INFO : root : #### Start RTApp execution\n", + "2016-12-07 10:27:38,726 INFO : Workload : Workload execution START:\n", + "2016-12-07 10:27:38,728 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/simple_00.json 2>&1\n", + "2016-12-07 10:27:54,778 INFO : root : #### Read energy consumption: /home/vagrant/lisa/results/20161207_102628/energy.json\n", + "2016-12-07 10:27:55,401 INFO : root : #### Stop FTrace\n", + "2016-12-07 10:27:57,663 INFO : root : #### Save FTrace: /home/vagrant/lisa/results/20161207_102628/trace.dat\n", + "2016-12-07 10:28:03,718 INFO : root : #### Save platform description: /home/vagrant/lisa/results/20161207_102628/platform.json\n" + ] + } + ], + "source": [ + "logging.info('#### Setup FTrace')\n", + "te.ftrace.start()\n", + "\n", + "logging.info('#### Start energy sampling')\n", + "te.emeter.reset()\n", + "\n", + "logging.info('#### Start RTApp execution')\n", + "rtapp.run(out_dir=te.res_dir, cgroup=\"\")\n", + "\n", + "logging.info('#### Read energy consumption: %s/energy.json', te.res_dir)\n", + "nrg_report = te.emeter.report(out_dir=te.res_dir)\n", + "\n", + "logging.info('#### Stop FTrace')\n", + "te.ftrace.stop()\n", + "\n", + "trace_file = os.path.join(te.res_dir, 'trace.dat')\n", + "logging.info('#### Save FTrace: %s', trace_file)\n", + "te.ftrace.get_trace(trace_file)\n", + "\n", + "logging.info('#### Save platform description: %s/platform.json', te.res_dir)\n", + "(plt, plt_file) = te.platform_dump(te.res_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collected results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-07 10:29:04,768 INFO : root : Generated RTApp JSON file:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"global\": {\n", + " \"calibration\": 138, \n", + " \"default_policy\": \"SCHED_OTHER\", \n", + " \"duration\": -1, \n", + " \"logdir\": \"/root/devlib-target\"\n", + " }, \n", + " \"tasks\": {\n", + " \"task_per20\": {\n", + " \"loop\": 1, \n", + " \"phases\": {\n", + " \"p000001\": {\n", + " \"loop\": 50, \n", + " \"run\": 20000, \n", + " \"timer\": {\n", + " \"period\": 100000, \n", + " \"ref\": \"task_per20\"\n", + " }\n", + " }\n", + " }, \n", + " \"policy\": \"SCHED_FIFO\"\n", + " }, \n", + " \"task_pls5-80\": {\n", + " \"loop\": 1, \n", + " \"phases\": {\n", + " \"p000000\": {\n", + " \"delay\": 500000\n", + " }, \n", + " \"p000001\": {\n", + " \"loop\": 10, \n", + " \"run\": 65000, \n", + " \"timer\": {\n", + " \"period\": 100000, \n", + " \"ref\": \"task_pls5-80\"\n", + " }\n", + " }, \n", + " \"p000002\": {\n", + " \"loop\": 10, \n", + " \"run\": 5000, \n", + " \"timer\": {\n", + " \"period\": 100000, \n", + " \"ref\": \"task_pls5-80\"\n", + " }\n", + " }\n", + " }, \n", + " \"policy\": \"SCHED_OTHER\"\n", + " }, \n", + " \"task_rmp20_5-60\": {\n", + " \"cpus\": [\n", + " 0\n", + " ], \n", + " \"loop\": 1, \n", + " \"phases\": {\n", + " \"p000001\": {\n", + " \"loop\": 10, \n", + " \"run\": 5000, \n", + " \"timer\": {\n", + " \"period\": 100000, \n", + " \"ref\": \"task_rmp20_5-60\"\n", + " }\n", + " }, \n", + " \"p000002\": {\n", + " \"loop\": 10, \n", + " \"run\": 25000, \n", + " \"timer\": {\n", + " \"period\": 100000, \n", + " \"ref\": \"task_rmp20_5-60\"\n", + " }\n", + " }, \n", + " \"p000003\": {\n", + " \"loop\": 10, \n", + " \"run\": 45000, \n", + " \"timer\": {\n", + " \"period\": 100000, \n", + " \"ref\": \"task_rmp20_5-60\"\n", + " }\n", + " }, \n", + " \"p000004\": {\n", + " \"loop\": 10, \n", + " \"run\": 65000, \n", + " \"timer\": {\n", + " \"period\": 100000, \n", + " \"ref\": \"task_rmp20_5-60\"\n", + " }\n", + " }\n", + " }, \n", + " \"policy\": \"SCHED_OTHER\"\n", + " }, \n", + " \"task_stp10-50\": {\n", + " \"loop\": 1, \n", + " \"phases\": {\n", + " \"p000000\": {\n", + " \"delay\": 500000\n", + " }, \n", + " \"p000001\": {\n", + " \"loop\": 1, \n", + " \"sleep\": 1000000\n", + " }, \n", + " \"p000002\": {\n", + " \"loop\": 10, \n", + " \"run\": 50000, \n", + " \"timer\": {\n", + " \"period\": 100000, \n", + " \"ref\": \"task_stp10-50\"\n", + " }\n", + " }\n", + " }, \n", + " \"policy\": \"SCHED_OTHER\"\n", + " }\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "# Inspect the JSON file used to run the application\n", + "with open('{}/simple_00.json'.format(te.res_dir), 'r') as fh:\n", + " rtapp_json = json.load(fh, )\n", + "logging.info('Generated RTApp JSON file:')\n", + "print json.dumps(rtapp_json, indent=4, sort_keys=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-07 10:29:06,513 INFO : root : Content of the output folder /home/vagrant/lisa/results/20161207_102628\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 3592\r\n", + "drwxrwxr-x 2 user user 4096 Dec 7 10:28 .\r\n", + "drwxrwxr-x 22 user user 4096 Dec 7 10:26 ..\r\n", + "-rw-rw-r-- 1 user user 68 Dec 7 10:27 energy.json\r\n", + "-rw-rw-r-- 1 user user 705 Dec 7 10:27 output.log\r\n", + "-rw-rw-r-- 1 user user 673 Dec 7 10:28 platform.json\r\n", + "-rw-r--r-- 1 user user 6360 Dec 7 10:27 rt-app-task_per20-0.log\r\n", + "-rw-r--r-- 1 user user 2764 Dec 7 10:27 rt-app-task_pls5-80-1.log\r\n", + "-rw-r--r-- 1 user user 5120 Dec 7 10:27 rt-app-task_rmp20_5-60-2.log\r\n", + "-rw-r--r-- 1 user user 1648 Dec 7 10:27 rt-app-task_stp10-50-3.log\r\n", + "-rw-r--r-- 1 user user 3104 Dec 7 10:27 simple_00.json\r\n", + "-rw-r--r-- 1 user user 3629056 Dec 7 10:28 trace.dat\r\n" + ] + } + ], + "source": [ + "# All data are produced in the output folder defined by the TestEnv module\n", + "logging.info('Content of the output folder %s', te.res_dir)\n", + "!ls -la {te.res_dir}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-07 10:29:09,792 INFO : root : Energy: /home/vagrant/lisa/results/20161207_102628/energy.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"LITTLE\": 1.8224460000000136, \n", + " \"big\": 0.5827259999999796\n", + "}\n" + ] + } + ], + "source": [ + "# Dump the energy measured for the LITTLE and big clusters\n", + "logging.info('Energy: %s', nrg_report.report_file)\n", + "print json.dumps(nrg_report.channels, indent=4, sort_keys=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-07 10:29:14,705 INFO : root : Platform description: /home/vagrant/lisa/results/20161207_102628/platform.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"clusters\": {\n", + " \"big\": [\n", + " 1, \n", + " 2\n", + " ], \n", + " \"little\": [\n", + " 0, \n", + " 3, \n", + " 4, \n", + " 5\n", + " ]\n", + " }, \n", + " \"cpus_count\": 6, \n", + " \"freqs\": {\n", + " \"big\": [\n", + " 450000, \n", + " 625000, \n", + " 800000, \n", + " 950000, \n", + " 1100000\n", + " ], \n", + " \"little\": [\n", + " 450000, \n", + " 575000, \n", + " 700000, \n", + " 775000, \n", + " 850000\n", + " ]\n", + " }, \n", + " \"nrg_model\": null, \n", + " \"topology\": [\n", + " [\n", + " 0, \n", + " 3, \n", + " 4, \n", + " 5\n", + " ], \n", + " [\n", + " 1, \n", + " 2\n", + " ]\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "# Dump the platform descriptor, which could be useful for further analysis\n", + "# of the generated results\n", + "logging.info('Platform description: %s', plt_file)\n", + "print json.dumps(plt, indent=4, sort_keys=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Trace inspection\n", + "\n", + "More information on visualization and trace inspection can be found in **examples/trappy**." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "\n", + "\n", + "\n", + " \n", + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NOTE: The interactive trace visualization is available only if you run\n", + "# the workload to generate a new trace-file\n", + "trappy.plotter.plot_trace(te.res_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RTApp task performance plots" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-07 10:29:27,425 INFO : PerfAnalysis : PerfIndex, Task [task_rmp20_5-60] avg: -5.34, std: 8.79\n", + "2016-12-07 10:29:27,795 INFO : PerfAnalysis : PerfIndex, Task [task_pls5-80] avg: -8.28, std: 7.04\n", + "2016-12-07 10:29:28,161 INFO : PerfAnalysis : PerfIndex, Task [task_stp10-50] avg: -24.23, std: 1.75\n", + "2016-12-07 10:29:28,546 INFO : PerfAnalysis : PerfIndex, Task [task_per20] avg: 0.56, std: 0.06\n" + ] + }, + { + "data": { + "image/png": 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S+lh2J17EW2u1IsPT41fOMeroY34lUepuTaJ9xsuJJPThFK+q21qd+bzdhUim3A8cSlRr\nzk9UljpH27LvTiGSQ18mOskpp6pzRzkw+f1DoufvF8klOwHWKTLfe0T17i8Q238sUTpvANGT+N4F\n5mkkSu7NA36ezLO0KPY5r8kbV+y8Phw4hzhv9iVKyhaTVtku1j7x2uSS08+XWE57fJb8zn5JtR3x\nBWD+z5fzxueXZM4/xygwPP++mW7HcjRtSzbf+plpJUmqGBOekqRlyfPES98alN+GXaWkL42DS0zz\nxeR3qfbMlpS0pM4MClfRbGv7hcWkVUbL7QTmqeR3sXZC22uTIsPT41fOMUqP+boU7+Cl2DHPNh1Q\nyptEte7DierKjURP49kevNuiPdvQXi3tg9rkd6Ee4qG8c7Q1+y5N/j8EbE9U3W1L6eFylLPtjUSy\nN6snpa8zqf8DLiBKwv4yGXZ4kWkfJNpnXEA0CfCdItN1NcU+5+sRNQEWUbi6/H7A75Pxh5Crxl1M\n2tbwdkXGb5/8fp34Qq4jpJ/TVzPD0y/48n/yO877H7mEZrGEbTp9fuyvEJ+vGlre7lJtMUuS1CYm\nPCVJy5L5RC/D3Snc43RHui9Z/7rkqpHm+zKwDVHd+64lGFcxaRXRYiX7Tqzw+m5Ifu9J05ftYv5D\ntP23OdGBUSXVAN8tMLwX8K3k7zvLWM4zxEv/Cnnz5VuLKFHXSPPe7tNq4MU6Ciq1zrSU45qtnLfY\n8tq6De3V0j5IxxdKTq5K9NLeGuXsu3nAGGAaUQL6n1SmA5+scra9hsLbfiilm3koJE04lTpn7iWu\nXR8DFxIdznR1e1P4Gpd+/u+neZMMu5JrPuW7RLMKLbmRSI5uAmxVYHx6rl5XxrLaojdwZPJ3oY6H\nSpkP3EKcb4cUGL8muc6xsh32XZ/8LnSubAdsRLSVemMrY5IkSZKkpV49kSQ8uMzpNydeYD8l2u3L\nltBak6iy+e3M8Hpa7jV5JMV7aQf4VTJ+FrBF3vANiBKLDURP0/lqKd6DdrnrnZCM/2OR8RNp3ht2\nD2BuMvzHecOXJzqumE/x3nNb6lW3nsL78rpk3mdpWqUSov3E4zPDjkymf4tIlGZ9CTib4qWLCkl7\nI/6EpknxFYj2DdNeh7PnTR2Fe97+XjL8PZpWrV+dSCA1AP8tEMdNybjseQjRZuXVRKI3/8vrNJHf\nQFTDLbfKdS2lz7G2bkN7e1feO1nGvRRuYmIouWO1U97wNYnjkZ6j+Z+Ltuy7yTS/xqxCtGnbQByr\nbDuhpdTRci/tpY4/RMnMBqJ0a35ycyzRDm667fkxHwicStN2FknmvzuZ/orMuELHcBci6fkZuar1\nbVFLda5tkDumC4kvmFbNG/d14pxaRJT2zTeM3L79YYm4C/lDMt9T5JocqCHaYG0gShC3pYO11A+J\na2K2WZD1iWYL0nN7vTYsezPinvkpMD5v+MrEl4gNRA2KbHX5WuJcaQCOyxs+gKg90EAkz0uxl3ZJ\nkiRJy6R6WpfwhEiOzUvmW0BU2XuIKMmWJrzOKrKe9iQ8lyeXWEhffB8jEgcNRE/J2ar2tVQvKfDd\nvFhnE20kvp/EexjtS3gW2pcrE4mzdJ0vJet8g+L74Ky86ecCDxOl79Jk7SKiJ+9ypfMcn/z9erLM\n98klJYYVmK+O4kmsK/NifD6J75O8bawtMM/4vHmeSJY/lUjYr5w3bh5xDj1CtLHaQO74lKuWls+x\ntmxDexMVfcgdx9eJUtJ1xBcVqb9n4vofkZR5j1wCM/9z0ZZ9N5nC15h+RFMMDUSyvtx2f+toOeFZ\n6vhDJMzmJOPnE9v9UvL/v4C/FIj52Lxlvkqc10+QO46v0DzhVuwY7k4kCz8F9im1sSXUUv2E58+J\nDnPmA4+S24eLiKRy1rPk7hv/Ic7JQj9jC8y7IvG5aSCSgNOJ45AmXvcvsg3lmkTuHH6eaIYgTSqm\nX1js2o7lf5vYLw1EJ1ePENfDBqLq+mZF5juI3D1uFrHdC5P/H6blUuwmPCVJkiQtk14iXoZak/CE\nSLZNIpKO84gX2HoicTGeXK/h2fWUSniOoPTLOUQJmKOJF70PyCVdTqFwe4C1tJwUaGm9h1A6KXA6\nsW3ZpABET8/Tif0zlygNlSYQ25rwLLUvexDtA/6H6OX3I+AF4BqKv6xvSyTk6pM45xHVky8lEg+l\nepIvFfv+RCL8wySWKRRvM3RqMl+xJNZ4olOTNLkyg2g3sVRbskcT50aanE+X340oWTeZOH/fIbfN\nf6J44qGYWlo+x9qyDZVIVAwlqtO+TSTXFtH0PO4JnEHE/jHRhuBfgYEU/ly0Zd9dQfFrzOrJvIuI\n0tnlNBdVR8sJTyh+/FMDgWvJfU6eAn5C7JNCMfcnShPeTnwGPyJKRz9CXH+y1zwofQz3Jo7JJ0QC\ntLVqqd61bTK5hPBA4vryJnFeT6d4cwjptavYT7q/it2Plk9ieSZZ11tEcx5bF5m+NbYmSt8/QCRS\nFxBf1DxGlHRvT+nR1DCiGYc3k+XPBM6n5eYztiWqrb9NbPfTRGnjcpqDMOEpSZIkSVI7+XKtjlZH\nnGeVbntW5ZtM62sGqDq8JkuS2qQ1JR4kSZIkVcYFRCnv14F9qxyL1JkcQ9PPRE21ApEkdV0mPCVJ\nkqQlpzH5+WLy/8tVjEXqjNYjOptrTP5vLDGtJEkFmfCUJEmSlpxR1Q6gA+wC/LjMaRuB/0e0A6nC\nDqP8jscageEdGEs1/CD5kSSpzUx4SpIkSWqPL9C0RF4xNck0y3V4RKWlpWw7q3Vo3f6UJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJElSVU0AGvJ+PgVmAX8E\n1qrgenoBvwdmA58B05Ph9cBNFVwPwGTgpQovU8uYbtUOQFKX0VDmzw4VXN8FbZivNhPPXnnjtgNO\nB1Zqb3AtqKfyN/1qGQRMAv4PeA+YC9wH7F1k+i8QDyhvAx8B9wM7tmG9Eyh+jn2hFcvpCRwHPAHM\nB94F/gtsW2Dao4EZwMfAi8BpQI/MNN/PxPL5MuOYClzYirgBdiXO147yXeCQAsM3JPbB0DKXM4HY\nF0MqExY/Avao0LKWJZMp/8WgF/AscHyHRbPkjKTt955NgInAgALjJtPxL1ojgXlA/1bMMwa4E3iN\n+Jy+RlxfTspMVw9c0e4Ii6tn6bnPSdLSYgKwDfBV4FJgf+A/wAoVWv53gCOAM4HtgYOS4Y3JT6V1\nxDIlSWpmq7yfrYGbiRe1rTI/fSq0vgbg/DbMV5vMe0YSzyp5445Pxq3b3uBa8BJwYwevY0k5ikiM\n/IR4eBpDvEQ3AKdmpl2OSCy+TDxg7QRMARbS+mTEhGQdB9P8HMsmIYvpTpyn7wInJzHsCvw4iS3f\nj4FFwM+S6Y4nkgl/yEy3ehLDpcn05SQ89yU+K61J1EIkSBtaOU9rPEkkSgr5HfBAmcuZQGUTnvOI\nEglqnclEor4cJwKvEInPrm4kbU94/r8S864PbN72sMp2G3BVmdMeScT7d2BPIu4DgYuBhzPTvkTH\nfo6WpvucJHV1Eyj8LHZGMnz/di4/TZheSjynZdVT+XvCZCzhKUmqksnAhx24/PYmPA8uMC5NeBYq\nzVNJ9XTci2ClvqEtV78iw9OEd8+8Yd8l9u/WecO6E4m1B1u53gm0P4n2faK6zVYtTLcqsIBI8uU7\nhUhqDi4wz0TKL+H5OHBZGdNlXZisv9LSc6hUwnMTYvvKKZ07gcomPD+kY0umLa0mU96LwXLAW8SX\nGEuDkbQ/4TmikgG10q7E53zDMqZ9meKf2ayOTnjWY8JTkjqLCRR+Fts1GX5y8v93gceIWk/vAP8A\n1svMU0cUYNiBqKn1EfHFXKFaV+n7Vj1N7wm1yfgfEjWtXiKe7+6n6XtCfvzPEoUNniZKjk6m+XNN\nL+L5Ja2R9RZxr8t/XzmZuK/ulpl3crItXyywfkmSmphM84Tn94B7gTeJZNjjwAk0L5G3JZEwe5Nc\nlbybgbXzpskmPGuAs4jSgt8sEVcthROeEyldBf8bRDXB14mHgKeBXwC9M8tZH7iaXHXCN4B/0bQk\nUD3NXwS/S7Sn05oqynU0f+D4G5GwbSASuCcRL8Hzk+k3IhIa5yQxvgtcR/PEZT1RHfHrxHFaAMwk\nqnWX47QkhtXzht1F7Lesk5Np1yxz2ZB7cCu3WnUhLwF3lzHdgcm6sonRNZLhpxSYZyLlJTyHUzgZ\n0xv4dRLjAqKpgEeA/ZLxkyl8vqalk8v9rNVR+KH1pQLLzpYOfJR4EG7JBFpOeC4H/Ab4H7mmEe4H\nds9MV2ib78kbvwZR6nYW8Am5pge6501TS+sesrcmPgtziGPxAtGMA+SO334F5js4Gffl4ptNP6L0\n3VNJDG8S5+SwzHStjXkC5b0YFHIQxUu6b0ycH28ky34Z+BNNS4J+Cfgn8aK0gDim2evtSHIlSs4m\nrqsfEvt5daJZkUuJpi/eJr4QyF5r02ZNvg08l8TzFHGtLrSu7Gfsy8R1eG4S53Rgn7zxEyj98jaZ\n5vtzeeK+8BJx/r1KfDGRbSalPtnWscl65wPPAIfSXHdif/+qwLisD4l7QDmyCc9yP4MQTV4dTe6l\n+F2ixPe4vGnqqcx9TpLUfhMo/Cx2TDL8m8AlxL3rHGBn4tnmaaI9zvxaSFOJZ6KXiev6DsRzy1bE\n+9pH5GpdrZrMU0/hhOeLwC3E/WN3oomsuUDfArFfTyRoDyDu+y/T9Nm0G1Er4kMi6bkjcBjxTPgk\ncY9O3ZKsJ33WOTRZx2FIklSGyTRPeJ5L3BjHECVmjiW+ebs8b5rPETfRh4h2IIcRL6EXES/bqfyE\n53LES/h7xA26lFoKJzzXBs5Lxu1B8yr4PwF+QNxohxPt08ykecJsBpFoOCCJ/evEg0N+CaF6cjf9\nbsRL5ifk2rkpV7EHjjTh+RJwA7BLEs/sJLa/E8mE0cl2fEBULc/3EvGAUE+04zgG+Au5pEs5sb1B\nJKJTs4lkcNbXkuV+tYzlpiYk86SNos8lErflfiu7TjL/eUSi/E3iRfxJmp8bv0imLVR69i3gygLD\nJ1JewvMXRMKgZ2b474lE5bHEcd2FqGb83WT8+sRxTBOx6U+aeCrnswbFz6EtiMTeo3nLzlbf/Q3w\nPi239z2BlhOefYlrxkFEgmpn4nPzKU0/F1sTD9I35cWVXhfWIKphvwh8CxhFNEWwgKaJnVrKf8ge\nQ3yJ8r8kjhHJ9vw1b5ppRPtXWQ/TcsnlQUTCcz9yx/lS4pzOv2a0JuYJlPdiUMxVRabbnLimzwQO\nJ47TAcn0KybTbERcT54jvigYS+yrBiLhnhpJ7hp1OXG802vRVODfRCJ0p2S+T8klmVMNyTY9QTQL\nsRtwazI8vw3hdF35Cc9RxDW3jijFuTNxjuTfG/qR+zLmSJq/vE3O7Kca4HbifJmYxH5css+m0TQp\n/BJxrj6Z7KevAtck6xpOc9dS+MuirDuT9Z8ObEbTRH9WNuFZ7mcQ4M9E6Zg/EPt9NPHFz1F509RT\nmfucJKn9JpB7ZuxB3Le/Rjwbvk/cIxuI58V8axPPXb/MG1ZH8doPkylcw6+ewgnPx2j6rvDlZHj6\n5WU3ooBGtlmWdcl9sZ3aj9x7XL6h5O7lqc8T9+EHiYI2HxH3NkmSyjKZ0lXauxE33IOIF6q0BEx6\nUxpXZL5UmvD8PJFseAXYtIy4amm5Snuhkk35aojY04eDdL2rJv+3VAqynrjpL08k6d4hXsBbq47C\nDxy1yfBs2qg4AAAgAElEQVTpmeHpt7jZ5Oa5yfDPZWL8jOb79A4isVyq6vy3kuUdlRn+CZHcydqW\npg835RgD/JRI6AwjknWvEOdcOefBNsk63yMSJnsTSYc0ifitvGkvIZJmhTxLfJucNZHyEp53E8m0\nrCeIc6OUctvwLPZZg9IPrU/StPRkVlqCsaX9PYHWV2nvTsR8GZEsyvchhavi/p54aM928HJcsv60\n6YFaynvIhkj6PkfptiwPSebLTwhvlQwbX2K+QtLtvoumx7/cmFvzYlDMTJpfIyDO1bnkEn6FXEUk\n8NfODL+FSOCnidmRSdw3ZKZLr0XZ5Ob1REnPfA3JMlfLG9aNSAw+lzcsXVd+wvMZosR0Nll/I7H/\nUqXa8JxM0xKeYyj8hdA+NL+m1BMvV/nn6nLElw/ZpjMgV2K+b4Fx+dYnSnOnpVE/Is6l79K8dHdL\nVdqLfQbTUs0/bSGWeipzn5Mktd8ECtdaeIx4Dv8Z8UVWP+Lan//zAE2/wK0j7leFTKZ1Cc+fZ6Zb\njqZfkg5O/v9BgWVOpelzzZXEc0o2/h4ULnSxLfElYVpzL1uTRMsAe2mXVElbEje7OUQybSFRHbIb\nUTII4Hmietw5RFXFTUosb33iJrwikcB6okOizq3rb+RKFC4kbviQS6S8QyQLTiRuzFtS+DraSDxQ\nTCUSvMMov921rHeI0lCF3Jr5f0by+5Yiw7OJ3qdovk+vIl66tyyyzl2I0rj/oPW9jrfGHUQS4Fai\nV/iLiRfxRpq+iKfJ6fQnLfGUHpfliKTpdUTTA/sSieLTOjD2fGtQ+KHxIeKb918QCZvWts1azmct\nVeocKiWNuzVNEZSyD/Bf4kH5UyLmw2hasruU3YjP0WyaHvPbk/HZpO4tNO3dMz3X08/BIOJzf3kS\nSzFXEcm47+UNO4ooNXFNGXEfSZxzC8ht904U3u6WYt6IOB7ZTm5eIaonl6PQOdmb2H9/J14mitmR\nSIy+lhk+OVnGNpnhN2f+L3WNWpXmLyN30zQR2pDEuCGwVpEYNyT201XkvgxIf24j9l/2M1KOtD3b\nyZnh1xKJx2x7t48RVd5TnxCJ2kJfuKXHY40WYniRSLyPIK5h/wK+QlyLHyCud6WU8xncJfl9UQvL\nquR9TpJUGQcRX5ZuQdzvtiDuD6sTz8xvEdf+/J+taf5l5+wKxZN9pvgk+Z0+96brfaPAvG/S9Evg\n1YnOaLPxL0zGZbfhYSLRuTzxHjG/9eGrqzPhKalS1iXaFFyTKGk4jLjhfo+4WaXtqnxAvKw9RlQ1\nfpJ4eZ5I8xIqWwEDiRfc1zsw9hWJUqRfIarIjkhi3ysZn8beSCQq7iCSntOIB4fzyFX5hNjeQUn8\nt1NeVcViSj1wvJP5f2ELw7NJtUIPF+mwQqW8xhAlse4gqmlmzaVwicfP541vj5eJl/X8pMoVNH3g\nuSuzrhlE1f18dxIlr1bNm3Y5mrb9k/p8BeIu5Bii+tCeRCnLuUSpu3I6Lin3s5aq1ENre+xFJAdn\nEefONkTMf6T8ZO/qRDXvNFGT/jxJfDaz52xLD9lpycFXKW0hUbX3AOLLgNWIxPllSSylHEc8ZD9A\n7IOtievM7RTe7va+GJSjscCwVYhnwpb2xecpfD6lw7LHoLXXqOy529prFOTaFf41zV+ILiKXqGut\nVck1r5Gvkdj3LZ1/JDG0t+O5RuJ+9TOiWt9axGdrKKXbJiv3M7gasZ0tnU+VvM9JkirjGeJL1sdp\neh2fQ9w/tieu/dmfPTPLKfSs0BHSe2WhL9fXyMQxJ5m+UPxfJtcsVOoMot3xR4EziVKnWsZkkwuS\n1FZ7ElWm96JpgqlQNdcnic4sINohm0CUVllAtOuWupq4Wf+cXKdFHWFH4kY7gqZt9RVK3r1Cruri\nhkRV04lEldjvJMMbidJW15JrU/E7tO3hoSMfOIo9XEDzl/UxRPXUqUT18M8KzPsEcTyz0irRT7Yh\nxkLy98npNO3cKq1mM5ModVXOch5Pfm9G06rCaxBJjPbEPZumVXJT84nzZmIyflciAXoThXuFz9ea\nzxq0/RxKk0KFkk6tNZ4onZbt/Gd5yo/vbaJNyx8XGd/axG5acnCdMqb9HdFB2DeJ5FB3oop9S8YT\nn5nvZYa3VHW5mJZeDMrxBs0Tfu8QVd1a2hdzKVyyMh1WrApcWxXapmLXqFQaw1nEFzSFPFdkeClp\nNbp+NN3OmiSmh9qwzFR7PmvziZLi36B0G8elPoP53ia2c40W4qnkfU6S1LFuIp5j+lNeh5SlVPI6\n/yzx/LY/0exNagCwHU2/iL2JuNf1oHnTPlk7E+10n0kUTHmMKECzPS1/Wa2liCU8JbVHY4G/86uG\n1hCdX5TyOFEK6n0KV6P+OfB94obV3oRnWloqW22yUOwQVe5LeYGI70maxp5Wv/gz8XJ5KLnqxp3J\nF2meoDyAKIWb3z7oaCLZeS+RbCv2oDCFqBqZ39t5D+JF+0Hanzhbn6jW/kDesJeTWNOf55PhnxE9\nSW9CPDSlaogqmzPJlTK7negBekJmfROIcyPbDmFrPEpUn812WpTvbeL8uDqZNk1ApOdrNiHR1s9a\n1ieUbs8o7cTmqVYut5AGmp83a9C84fk0rkKl4G4mkucv0vSYpz+tTXg+R5wHh1G6DU+SZV9LlB74\nNtGcQEulISG2O3td2YxoV6ot8l8M8qUvBuV4hKjilm8B0ezBPpRuw/Nu4guibCLyYOILhpY6cWqt\nnWjac2x34mXnBYqX+n+WuA5sQeHzZDrRNigUvyek8u9x/0p+Z9tt3TuZP9vBXWtsTpRG/6CF6Yo1\nL5E2DVOqJkSpz2D+dqZNpXyH0rrKfU6SFF9QXULUjDqbaCZoFPHcfzFNO/yBplXJs0qNa60G4FSi\nlsIUormnA4kaW7Mz67qaaJrm1mSescRzwiHEdqWlVNck2vucSpTyfI94dtiCaFJNyxBLeEpqj/yb\nUNp77FXEzWQF4oVp5cw8uxFJgylEpwo1REm1lchVR846n3hBvYQo2ZbtYbBcaUm+Y4mXtE+Jl8z/\nEu2K/p64MX5G3GyzycDNiLbS/k68cC8kXv43JUrYFHIdcQO+lngp3p/WfbNYyYeKrNlE4mYikYwc\nT3TscyKRAISoLn1DMu0vaF6K8ClypSr/SJRk+wfxrerbxLEeSOt6aIc4F+5Jlj+P2McnEsfm1DKX\ncRpRcvJ2Yhs/JErnbkrTjmveJaqHnkkkQe8iqh2fTvSoPYO2uzWJe1siYZx6iPim+olk/YOJ/f9f\ncvs+PV9PSrZhEVHCsdzPWqrYOfQ4kaj4BpFE/JimbbqOIJI8i1rcyrATkZTOuoVIVu5FVCm+jihJ\n+BMiQTMwM/0TxEP4bsR5mfYKfhrxjf39xDXhOSIZXEsksY+keduSLfkecRweJDrSmUU0GTCa5omt\n84lkeyPNk+PF3EycrxOJ479R8v+LtO0ZLH0xuIy4hl5GHPfTaf5iUMxtxDFflyixnjqOaC/3IaK0\n8Uyievg4Isk7j7g+7ka0b/xT4tw9kPicnUDpjuzaYi5xHTiTKMn4XaIadbaUYta3ie28nWhz83Wi\nxP5g4supfZPp0vP9CGL7PiaOTfplSP7+vItozuNsooTu/cQ94QwiifqXMrcpe4y6EdfZK8uY9yni\nM3lbEufyRDMJPyQ+K5fnTZtdT7mfwfuIbfkJcfxvIRLDaS+3hdpubu99TpLUfi2VvDySeN75NnE/\n7UbcA9J7f/5yii2r2Lj2lPpMO9g7ibifvEQUKBlJ0/bZG4imjY4l2io9hXgveJV4Lnmc2KariGfX\n/Oa3HkqmP4dIhOZ3sCRJUjNX0Lw0yteIHqnnE4mDXxJVoReR6wV3EPBXogTOR8QL8wPEjStf2kt7\nvm8QiZ7LKP5iX0vxXtohbqCvEjfI/Li2IZJN84hq9H8gvgnMX9ZqxE35aeLF/oNke4+haamWl2h+\nIx2RTH8LhduKLGQquaRXvtokruMyw0cm27RXZviEZHh+srI+ifHrxEv/x0SC45jMvKcn61pE854f\n8/df6gtEgmEOcR78l+adeZTjXKLk7PvEMX+VKD1UThuX+b5IJLTez4tn1yLTHk0kNz8mjuFp5DpB\nyppIeb20Q+zfSzPDziKq48wlStc9T7Q5uEreND2JJP+bxL5eRK7Dk3I+a1D8HCJZ1u3EvmmgaU+Y\naa+Z5SSq017MC/3kx3xiso4FxLE9jDi/sgnVzYimJeYly8jvSX5V4LfEufoJcZ49RCTf0lJ6tRT+\nfJAMz3ZYtTXxuXyXpseikJdoXRMHPYmH61nEsXqESCBeQdP93dqYDyNKMn5MtNeVlm4op5f25Ykv\nIwo1DbAx0c7j28my64kkWn4J2C8SpaffTaaZTvPr7UjKvxZB7jzI/zyl94AjiWPyCZHwyyY703Vl\nr0WbEqVB3kjmfZ1IWmZLQh9DnE+fJstJt6XQ/lye+OLnpWSZrxIJwGwTBYXuARCfx3syw3ZJ1ptN\n/BdyOJFUfIFcgvY5IomZbWqgUC/t5X4Ga4gXyseTdbxLvBDnXzsrdZ+TJEla6pxCPPh/QLzMTSES\nIVkTiRIb84kHxWyPzssBFxAP5/OIh/C1M9OsQnxb/V7y82eiNFm+dYmX4nnJss6jeRXETYkqX/OJ\nh9xySxlJWnJqiRflQ7EUeyn1+O1mW/UgEmzlJjz3IxLkhdry7KwupvLVk7u6zYhjnq321RWdTJTu\nbKlX72oq9KXX0uhWIsksSa1VqfdpSVKF3UZ8iz6YeIm4iXgBz29H6SQiQbknUaLgKuJind8b8u+I\nkhM7EqWx7iZKveSXtrqNqAa4NVGK63Gavuh3J0rg/ItoR2knIqGZ/6Ddlygl8FfiJvF1olRModIY\nkqqnlqYlzLIljBTqMeHZFt+naenFchKeEC8YhaqCdkYbEiXAvlztQDqJDYhnjAeJZ4OloeRaT6KE\n6PHVDqSEZSHhuQPxZUj/agciqUuq1Pu0JKmD9SMebocl/9cQ7VGdkDdNL6JazRHJ/ysRVYr2yZtm\nTaKq6ujk/7Ra3lfyptk6GZZWH9olmSe/Ef5vEC986c3gO0SbTvmlPk+ivE4LJC05PYnqkulPsXYN\nq6k7UUqw2E+xatSVVKzKZUerofS2d/ZSuavR9PxaEsdK1TWZeEZ4nLZ3NqTWWxYSnpJUSW15n5Yk\nLQEbEhfotIj9+sn/m2emu4F4+YAocdFA8+rpjxFtEkG0T/RugfW9S7R5BVE18X+Z8asky04byv0z\nUU0g35bJNAOQpPLVUbzNw2xbikubyZTe9nI7yJEkSVJOW96nJWmp1VlK0tQQvaP+h+gMBHKlLd/M\nTPsWuQ4Q1iA6s3g/M82befOvkcyT9VZmmux63k2WnT9NNgnxZt64lwusQ5IKOYLSVYk+WVKBVMHp\nWGpLkiSpktr6Pi1JS63OkvC8kGhTZFhLEyYaWxhfrPfm9szT0jqz1iR65V2VSJzmux24o5XLk7Ts\nWI7mvRgvS5blbZckSdUzBhibGdYLmAscTlQR74wq+T69ZvIjSZ3JbFp5De4MCc8LgN2Ihttfzxv+\nRvJ79by/s/+/QdyAVqJpKc/Vgf/mTfOFAuv9QmY5W2XGr5IsO3+aNTLTrJ6JNd+awNcKDIfY1rOK\njJMkSZIkdS5r0jkTnu15n85ac+WVV379vffeq3iQktROrxF985R9Ha5mwrOGuDjvAYykeZXwl4gL\n8Wiih3WIBOQIcg0vTwM+Tab5RzJsTeLbrbTn0QeIhOhXgEeSYVsnw+5P/r8f+BFx8U+L/I8mqpVO\ny1vOWUSHKJ/mTfNagdgXu/LKKxk8eHCx0ZIkSZKkTuqZZ55h/Pjx1Q6jkEq8T2et+d5773WJd9i9\n996b6667rtphtMg4K8s4K+fdd9/lb3+byvXX/4lDD53cbPzHH8/jk08e4YADRrHKKqss+QDzJNfh\ntWnlF0/VTHheBOxPXKA/Ild68j3gY6KY/W+JROTzwAvJ3/OAvyXTvg9cDvyGqGbwLvBroifVfyXT\nPENUIb8U+DZxY7gEuClZLsCdRFsnVxIX/1WBXyXTzUum+RvR9txkIvE5CDgFOKPURg4ePJghQ6yd\nKUmSJEmqmEq8TxfUFd5he/Xq1eljBOOsNOOsnDlz5nDPPTPp0WM5Nt54p2bj582bw9y5b7L55pvT\nr1+/KkTYftVMeB5JXITrMsMnED2iA5wDrABcTFQxf5D4huqjvOm/D3wG/D2Z9l/AwTRtl+QA4tuv\nO5P//wkclTe+gah+fjFRFX4BueRn6gNgZ+LG8ijwDpFonVTm9kqSJEmSVAmVep/ukjbaaKNqh1AW\n46ws46y8L3xhw2qH0GGqmfDsVuZ0Z1C6FOVC4Jjkp5j3gINaWM8sYFwL0zxJVAGQJEmSJKlaKvU+\nLUlLpc7QaZEkSZIkSZLUJZ17LnzwAfTtW+1IlDLhKUmSJEmSuozddtut2iGUxTgrqzPHee658Npr\nsPbacNppnTfOrE022bnaIXQYE56SJEmSlrjnn3+eDz/8sNphaBnXp08fBg4cWO0w1Eo333wzRxxx\nRLXDaJFxVpZxVt7TT9/Fzjv/sNphdAgTnpIkSZKWqOeff55BgwZVOwwJgOeee86kZxczceLEaodQ\nFuOsLOOsvDFjTmh5oi7KhKckSZKkJSot2XnllVcyePDgKkejZdUzzzzD+PHjLWncBQ0ZMqTaIZTF\nOCvLOCuvf//Nqx1ChzHhKUmSJKkqBg8e3KVeDCVJUtfQrdoBSJIkSZIkSVKlmPCUJEmSJEldxuWX\nX17tEMpinJVlnJX30ENXVjuEDmPCU5IkSZIkdRnTp0+vdghlMc7K6sxxDhoEm2wSvztznFmvvvp4\ntUPoMLbhKUmSJEmSuoyLLrqo2iGUxTgrqzPHec89+f913jiz9t77nGqH0GEs4SlJkiRJFfLQQw/x\n9a9/nQEDBrD88suzxhprsN1223H88ccvnmbkyJGMGjWqw2IYOXIkm266aYctX5Kkzs6EpyRJkiRV\nwC233MJ2223HvHnz+NWvfsVdd93F+eefz/bbb8/f//73xdPV1NRQU1PTobF09PIlSerMrNIuSZIk\nSRVwzjnnsMEGG3DHHXfQrVuubMm+++7Lr371q8X/NzY2mpCUJKkDWcJTkiRJkipg7ty59OvXr0my\ns1xnnHEGW2+9NauuuiorrbQSQ4cO5Y9//GPBaf/2t7+x7bbb0qdPH/r06cOWW25ZdNrUlClT6N27\nN0cccQSLFi1qdXxSZ7L77rtXO4SyGGdlGWflXX75+GqH0GFMeEqSJElSBWy33XY8+OCDHHvssTz8\n8MN8+umnZc9bX1/PEUccwTXXXMOUKVPYa6+9OOaYYzjzzDObTHfaaacxfvx4+vfvz5/+9CduuOEG\nDjnkEF555ZWiy540aRL77rsvp556Kpdccgndu3dv8zZKncFRRx1V7RDKYpyVZZyVN2zYN6sdQoex\nSrskSZKkzmv+fJgxo+PXs/HG0Lt3uxbxy1/+khkzZnDBBRdwwQUX0LNnT77yla8wbtw4jj76aHqX\nWP4VV1yx+O+GhgZ22GEHGhoaOP/88zn11FMBeOmllzjrrLMYP348f/7znxdPv9NOOxVcZmNjI8cc\ncwyXXnopf/7zn9l///3btX1SZzF69Ohqh1AW46ws46y8jTbquA70qs2EpyRJkqTOa8YMGDq049cz\nbRoMGdKuRXz+85/n3nvvZdq0adx9991MmzaNqVOncsopp/CHP/yBRx55hFVXXbXgvPfccw9nnXUW\njz76KB988MHi4TU1Nbz99tusttpq3HXXXTQ0NPC9732vxVgWLFjAHnvswX//+1/uuusuhg8f3q5t\nkySpKzHhKUmSJKnz2njjSEYuifVUyNChQxmaJGk/++wzTjrpJCZNmsQ555zD2Wef3Wz6hx9+mDFj\nxjBq1Cguu+wy+vfvT69evZgyZQo///nPWbBgAQBvv/02AP37928xhrfeeotZs2ax8847s+2221Zs\n2yRJze24I7z5Jqy+OtxzT7WjEZjwlCRJktSZ9e7d7pKX1dSjRw9OP/10Jk2axFNPPVVwmquvvppe\nvXpx880306tXr8XDr7/++ibTrbbaagDMmjWLtddeu+R6BwwYwLnnnsuee+7JXnvtxbXXXttk2VJX\ndsMNN7DnnntWO4wWGWdldeY4n3sOXnsN3n+/c8eZ9cQTt7LttgdXO4wOYadFkiRJklQBs2fPLjj8\n6aefBmCttdYqOL6mpobu3bs36d19wYIF/OUvf6GmpmbxsDFjxtC9e3d+97vflRXPV7/6VW6//Xbu\nvfdevva1rzF//vxyN0Xq1K666qpqh1AW46ws46y8//3v+pYn6qIs4SlJkiRJFTBmzBjWWWcdxo0b\nx0YbbURDQwOPPfYYv/nNb+jTpw/HHnvs4mkbGxsX/73bbrsxadIkDjjgAA4//HDmzp3Lr3/9a5Zf\nfvkm0w0YMIAf/ehHnHnmmSxYsID99tuPlVZaiaeffpq5c+cyceLEZssfNmwYd999N2PHjmXMmDHc\ncsst9O3bt+N3htSBrrnmmmqHUBbjrCzjrLyDD76s2iF0GBOekiRJklQBp556Kv/85z+ZNGkSs2fP\n5pNPPmGttdZi9OjRnHLKKWy00UZAlOjML7k5atQo/vjHP3L22Wez++67079/fw4//HBWW201vvWt\nbzVZxxlnnMHAgQO54IILGD9+PD169GDQoEEcc8wxi6fJLn/o0KHU1dWx8847s9NOO3HHHXfw+c9/\nvoP3hiRJ1WPCU5IkSZIqYJ999mGfffZpcbqpU6c2GzZhwgQmTJjQbPihhx7abNj48eMZP358q5b/\nxS9+kddff73F2CRJWhrYhqckSZIkSZKkpYYJT0mSJEmS1GUUKvncGRlnZRln5V199dHVDqHDWKVd\nkiRJkiR1GaNHj652CGUxzsrqzHEedxx88AH07Qtrrtl548waNGhUtUPoMCY8JUmSJElSl7H//vtX\nO4SyGGdldeY4jzsu/7/OG2fWkCF7VTuEDmOVdkmSJEmSJElLDROekiRJkiRJkpYaJjwlSZIkSVKX\ncd9991U7hLIYZ2UZZ+W9+OKD1Q6hw5jwlCRJkiRJXcY555xT7RDKYpyVZZyVN3XqhdUOocOY8JQk\nSZIkSV3G1VdfXe0QymKclWWclXfQQZdUO4QOY8JTkiRJkiR1Gb179652CGUxzsoyzsrr1avrxNpa\nJjwlSZIkqQKuu+46unXrxjXXXNNs3BZbbEG3bt244447mo3bcMMNGTJkCAC1tbWMGzeu2TSXXXYZ\n3bt3Z88992ThwoUAdOvWrcnPyiuvzKhRo7j11lubzFtsmW1x//33c8YZZ/D+++9XZHmStDR49ll4\n6qn4rc7BhKckSZIkVcDIkSOpqamhrq6uyfB33nmHxx9/nBVXXLHZuFmzZvHiiy+y4447Lh5WU1PT\nZJpf/epXHHHEERx00EFcf/319OrVa/G4ffbZhwcffJD777+fiy66iDfeeINx48Y1SXrW1NQ0W2Zb\nmfCUpOZ22gm+9KX4rc7BhKckSZIkVcCqq67Kpptu2iyp+e9//5uePXty2GGHMXXq1Cbj0mlHjRpV\ncJk/+tGPOOmkkzj22GOZPHky3bo1fYVbffXV2Wqrrdhmm2048MADueWWW2hsbOS8885bPE1jY2P7\nNy6jI5YpleuEE06odghlMc7KMs7Ku+mmidUOocOY8JQkSZKkChk5ciTPPvssb7755uJhdXV1bLXV\nVuy6665MmzaNjz76qMm4Hj16sMMOOzRZTmNjI9/5znf45S9/ycSJE5k0aVJZ619//fXp168fL7/8\ncqvivuuuu9hjjz1YZ511WGGFFRg4cCBHHnkkc+fOXTzNxIkTOfHEEwFYb731Flelv/feexdPc801\n17Dtttuy4oor0qdPH8aOHctjjz3WZF0TJkygT58+zJw5k1133ZU+ffqw7rrrcvzxxy+urp/65JNP\n+OlPf8rgwYNZYYUV6NevHzvuuCMPPPAAADvttBODBw9utj2NjY1suOGGfO1rX2vVflDXsO6661Y7\nhLIYZ2UZZ+WtvPLa1Q6hw5jwlCRJkqQKSaum55fknDp1KiNGjGD77benpqamSYJw6tSpbLnllvTp\n0weI6ucLFy7kgAMO4JJLLuH888/ntNNOK3v97777LnPnzmW11VZrVdwzZ85km2224aKLLuLOO+/k\ntNNO46GHHmLYsGF89tlnABx++OEcffTRAEyZMoUHH3yQBx98kC233BKAs846iwMOOIAvfelL/OMf\n/+Avf/kLH374IcOHD+eZZ55psr5PP/2UcePGsfPOO3PjjTdy2GGHMWnSJM4+++zF03z22Wfssssu\n/OxnP2P33XfnhhtuYPLkyWy33XbMmjULgGOPPZZnn32Wu+++u8nyb7vtNl588cXF8Wrp0lWOq3FW\nlnFW3vDhh1c7hA7To9oBSJIkSVIx8z+dz4w5Mzp8PRv325jePdvfW+3w4cPp1q0bdXV17Lfffsyd\nO5ennnqK3/zmN3zuc59jyJAhTJ06lV122YVXXnmF+vp69t1338XzNzY2cueddwLw4x//mKOOOqrk\n+hoaGli0aBENDQ3MnDmT4447jsbGRg488MBWxX3kkUc2iWHbbbdlxIgR1NbWcttttzFu3DjWXntt\n1llnHQC23HLLJqWYZs2axemnn87RRx/Nb3/728XDd955ZwYOHMgZZ5zB1VdfvXj4woULOfPMM9l7\n772BqNL/6KOP8re//Y1TTz0VgKuuuoq6ujouu+wyDjvssMXz7rbbbov/HjduHOuttx4XXnghO+U1\nnnfhhRey4YYbMnbs2FbtB0nS0sGEpyRJkqROa8acGQy9ZGiHr2faEdMYsuaQdi9nlVVWYYsttljc\nNue///1vunfvzvbbbw/AiBEjuOeee4DC7XfW1NSwxRZb8M4773DhhRcybtw4ttpqq6Lru/jii7n4\n4js5dIcAACAASURBVIsX/7/yyitz5plnNklgluOtt97itNNO45ZbbmH27Nk0NDQsHjdjxowWe3m/\n4447WLRoEQcddNDiEqEAyy23HDvssEOzdk1ramqaLXPTTTddvG8gSmmusMIKTZKdWTU1NRx11FGc\neOKJzJo1i3XWWYeZM2dyxx138Jvf/KacTZckLYVMeEqSJEnqtDbutzHTjpi2RNZTKSNHjuTcc89l\n9uzZTJ06lS9/+cv07h2lR3fYYQfOPfdcPvjgA6ZOnUrPnj0ZPnz44nkbGxvp378/119/PaNGjWL0\n6NHcfvvtbLPNNgXX9Y1vfIMTTjiBmpoa+vTpwwYbbNDqHtkbGhoYPXo0b7zxBqeeeiqbbropn/vc\n51i0aBHbbLMNCxYsaHEZaZulX/nKVwqO7969e5P/P/e5zzXpbR4iOfrxxx8v/v/tt99mrbXWanHd\n3/zmNzn99NP5/e9/z89//nMuuugievfuXTJRqq5txowZbLxx5T6zHcU4K8s4K+/NN59nxRX7VTuM\nDmHCU5IkSVKn1btn74qUvFySdtxxR84991zq6ur497//3aTjnGHDhtHY2Mi9995LXV1dk2Rovtra\nWurq6hg1ahRjxozh9ttvZ9ttt2023WqrrcaQIe3bP08++SSPP/44f/rTnzjooIMWD3/hhRfKXka/\nfvHCfN111zFgwIAWpy+nl/fVVluN+++/n8bGxpJJ3L59+3LwwQdz2WWXccIJJ3DFFVdwwAEH0Ldv\n37LjV9dy4okncuONN1Y7jBYZZ2UZZ+XdfPMZHHvs7dUOo0PYaZEkSZIkVdCwYcPo3r071157LU89\n9RQjR45cPG6llVZiiy22YPLkybz88stNqrNnDRgwgLq6Ovr168fYsWO5//77OyTeNJmYLXH5hz/8\nodm0yy23HADz589vMnzs2LH06NGDF154gSFDhhT8KbTOUnbddVcWLFjA5MmTW5z2mGOOYc6cOey1\n1168//77LbZ9qq7twgsvrHYIZTHOyurMcd59Nzz5ZPzuzHFm7bXXL6sdQoexhKckSZIkVVDfvn0Z\nOnQoU6ZMoUePHovb70yNGDGCSZMmAZRMeAKsu+66i0t6jh07lltvvZVhw4a1OqbZs2dz7bXXNhu+\n3nrrsfnmm7PBBhtw8skn09jYyCqrrMJNN93Ev/71r2bTb7bZZgCcd955HHzwwfTs2ZONN96YAQMG\n8NOf/pQf//jHvPjii4wZM4ZVVlmFN954g0ceeYQVV1yRiRMnLl5OOSU8999/f6644gqOPPJInn32\nWUaOHElDQwMPPfQQm2yyCd/4xjcWTzto0CBGjx7NHXfcwfDhw9l0001bvY/UdeR3mNWZGWdldeY4\nN9oo/7/OG2fWKqv0r3YIHcYSnpIkSZJUYWkic8stt2TFFVdsMm7EiBFAlJbMJkMLlXxcZ511qKur\nY/XVV2fXXXflvvvua1UsNTU1TJ8+nX333bfZz0UXXUSPHj246aabGDRoEN/+9rc54IADmDNnTsGE\n54gRIzjllFO46aabGD58OFtvvTXTp08H4OSTT+baa6/lueeeY8KECYwdO5aTTz6ZWbNmLd7mNJ5C\n25kd3r17d2699VZOOeUUpkyZwp577skhhxzC/fffT21tbbP599tvPwBLd0qSaF1r1mqNIcC0adOm\ntbtNHUmSJGlpMn36dIYOHYrPyqqkvffem4cffpj6+vpmnSQVUs55mE4DDAWmVzTgzsd3WGkZMWfO\nHCZNup5VV92rYKdF8+bNYe7c6/nBD/Za3EZztbT1OmwJT0mSJElSl7Rw4UIeeOABzjvvPG644QZO\nOOGEspKd6trOPvvsaodQFuOsLOOsvHvuOb/aIXQY2/CUJEmSJHVJr7/+Ottvvz0rrbQSRx55JEcf\nfXS1Q9ISkO00q7MyzsoyzspbuHBBtUPoMCY8JUmSJEldUm1tLQ0NDdUOQ0vYGWecUe0QymKclWWc\nlTd27EnVDqHDWKVdkiRJkiRJ0lLDEp6SJEmSJElSG517LnzwAfTtC8cdV+1oBJbwlCRJkiRJXcic\nOXOqHUJZjLOyOnOc554LZ5wRvztznFnz5s2tdggdxoSnJEmSJEnqMg477LBqh1AW46ws46y8a645\nttohdBgTnpIkSZIkqcuYOHFitUMoi3FWlnFW3pgxJ1Q7hA5jG56SJEmSquKZZ56pdghahnn+dV1D\nhgypdghlMc7KMs7K699/82qH0GFMeEqSJElaovr06QPA+PHjqxyJlDsfJUlLDxOekiRJkpaogQMH\n8txzz/Hhhx9WOxQt4/r06cPAgQOrHYYkqcJMeEqSJEla4kwySWqryy+/nG9+85vVDqNFxllZxll5\nDz10JTvt9P1qh9Eh7LRIkiRJkiR1GdOnT692CGUxzsrqzHEOGgSbbBK/O3OcWa+++ni1Q+gwlvCU\nJEmSJEldxkUXXVTtEMpinJXVmeO85578/zpvnFl7731OtUPoMCY8lzXPPw+dqa2kPn3A6kySJEmS\nJEmqEBOeHe2ZZ6odQc6rr8Iee1Q7iub++U/o37/aUai1TFZLkiRJkqROyIRnRxs/vtoRNNdZEoxp\nArYzJmFVnueeM+kpSZIkSZI6FROeHe3KK2Hw4GpHkdOZSuUNGRIJs85UxV7leeaZSOZ77CRJkiQt\nYbvvvjs33nhjtcNokXFWlnFW3uWXj+fYY2+vdhgdwoRnRxs8OBJ7KqyzJF8lSZIkSV3CUUcdVe0Q\nymKclWWclTds2DerHUKH6VbtACRJkiRJkso1evToaodQFuOsLOOsvI02GlXtEDqMCU9JkiRJkiRJ\nSw0TnpIkSZIkSVIb7bgjfPGL8VudgwlPSZIkSZLUZdxwww3VDqEsxllZnTnO556Dp5+O3505zqwn\nnri12iF0GBOekiRJkiSpy7jqqquqHUJZjLOyjLPy/ve/66sdQocx4SlJkiRJkrqMa665ptohlMU4\nK8s4K+/ggy+rdggdxoSnJEmSJEmSpKWGCU9JkiRJkiRJSw0TnpIkSZIkSZKWGiY8JUmSJElSl3Ho\noYdWO4SyGGdlGWflXX310dUOocP0qHYAkiRJkiRJ5Ro9enS1QyiLcVZWZ47zuOPggw+gb19Yc83O\nG2fWoEGjqh1ChzHhKUmSJEmSuoz999+/2iGUxTgrqzPHedxx+f913jizhgzZq9ohdBirtEuSJEmS\nJElaapjwlCRJkiRJkrTUMOEpSZIkSZK6jPvuu6/aIZTFOCvLOCvvxRcfrHYIHcaEpyRJkiRJ6jLO\nOeecaodQFuOsLOOsvKlTL6x2CB3GhKckSZIkSeoyrr766mqHUBbjrCzjrLyDDrqk2iF0GBOekiRJ\nkiSpy+jdu3e1QyiLcVaWcVZer15dJ9bW6lHtACRJkiRJkqSu6tln4bPPoEcP2GijakcjMOEpSZIk\nSZIktdlOO8Frr8Haa8Orr1Y7GoFV2iVJkiRJUhdywgknVDuEshhnZRln5d1008Rqh9BhTHhKkiRJ\nkqQuY9111612CGUxzsoyzspbeeW1qx1ChzHhKUmSJEmSuoyjjz662iGUxTgryzgrb/jww6sdQocx\n4SlJkiRJkiRpqWHCU5IkSZIkSdJSw4SnJEmSJEnqMmbMmFHtEMpinJVlnJX35pvPVzuEDtOj2gFI\n6sKeeab9y+jTBwYObP9yJEn6/+zdf7xc913f+XfIxayNr2pvvA/HSKiFzZUcu5BwXXg01C61vVx7\nQ3sT5C5BreyNlKQFYsXOBcmPtlsikS6PSIDiJDJQ14Js8SIp3RrFNiE1a7mhahNvYhni4itfg8kP\nqYlAhlRyFWpEsn+c0SOjsWwdyd+j78zo+Xw87uNoZr73nNfIihR9dM4cAM4J69evz/33318745R0\nlqWzvAcf3Jjbbvt47YxOGHgCp29ystmuWlVmfwsLhp4AAEArW7durZ3Qis6yhrnz4YeTY8eSiYnk\n/POHt3PQihXvq53QGQNP4PRNTTVDyiNHXt5+5ueboenL3Q8AAHDOWLp0ae2EVnSWNcydy5f3Pxre\nzkEXX7ykdkJnDDyBM+OMTAAAAGAIGXgC4+Hpp4fvTNGSn09a6v35zFQAAADGnIEnUN/LvfnR/v3J\nm95UpqW0j340WfIyLxMo/f58ZioAACNs06ZNueOOO2pnnJLOsnSWt3v3BzM7+zO1Mzph4AnUU/rm\nRyWGi6UcH1KWHFS+3PfnM1MBABgDR48erZ3Qis6ydJb3/PNfrZ3QGQNPoJ5SNz9Khu9S7enpcu8t\nGb73BwAAlWzcuLF2Qis6y9JZ3o03jsaZqGfCwBOoa5yHeMP63l7uRwicCwyYAQAARpaBJ8C5ovRH\nCIw7n3UKAAC0sGVLcvhwsmhRMjdXu4bEwBPg3FHyIwTGmc86BQAYaocOHcoll1xSO+OUdJY1zJ1b\ntiQHDiSLFye33DK8nYOee+7ZXHjhaLSeLgNPgHOJMxYBABhxa9asyf33318745R0lqWzvJ07b8tt\nt328dkYnvql2AAAAAEBbGzZsqJ3Qis6ydJZ3ww3raid0xsATAAAAGBnT09O1E1rRWZbO8pYseV3t\nhM4YeAIAAAAAY8PAEwAAAAAYGwaep+8nkvxRkq8m+UySq+vmAAAAwLlj27ZttRNa0VmWzvIeffTe\n2gmdMfA8PW9J8v4k703y+iT/IclvJfn2mlEAAABwrti7d2/thFZ0ljXMncuWJVdc0WyHuXPQ/v2f\nrZ3QmYnaASNmLsk9SX6l9/jdSW5I8uNJ/mmtKAAAADhX3HXXXbUTWtFZ1jB37t7d/2h4OwfddNPm\n2gmdcYZne+clmU7y0MDzDyX5/rOfAwAAAAAMcoZne5ckeWWSgwPP/3GSV7/YN83/yXzypS6zTs/k\neZOZetXUy97P088+nSPPHylQNN5K/XwPo1K/Bsb512TJ//7D+P7G1lfmM/k/JuP5v1wAAIDxZ+DZ\nsVX3rUo+VbviRB/90Y9myaIlZ/z9+w/vz5t2vKlg0Xh7uT/fw6j0r4Fx/jVZ4r//ML+/sfWu5KNf\n/kSWDNE/WAEAZ9/8n8zXTgDgDBh4tncoyV8muXTg+UvzEudwfs/vfU8m/2jyhOduePMNufGHbywe\neCrHhyalBifjOMgrqfTP9zAqNagcx1+TXfz3H6b3N872P/6JvOnTc3nTp+eST9euAQDOmid6X/3+\nvEYIpzI7O5v777+/dsYp6SxLZ3nbtq3Kbbd9vHZGJww823s+yWNJZpJ8tO/5H0zyGy/2Tff84j2Z\nnp7uOK2d6cums3DrwlBdhjzOSv58D6MSvwbG+ddk6f/+w/b+xtn0l5KFDyZH/s29yWtfWzsHAKho\n/rPzWXXjqtoZDLj11ltrJ7Sisyyd5V199dtqJ3TGwPP0bEnya0k+k+ZC9X+UZEmSX64ZdToMTM4u\nP9+nNs4/R+P83sbd1J8muei1yWXD8Q9WAEAlPt5mKM3MzNROaEVnWTrLW7782toJnTHwPD0fSfKq\nJD+d5LI0Fzy8MckXa0YBAAAAAA0Dz9P3S70vAAAAAM5x112XHDyYXHppsnt37RqS5JtqBwAAAAC0\ntWvXrtoJregsa5g7FxaSJ59stsPcOeiJJz5WO6EzBp4AAADAyNi+fXvthFZ0lqWzvMcfv692QmcM\nPAEAAICRsXPnztoJregsS2d5t9xyT+2Ezhh4AgAAAABjw8ATAAAAABgbBp4AAAAAwNgw8AQAAABG\nxurVq2sntKKzLJ3l7dixtnZCZyZqBwAAAAC0NTMzUzuhFZ1lDXPn3Fxy+HCyaFFy2WXD2zlo2bJr\nayd0xsATAAAAGBkrV66sndCKzrKGuXNurv/R8HYOmp5eUTuhMy5pBwAAAADGhoEnAAAAADA2DDwB\nAACAkbFnz57aCa3oLEtnec8886naCZ0x8AQAAABGxubNm2sntKKzLJ3lPfLI1toJnTHwBAAAAEbG\njh07aie0orMsneXdfPPdtRM6Y+AJAAAAjIwLLrigdkIrOsvSWd55541O6+maqB0AAAAAAKPqqaeS\nY8eSiYlk+fLaNSQGngAAAABwxq6/PjlwIFm8ONm/v3YNiUvaAQAAgBGybt262gmt6CxLZ3kPPLCh\ndkJnDDwBAACAkbF06dLaCa3oLEtneRddtLh2Qmdc0g4AJzM/3/0xJieTqanujwMAMEbWrl1bO6EV\nnWXpLO+aa95RO6EzBp4A0G9ystmuWnV2jrewYOgJAABQkIEnAPSbmmqGkEeOdHuc+flmqNr1cQAA\nAM4xBp4AMMgZlwAAQ2vfvn25/PLLa2ecks6ydJZ38ODTufDCS2pndMJNiwAAAICRsX79+toJregs\nS2d5Dz64sXZCZ5zhCQAAAIyMrVu31k5oRWdZw9z58MPJsWPJxERy/vnD2zloxYr31U7ojIEnAAAA\nMDKWLl1aO6EVnWUNc+fy5f2Phrdz0MUXL6md0BmXtAMAAAAAY8PAEwAAAAAYGwaeAAAAwMjYtGlT\n7YRWdJals7zduz9YO6EzBp4AAADAyDh69GjthFZ0lqWzvOef/2rthM4YeAIAAAAjY+PGjbUTWtFZ\nls7ybrzxjtoJnTHwBAAAAADGxkTtAAAAAAAYVVu2JIcPJ4sWJXNztWtInOEJAAAAjJBDhw7VTmhF\nZ1nD3LllS7JxY7Md5s5Bzz33bO2Ezhh4AgAAACNjzZo1tRNa0VmWzvJ27rytdkJnDDwBAACAkbFh\nw4baCa3oLEtneTfcsK52QmcMPAEAAICRMT09XTuhFZ1l6SxvyZLX1U7ojIEnAAAAADA2DDwBAAAA\ngLFh4AkAAACMjG3bttVOaEVnWTrLe/TRe2sndMbAEwAAABgZe/furZ3Qis6yhrlz2bLkiiua7TB3\nDtq//7O1EzozUTsAAAAAoK277rqrdkIrOssa5s7du/sfDW/noJtu2lw7oTPO8AQAAAAAxoaBJwAA\nAAAwNlzSDgA1zc93f4zJyWRqqvvjAAAADAEDTwCoYXKy2a5adXaOt7Bg6AkAjIXZ2dncf//9tTNO\nSWdZOsvbtm1Vbrvt47UzOmHgCQA1TE01Q8gjR7o9zvx8M1Tt+jgAAGfJrbfeWjuhFZ1l6Szv6qvf\nVjuhMwaeAFCLMy4BAE7bzMxM7YRWdJals7zly6+tndAZNy0CAAAAAMaGgScAAAAAnKHrrkuuvLLZ\nMhwMPAEAAICRsWvXrtoJregsa5g7FxaSJ59stsPcOeiJJz5WO6EzBp4AAADAyNi+fXvthFZ0lqWz\nvMcfv692QmcMPAEAAICRsXPnztoJregsS2d5t9xyT+2Ezhh4AgAAAABjw8ATAAAAABgbBp4AAAAA\nwNgw8AQAAABGxurVq2sntKKzLJ3l7dixtnZCZyZqBwAAAAC0NTMzUzuhFZ1lDXPn3Fxy+HCyaFFy\n2WXD2zlo2bJrayd0xsATAAAAGBkrV66sndCKzrKGuXNurv/R8HYOmp5eUTuhMy5pBwAAAADGhoEn\nAAAAADA2DDwBAACAkbFnz57aCa3oLEtnec8886naCZ0x8AQAAABGxubNm2sntKKzLJ3lPfLI1toJ\nnTHwBAAAAEbGjh07aie0orMsneXdfPPdtRM6Y+AJAAAAjIwLLrigdkIrOsvSWd55541O6+maqB0A\nAAAAAKPqqaeSY8eSiYlk+fLaNSQGngAAAABwxq6/PjlwIFm8ONm/v3YNiUvaAQAAgBGybt262gmt\n6CxLZ3kPPLChdkJnDDwBAACAkbF06dLaCa3oLEtneRddtLh2Qmdc0g4A54L5+e6PMTmZTE11fxwA\n4Jy2du3a2gmt6CxLZ3nXXPOO2gmdMfAEgHE2OdlsV606O8dbWDD0BAAAqjLwBIBxNjXVDCGPHOn2\nOPPzzVC16+MAAACcgoEnAIw7Z1wCAGNk3759ufzyy2tnnJLOsnSWd/Dg07nwwktqZ3TCTYsAAACA\nkbF+/fraCa3oLEtneQ8+uLF2Qmec4QkAAACMjK1bt9ZOaEVnWcPc+fDDybFjycREcv75w9s5aMWK\n99VO6IyBJwAAADAyli5dWjuhFZ1lDXPn8uX9j4a3c9DFFy+pndAZl7QDAAAAAGPDwBMAAAAAGBsG\nngAAAMDI2LRpU+2EVnSWpbO83bs/WDuhMwaeAAAAwMg4evRo7YRWdJals7znn/9q7YTOGHgCAAAA\nI2Pjxo21E1rRWZbO8m688Y7aCZ0x8AQAAAAAxsZE7QAAAAAAGFVbtiSHDyeLFiVzc7VrSJzhCQAA\nAIyQQ4cO1U5oRWdZw9y5ZUuycWOzHebOQc8992zthM4YeAIAAAAjY82aNbUTWtFZls7ydu68rXZC\nZww8AQAAgJGxYcOG2gmt6CxLZ3k33LCudkJnDDwBAACAkTE9PV07oRWdZeksb8mS19VO6IyBJwAA\nAAAwNgw8AQAAAICxYeAJAAAAjIxt27bVTmhFZ1k6y3v00XtrJ3TGwBMAAAAYGXv37q2d0IrOsoa5\nc9my5Iormu0wdw7av/+ztRM6M1E7AAAAAKCtu+66q3ZCKzrLGubO3bv7Hw1v56CbbtpcO6EzzvAE\nAAAAAMaGgScAAAAAMDZc0g4AlDM/3/0xJieTqanujwMAAIwkA08A4OWbnGy2q1adneMtLBh6AsA5\nanZ2Nvfff3/tjFPSWZbO8rZtW5Xbbvt47YxOGHgCAC/f1FQzhDxypNvjzM83Q9WujwMADK1bb721\ndkIrOsvSWd7VV7+tdkJnDDwBgDKccQkAnAUzMzO1E1rRWZbO8pYvv7Z2QmfctAgAAAAAGBsGngAA\nAABwhq67LrnyymbLcDDwBAAAAEbGrl27aie0orOsYe5cWEiefLLZDnPnoCee+FjthM4YeAIAAAAj\nY/v27bUTWtFZls7yHn/8vtoJnTHwBAAAAEbGzp07aye0orMsneXdcss9tRM6Y+AJAAAAo+dvJ3kg\nyYEkX0vypoHXP9x7vv/rP53FPoBqDDwBAABg9FyQ5PEk7+w9/vrA619P8ltJXt339cazVgdQ0UTt\nAAAAAOC0fbz39WJekeT5JH98dnIAhoczPAEAAGD8fD3J30lyMMlTSe5O8j/VDCpl9erVtRNa0VmW\nzvJ27FhbO6EztQaefy3JtiTPJDma5A+SbEjyzQPrlqb5TJLnkvxJkg+cZM13JflEbz/7k/zzkxzv\nB5I8luSrSf4wyT8+yZqbkjyZ5M+T/H6SN59kzU8k+aPefj6T5OoXe4MAAABQ0W8l+QdJrk3yk0m+\nN8nuJOfVjCphZmamdkIrOssa5s65ueQ972m2w9w5aNmya2sndKbWJe3L05xe/4/SDDu/K8m/SvKt\nSdb11rwyyW+m+deov5XkkiT/V+/73tVbsyjJbyd5OMmP9/b74ST/LcmW3prvSPKxJP8yzW/2Vyf5\nxTQD1Pt6a96QZEeSf5ZkV5IVST7SW/v/9da8Jcn7e8f5j0l+LM0fIFck+eLL++kAAACAoj7S9+Mn\n05y087kkP5TkN2oElbJy5craCa3oLGuYO+fm+h8Nb+eg6ekVtRM6U+sMz3+XZE2S/zfNb7gPJPn5\nNIPG42aSvDbJqiS/l2ao+ZNJ3pHkwt6af5jmX6femuY38N9I8rNJ+n+p/VjvGHNpTuPfluRXkvxU\n35rbkzyUZHOShSTv6x3v9r41c0nu6X3vU0nenWbQ+eOn/e4BAADg7Ppyki8kec1LLXrjG9+Y2dnZ\nE77e8IY3ZNeuXSese+ihhzI7O/uC73/nO9+Zbdu2nfDc3r17Mzs7m0OHDp3w/Hve855s2rTphOe+\n8IUvZHZ2Nvv27Tvh+Q996ENZt27dCc8dPXo0s7Oz2bNnzwnPb9++/aSXFb/lLW/xPrwP72P79qxd\n+8JL2e+++y353d898X088sgjZ/V9vPWtb81rXvOaE37/ede73pUz8Yoz+q5u/Is0Q87v6z3+mSR/\nL8n39K25OMmzaU7J/0SSf51kMskP9635njSXr39Hks8n+Z3e43f3rfnhJDuTnJ/kL3vrtqS5ZP64\ndye5Lc3l9+elOWv07yf5aN+aO5O8Ps3nogyaTvLYY489lunp6Zd+5wBAO3v3JlddlTz2WOLPVwA6\ntnfv3lx11VVJclWSvZVzXsrX0nws2/0vseaSNCftvCPJvSd53d9h4Rxx6NChvP/99+VVr1qRCy+8\n5AWvP/fcoTz77H1597tX5JJLXvj62XSmvw8Py13a/+ckt+bEMzNfneZy9n5/luYuc6/uW/PMwJqD\nfa99PsmlJ9nPwTTv/ZLej092rOPPp7fulSdZ88d9awCAs2V+vrt9T04mU1Pd7R8AyvjWJP1/YH1n\nmhNynk3yp0k2Jvl/0pzZ+dfSXA35Jxnxy9mTZM+ePbn66uG/pYbOsnSW98wzn8p3f/ffrZ3RidID\nzw1JfvoUa/5GTpzIfluSj6f5fJFfGVh7qjNQv346cQDAiJucbLarVnV7nIUFQ08Aht3xmxAlzd+N\nj9/H4sNpbrj715PcnOSiJF/qrf3f0ly9ONI2b948EgMlnWXpLO+RR7YaeLb0oSS/foo1n+/78bcl\neSTNTYD+0cC6L+Ubl7cfd3Gay8u/3Hv85bzwDMtL+157qTXHkhzqW3PpSdYc38ehNJe+n2zNl/IS\nbr/99lx00UUnPLdy5cqh/rBdABhaU1PNMPLIkW72Pz/fDFO72j8AQ2v79u3Zvn37Cc995StfqVTT\nyr/PS9+X48az1HHW7dixo3ZCKzrL0lnezTffXTuhM6UHns/2vtpYnGbY+ekkL/zU1+STae6a3n9J\n+kyS/57mMzmPr/nZJN+c5C/61hzINwarn0zzWaD9ZnrH/cu+NTM58TM8Z9IMYpPmMvrHes/1f4bn\nD+YUlwPceeedPv8EAEpy5iUAHTjZiSl9nx3HELngggtqJ7Sisyyd5Z133ui0nq5ad2lfnOZfoz6f\nZF2aoearc+KZmA+lufP6vWk+h+T6JD+X5O4kz/XW/HqaAeiHk1yZ5mZE/yTfOJU/SX45yV9NKVki\nwAAAIABJREFU8gtp7vq+pvf1831rPpBmmLk+yeVJ7ugd786+NVuSvD3NcPa1Sd6fZElv/wAAAACc\ng556Kvn932+2DIdaNy36wTQ3KvrOJPv7nv96mpsDJc1d5n4oyS+mOdPyq2mGn+v61h/u7euuJJ9J\n88HMv5BmGHnc55K8sffcO9Oc/bk2J56Z+ckkP5rmTvHvTfIHSX4kzVmgx30kyavSfEbpZUme6O33\ni6f1zgEAAAAYG9dfnxw4kCxenOzff+r1dK/WGZ4f7h37lb3tN/U97vfFNJejf2uaO6Xfnm9cun7c\nf07yA0nOT3Pm6HtPcrzfSXP7+v8hzaD1ZB9S8G/TnLn5LWnOFt11kjW/lOQ7evv53iR7XvQdAgAA\nAMWtW7fu1IuGgM6ydJb3wAMbaid0ptbAEwAAAOC0LV26tHZCKzrL0lneRRctrp3QGQNPAAAAYGSs\nXbu2dkIrOsvSWd4117yjdkJnDDwBAAAAgLFh4AkAAAAAjI1ad2kHABhe8/Pl9zk5mUxNld8vAJxj\n9u3bl8svv7x2xinpLEtneQcPPp0LL7ykdkYnDDwBAI6bnGy2q1Z1s/+FBUNPAHiZ1q9fn/vvv792\nxinpLEtneQ8+uDG33fbx2hmdMPAEADhuaqoZSh45Una/8/PNELX0fgHgHLR169baCa3oLGuYOx9+\nODl2LJmYSM4/f3g7B61Y8b7aCZ0x8AQA6OcMTAAYakuXLq2d0IrOsoa5c/ny/kfD2zno4ouX1E7o\njJsWAQAAAABjw8ATAAAAABgbBp4AAADAyNi0aVPthFZ0lqWzvN27P1g7oTMGngAAAMDIOHr0aO2E\nVnSWpbO855//au2Ezhh4AgAAACNj48aNtRNa0VmWzvJuvPGO2gmdMfAEAAAAAMbGRO0AAIBzxvx8\n+X1OTiZTU+X3CwBAK1u2JIcPJ4sWJXNztWtIDDwBALo3OdlsV63qZv8LC4aeAJwzDh06lEsuuaR2\nxinpLGuYO7dsSQ4cSBYvTm65ZXg7Bz333LO58MLRaD1dBp4AAF2bmmqGkkeOlN3v/HwzRC29XwAY\nYmvWrMn9999fO+OUdJals7ydO2/Lbbd9vHZGJww8AQDOBmdgAkARGzZsqJ3Qis6ydJZ3ww3raid0\nxk2LAAAAgJExPT1dO6EVnWXpLG/JktfVTuiMgScAAAAAMDYMPAEAAACAsWHgCQAAAIyMbdu21U5o\nRWdZOst79NF7ayd0xsATAAAAGBl79+6tndCKzrKGuXPZsuSKK5rtMHcO2r//s7UTOuMu7QAAAMDI\nuOuuu2ontKKzrGHu3L27/9Hwdg666abNtRM6Y+AJADDq5ufL7m9yMpmaKrtPAAA4Sww8AQBG1eRk\ns121qvy+FxYMPQEAGEkGngAAo2pqqhlMHjlSbp/z880AteQ+AQDgLDLwBAAYZc7CBOAcMzs7m/vv\nv792xinpLEtnedu2rcptt328dkYn3KUdAAAAGBm33npr7YRWdJals7yrr35b7YTOGHgCAAAAI2Nm\nZqZ2Qis6y9JZ3vLl19ZO6IyBJwAAAAAwNgw8AQAAAOAMXXddcuWVzZbhYOAJAAAAjIxdu3bVTmhF\nZ1nD3LmwkDz5ZLMd5s5BTzzxsdoJnTHwBAAAAEbG9u3baye0orMsneU9/vh9tRM6Y+AJAAAAjIyd\nO3fWTmhFZ1k6y7vllntqJ3RmonYAAABDaH6+/D4nJ5OpqfL7BQCAPgaeAAB8w+Rks121qpv9LywY\negIA0CkDTwAAvmFqqhlKHjlSdr/z880QtfR+AQBggIEnAAAncgYmAENs9erV+dVf/dXaGaeksyyd\n5e3YsTZvf/vo3GTpdBh4AgAAACNjZmamdkIrOssa5s65ueTw4WTRouSyy4a3c9CyZdfWTuiMgScA\nAAAwMlauXFk7oRWdZQ1z59xc/6Ph7Rw0Pb2idkJnvql2AAAAAABAKQaeAAAAAMDYMPAEAAAARsae\nPXtqJ7Sisyyd5T3zzKdqJ3TGwBMAAAAYGZs3b66d0IrOsnSW98gjW2sndMZNiwAAOHvm58vvc3Iy\nmZoqv18AhtKOHTtqJ7Sisyyd5d188921Ezpj4AkAQPcmJ5vtqlXd7H9hwdAT4BxxwQUX1E5oRWdZ\nOss777zRaT1dBp4AAHRvaqoZSh45Una/8/PNELX0fgEAWnrqqeTYsWRiIlm+vHYNiYEnAABnizMw\nAYAxdP31yYEDyeLFyf79tWtI3LQIAAAAGCHr1q2rndCKzrJ0lvfAAxtqJ3TGwBMAAAAYGUuXLq2d\n0IrOsnSWd9FFi2sndMbAEwAAABgZa9eurZ3Qis6ydJZ3zTXvqJ3QGQNPAAAAAGBsGHgCAAAAAGPD\nwBMAAAAYGfv27aud0IrOsnSWd/Dg07UTOmPgCQAAAIyM9evX105oRWdZOst78MGNtRM6M1E7AAAA\nAKCtrVu31k5oRWdZw9z58MPJsWPJxERy/vnD2zloxYr31U7ojIEnAAAAMDKWLl1aO6EVnWUNc+fy\n5f2Phrdz0MUXL6md0BmXtAMAAAAAY8PAEwAAAAAYGwaeAAAAwMjYtGlT7YRWdJals7zduz9YO6Ez\nBp4AAADAyDh69GjthFZ0lqWzvOef/2rthM4YeAIAAAAjY+PGjbUTWtFZls7ybrzxjtoJnTHwBAAA\nAADGxkTtAAAAAAAYVVu2JIcPJ4sWJXNztWtInOEJAAAAjJBDhw7VTmhFZ1nD3LllS7JxY7Md5s5B\nzz33bO2Ezhh4AgAAACNjzZo1tRNa0VmWzvJ27rytdkJnDDwBAACAkbFhw4baCa3oLEtneTfcsK52\nQmcMPAEAAICRMT09XTuhFZ1l6SxvyZLX1U7ojIEnAAAAADA2DDwBAAAAgLFh4AkAAACMjG3bttVO\naEVnWTrLe/TRe2sndMbAEwAAABgZe/furZ3Qis6yhrlz2bLkiiua7TB3Dtq//7O1EzozUTsAAAAA\noK277rqrdkIrOssa5s7du/sfDW/noJtu2lw7oTPO8AQAAAAAxoaBJwAAAAAwNgw8AQAAAICxYeAJ\nAAAAjIzZ2dnaCa3oLEtnedu2raqd0BkDTwAAAGBk3HrrrbUTWtFZls7yrr76bbUTOmPgCQAAAIyM\nmZmZ2gmt6CxLZ3nLl19bO6EzBp4AAAAAwNgw8AQAAACAM3TddcmVVzZbhoOBJwAAADAydu3aVTuh\nFZ1lDXPnwkLy5JPNdpg7Bz3xxMdqJ3TGwBMAAAAYGdu3b6+d0IrOsnSW9/jj99VO6IyBJwAAADAy\ndu7cWTuhFZ1l6SzvllvuqZ3QGQNPAAAAAGBsGHgCAAAAAGNjonYAAAC8bPPzdY47OZlMTdU5NgAA\nJ2XgCQDA6JqcbLarVtVrWFgw9AQ4i1avXp1f/dVfrZ1xSjrL0lnejh1r8/a3j85Nlk6HgScAAKNr\naqoZOB45cvaPPT/fDFprHBvgHDYzM1M7oRWdZQ1z59xccvhwsmhRctllw9s5aNmya2sndMbAEwCA\n0ebsSoBzysqVK2sntKKzrGHunJvrfzS8nYOmp1fUTuiMmxYBAAAAAGPDwBMAAAAAGBsGngAAAMDI\n2LNnT+2EVnSWpbO8Z575VO2Ezhh4AgAAACNj8+bNtRNa0VmWzvIeeWRr7YTOGHgCAAAAI2PHjh21\nE1rRWZbO8m6++e7aCZ0x8AQAAABGxgUXXFA7oRWdZeks77zzRqf1dE3UDgAAAACAUfXUU8mxY8nE\nRLJ8ee0aEgNPAAAAADhj11+fHDiQLF6c7N9fu4bEJe0AAADACFm3bl3thFZ0lqWzvAce2FA7oTMG\nngAAAMDIWLp0ae2EVnSWpbO8iy5aXDuhMwaeAAAAwMhYu3Zt7YRWdJals7xrrnlH7YTOGHgCAAAA\nAGPDwBMAAAAAGBsGngAAAMDI2LdvX+2EVnSWpbO8gwefrp3QGQNPAAAAYGSsX7++dkIrOsvSWd6D\nD26sndCZidoBAAAAAG1t3bq1dkIrOssa5s6HH06OHUsmJpLzzx/ezkErVryvdkJnhuEMz29J8rtJ\nvpbkuwdeW5rkgSTPJfmTJB9I8s0Da74rySeSHE2yP8k/P8kxfiDJY0m+muQPk/zjk6y5KcmTSf48\nye8nefNJ1vxEkj/q7eczSa5+yXcGAAAAFLV06dLaCa3oLGuYO5cvT668stkOc+egiy9eUjuhM8Mw\n8Nyc5MBJnn9lkt9Mcn6Sv5XkR9MMJX+hb82iJL+dZtD5N5KsTfJTSeb61nxHko+lGYq+PsnPJvlg\nkhV9a96QZEeSD6cZuv5ako8k+b6+NW9J8v4k7+3t5z8k+a0k335a7xYAAAAA6Eztgef/muR/STOk\nHDST5LVJViX5vSQPJ/nJJO9IcmFvzT9Mcl6St6Y5O/M30gw0+weeP5bkc73nnkqyLcmvDBzz9iQP\npRm+LiR5X+94t/etmUtyT+97n0ry7iRfTPLjp/eWAQAAAICu1Bx4Xprk7iQ3p7lEfNAbkjyR5Mt9\nzz2U5hL4q/rWfCLJXwys+bYkf7VvzUMD+34ozRmhr+w9/psvsub7ez8+L8n0KdYAAAAAHdu0aVPt\nhFZ0lqWzvN27P1g7oTO1Bp6vSHP5+C8l2fsia16d5ODAc3+W5Pneay+25mDfa0kzWD3Zmokkl5xi\nP8f3cUma4ejgmj/uWwMAAAB07OjRo7UTWtFZls7ynn/+ZOcfjofSA88NaW4+9FJfV6X5rM0L01w6\n3u8Vp3g86OsvLxcAAAAYJRs3bqyd0IrOsnSWd+ONd9RO6MxE4f19KMmvn2LN55P8H2kuNf/vA699\nJsm9SVanuZT9+wZevzjN5eXHL3P/cl54huWlfa+91JpjSQ71rbn0JGuO7+NQkr98kTVfyku4/fbb\nc9FFF53w3MqVK7Ny5cqX+jYAAADOou3bt2f79u0nPPeVr3ylUg0AL0fpgeezva9TeVeSf9b3eHGS\nf5fkR5I82nvuPyX5pznxkvSZNEPSx3qPP5nmJkXfnG98judMmru+f75vzd8bOP5Mkk+nGWIeXzOT\n5AMDa/5j78fP9445k+SjfWt+MM2Nkl7UnXfemenp6ZdaAgAAQGUnOzFl7969ueqqq17kOwAaW7Yk\nhw8nixYlc3OnXk/3an2G5xfT3FX9+NfTvef/MMl/6f34od5r9yZ5fZLrk/xcmhsdPddb8+tpBqAf\nTnJlkh9O8k+SbOk71i+nuYHRL6S56/ua3tfP9635QJph5voklye5o3e8O/vWbEny9jRnn742yfuT\nLOntHwAAADgLDh06dOpFQ0BnWcPcuWVLsnFjsx3mzkHPPdfmnMXRVPMu7YMGP4/za0l+KMmfpznT\ncmeS+5L8VN+aw2nOslyS5nL4rWkGm+/vW/O5JG9M8neSPJ7mzNK1OfHMzE8m+dE0w8zfS3JLmrNN\nP9235iNJbk/y0739XN3b7xdP+50CAAAAZ2TNmjW1E1rRWZbO8nbuvK12QmdKX9J+pj6X5i7og76Y\nF16OPug/J/mBU6z5nTQ3S3op/7b39VJ+qfcFAAAAVLBhw4baCa3oLEtneTfcsK52QmeG6QxPAAAA\ngJc0KvfJ0FmWzvKWLHld7YTOGHgCAAAAAGPDwBMAAAAAGBsGngAAAMDI2LZtW+2EVnSWpbO8Rx+9\nt3ZCZww8AQAAgJGxd+/e2gmt6CxrmDuXLUuuuKLZDnPnoP37P1s7oTPDcpd2AAAAgFO66667aie0\norOsYe7cvbv/0fB2Drrpps21EzrjDE8AAAAAYGwYeAIAAAAAY8PAEwAAAAAYGwaeAAAAwMiYnZ2t\nndCKzrJ0lrdt26raCZ1x0yIAAHg55ufrHHdyMpmaqnNsgIpuvfXW2gmt6CxLZ3lXX/222gmdMfAE\nAIAzMTnZbFdVPDtiYcHQEzjnzMzM1E5oRWdZOstbvvza2gmdMfAEAIAzMTXVDByPHDn7x56fbwat\nNY4NADDkDDwBAOBMObsSAM55112XHDyYXHppsnt37RoSNy0CAAAARsiuXbtqJ7Sis6xh7lxYSJ58\nstkOc+egJ574WO2Ezhh4AgAAACNj+/bttRNa0VmWzvIef/y+2gmdMfAEAAAARsbOnTtrJ7Sisyyd\n5d1yyz21Ezpj4AkAAAAAjA0DTwAAAABgbBh4AgAAAABjw8ATAAAAGBmrV6+undCKzrJ0lrdjx9ra\nCZ2ZqB0AAAAA0NbMzEzthFZ0ljXMnXNzyeHDyaJFyWWXDW/noGXLrq2d0BkDTwAAAGBkrFy5snZC\nKzrLGubOubn+R8PbOWh6ekXthM64pB0AAAAAGBsGngAAAADA2DDwBAAAAEbGnj17aie0orMsneU9\n88ynaid0xsATAAAAGBmbN2+undCKzrJ0lvfII1trJ3TGwBMAAAAYGTt27Kid0IrOsnSWd/PNd9dO\n6IyBJwAAADAyLrjggtoJregsS2d55503Oq2na6J2AAAAAACMqqeeSo4dSyYmkuXLa9eQGHgCAAAA\nwBm7/vrkwIFk8eJk//7aNSQuaQcAAABGyLp162ontKKzLJ3lPfDAhtoJnTHwBAAAAEbG0qVLaye0\norMsneVddNHi2gmdMfAEAAAARsbatWtrJ7Sisyyd5V1zzTtqJ3TGwBMAAAAAGBsGngAAAADA2DDw\nBAAAAEbGvn37aie0orMsneUdPPh07YTOGHgCAAAAI2P9+vW1E1rRWZbO8h58cGPthM5M1A4AAAAA\naGvr1q21E1rRWdYwdz78cHLsWDIxkZx//vB2Dlqx4n21Ezpj4AkAAACMjKVLl9ZOaEVnWcPcuXx5\n/6Ph7Rx08cVLaid0xiXtAAAAAMDYMPAEAAAAAMaGgScAAAAwMjZt2lQ7oRWdZeksb/fuD9ZO6IyB\nJwAAADAyjh49WjuhFZ1l6Szv+ee/WjuhMwaeAAAAwMjYuHFj7YRWdJals7wbb7yjdkJnDDwBAAAA\ngLExUTsAAAAAAEbVli3J4cPJokXJ3FztGhJneAIAAAAj5NChQ7UTWtFZ1jB3btmSbNzYbIe5c9Bz\nzz1bO6EzBp4AAADAyFizZk3thFZ0lqWzvJ07b6ud0BkDTwAAAGBkbNiwoXZCKzrL0lneDTesq53Q\nGQNPAAAAYGRMT0/XTmhFZ1k6y1uy5HW1EzrjpkUAADCq5ufrHHdyMpmaqnNsAIBTMPAEAIBRMznZ\nbFetqtewsGDoCQAMJQNPAAAYNVNTzcDxyJGzf+z5+WbQWuPYAEm2bduWt73tbbUzTklnWTrLe/TR\ne3P99bfXzuiEgScAAIwiZ1cC56i9e/eOxEBJZ1nD3LlsWfJX/kpy6aXD3Tlo//7P1k7ojIEnAAAA\nMDLuuuuu2gmt6CxrmDt37+5/NLydg266aXPthM64SzsAAAAAMDYMPAEAAACAsWHgCQAAAACMDQNP\nAAAAYGTMzs7WTmhFZ1k6y9u2bVXthM4YeAIAAAAj49Zbb62d0IrOsnSWd/XVo3E3+TNh4AkAAACM\njJmZmdoJregsS2d5y5dfWzuhMwaeAAAAAMDYMPAEAAAAgDN03XXJlVc2W4aDgScAAAAwMnbt2lU7\noRWdZQ1z58JC8uSTzXaYOwc98cTHaid0xsATAAAAGBnbt2+vndCKzrJ0lvf44/fVTuiMgScAAAAw\nMnbu3Fk7oRWdZeks75Zb7qmd0BkDTwAAAABgbBh4AgAAAABjw8ATAAAAABgbBp4AAADAyFi9enXt\nhFZ0lqWzvB071tZO6MxE7QAAAACAtmZmZmontKKzrGHunJtLDh9OFi1KLrtseDsHLVt2be2Ezhh4\nAgAAACNj5cqVtRNa0VnWMHfOzfU/Gt7OQdPTK2ondMYl7QAAAADA2DDwBAAAAADGhoEnAAAAMDL2\n7NlTO6EVnWXpLO+ZZz5VO6EzBp4AAADAyNi8eXPthFZ0lqWzvEce2Vo7oTMGngAAAMDI2LFjR+2E\nVnSWpbO8m2++u3ZCZww8AQAAgJFxwQUX1E5oRWdZOss777zRaT1dE7UDAAAAAGBUPfVUcuxYMjGR\nLF9eu4bEwBMAAAAAztj11ycHDiSLFyf799euIXFJOwAAADBC1q1bVzuhFZ1l6SzvgQc21E7ojIEn\nAAAAMDKWLl1aO6EVnWXpLO+iixbXTuiMgScAAAAwMtauXVs7oRWdZeks75pr3lE7oTMGngAAAADA\n2DDwBAAAAADGhoEnAAAAMDL27dtXO6EVnWXpLO/gwadrJ3TGwBMAAAAYGevXr6+d0IrOsnSW9+CD\nG2sndGaidgAAAABAW1u3bq2d0IrOsoa58+GHk2PHkomJ5Pzzh7dz0IoV76ud0BkDTwAAAGBkLF26\ntHZCKzrLGubO5cv7Hw1v56CLL15SO6EzLmkHAAAAAMaGgScAAAAAMDZc0g4AAJy++fk6x52cTKam\n6hwbGAqbNm3KHXfcUTvjlHSWpbO83bs/mNnZn6md0QkDTwAAoL3JyWa7alW9hoUFQ084hx09erR2\nQis6y9JZ3vPPf7V2QmcMPAEAgPamppqB45EjZ//Y8/PNoLXGsYGhsXHjxtoJregsS2d5N944Gmei\nngkDTwAA4PQ4uxIAGGIGngAAAABwhrZsSQ4fThYtSubmateQuEs7AAAAMEIOHTpUO6EVnWUNc+eW\nLcnGjc12mDsHPffcs7UTOmPgCQAAAIyMNWvW1E5oRWdZOsvbufO22gmdMfAEAAAARsaGDRtqJ7Si\nsyyd5d1ww7raCZ0x8AQAAIDR87eTPJDkQJKvJXnTSdZs6L1+NMkjSa44W3Fdmp6erp3Qis6ydJa3\nZMnraid0xsATAAAARs8FSR5P8s7e468PvH5Hktt7r39vki8n+e0kF56tQIBa3KUdAAAARs/He18n\n84o0w87/M8mu3nP/e5KDSf5Bkrs7rwOoyBmeAAAAMF6+I8mlSR7qe+75JJ9I8v1Vigratm1b7YRW\ndJals7xHH723dkJnDDwBAABgvLy6tz048Pwf9702svbu3Vs7oRWdZQ1z57JlyRVXNNth7hy0f/9n\nayd0xiXtAAAAcO4Y/KzPkXPXXXfVTmhFZ1nD3Ll7d/+j4e0cdNNNm2sndMYZngAAADBevtzbXjrw\n/KV9r53UG9/4xszOzp7w9YY3vCG7du06Yd1DDz2U2dnZF3z/O9/5zhdc0rt3797Mzs7m0KFDJzz/\nnve8J5s2bTrhuS984QuZnZ3Nvn37Tnj+Qx/6UNatW3fCc0ePHs3s7Gz27NlzwvPbt2/P6tWrX9D2\nlre8xfvwPryP7duzdu3aF7Tdffdb8ru/e+L7eOSRR87q+3jrW9+a17zmNSf8/vOud73rBcdv4xVn\n9F3l/FCSn07yXUn+W5LfSXJT3+tL04zGr03y1SS/nuSnkvxF35rvSrI1zV3n/jTJv0zy3oHj/ECS\nLUmuSPJfkmzuret3U+/7vjPJHyb5Z/nGhzsf9xNJ1qW5BOD303wI9J6c3HSSxx577LFMT0+/yBIA\nAKC1vXuTq65KHnss8f+xOQv27t2bq666KkmuSjLM16l+Lcmbk9zfe/yKJAeSvD/Jz/WeOy/NJe3r\nkvyrk+zD32HhHHHo0KG8//335VWvWpELL7zkBa8/99yhPPvsfXn3u1fkkkte+PrZdKa/D9c8w/Om\nJP86ybYk353mg5P/777XX5nkN5Ocn+RvJfnR3vf8Qt+aRUl+O8n+JH8jydo0A9G5vjXfkeRjaT6c\n+fVJfjbJB5Os6FvzhiQ7kny41/JrST6S5Pv61rwlzR8W7+3t5z8k+a0k337a7xwAAABenm9N83fT\n1/cef2fvx9+e5rL1O5P80zSD0L+e5u+7z6U5kQhgrNUaeE4k+UCa4eTdSf4gydNJ7utbM5PktUlW\nJfm9JA8n+ckk70hyYW/NP0zzr1RvTfJkkt9IM9DsH3j+WJLP9Z57Ks2A9Vd6xz7u9jR3r9ucZCHJ\n+3rHu71vzVySe3rf+1SSdyf5YpIfP/23DwAAAC/L96Y522lvmgHnlt6PN/Ze35xm6PmLST6d5LI0\nf8/+b2e9tLCTXWI7jHSWpbO8bdtW1U7oTK2B53SSb0vzm/LjaS4z/1iSK/vWvCHJEznx80UeSvIt\naU5jPb7mEznxEveHevv+q31rHho4/kNpzgh9Ze/x33yRNd/f+/F5veaXWgMAAABny79P83f6b0rz\nd9vjP17Tt2Zjmr8fn5/mo+KePLuJ3bj11ltrJ7Sisyyd5V199dtqJ3Sm1sDzO3vbDUl+JsnfTfJn\naX7Dvrj32quTHBz4vj9L8nzvtRdbc7DvtaT5UOaTrZlIcknf2pOtOb6PS9L8ATK45o/71gAAAAAd\nm5mZqZ3Qis6ydJa3fPm1tRM6U3rguSHNhyW/1NdVfcf9F2kuQ9+bZHWaMz7/ft/+TnVTpa8X6gYA\nAAAAxsBE4f19KKf+AOTPp7nZUHLi6fTPJ3kmzZ3Zk+ZS9v6bBiXN2Z/n5RuXuX85LzzD8tK+115q\nzbEkh/rWXHqSNcf3cSjJX77Imi/lJdx+++256KKLTnhu5cqVWbly5Ut9GwAAAGfR9u3bs3379hOe\n+8pXvlKpBhgl112XHDyYXHppsnt37RqS8gPPZ3tfp/JYkv+e5PIk/6n33DenuaP653uPP5nmjnL9\nl6TP9L7vsb41P9v73r/oW3NgYD9/b+D4M2k+tPkv+9bMpLmRUv+a/9j78fO9Y84k+Wjfmh9Mc4bq\ni7rzzjszPT39UksAAACo7GQnpuzduzdXXXXVi3wHtezatStvfvOba2ecks6yhrlzYSE5cCD5r/91\nuDsHPfHEx/KGN9xSO6MTtT7D83CSX07zAco/mGR5kl9Kc8n7v+mt+XdpzgC9N8nrk1yf5OfS3NX9\nud6aX08zAP1wmhse/XCSf5Lm7nTH/XKaGxj9Qpq7vq/pff1835oPpBlmrk8zhL2jd7w7+9ZsSfL2\nNJfevzbJ+5Ms6e0fAAAAOAsGz8QdVjrL0lne44/fVzuhM6XP8Dwd69JcVv5rae4Y96k4tNP9AAAg\nAElEQVQk1yX5r73Xv5bkh5L8YpozLb+aZvi5rm8fh9MMTO9K8pkkf5pmsPn+vjWfS/LG3nPvTHP2\n59qceGbmJ5P8aJrPFH1vkj9I8iNpzgI97iNJXpXkp5NcluYO8m9M8sUzefMAAADA6du5c2fthFZ0\nlqWzvFtuuad2QmdqDjyPpRlernuJNV/MCy9HH/Sfk/zAKdb8TpqbJb2Uf9v7eim/1PsCAAAAAIZQ\nrUvaAQAAAACKM/AEAAAAAMaGgScAAAAwMlavXl07oRWdZeksb8eOtbUTOlPzMzwBAAAATsvMzEzt\nhFZ0ljXMnXNzyeHDyaJFyWWXDW/noGXLrq2d0BkDTwAAAGBkrFy5snZCKzrLGubOubn+R8PbOWh6\nekXthM64pB0AAAAAGBsGngAAAADA2DDwBAAAAEbGnj17aie0orMsneU988ynaid0xsATAAAAGBmb\nN2+undCKzrJ0lvfII1trJ3TGwBMAAAAYGTt27Kid0IrOsnSWd/PNd9dO6IyBJwAAADAyLrjggtoJ\nregsS2d55503Oq2na6J2AAAAAACMqqeeSo4dSyYmkuXLa9eQGHgCAAAAwBm7/vrkwIFk8eJk//7a\nNSQuaQcAAABGyLp162ontKKzLJ3lPfDAhtoJnTHwBAAAAEbG0qVLaye0orMsneVddNHi2gmdcUk7\nAAAwWubn6x17cjKZmqp3fCBr166tndCKzrJ0lnfNNe+ondAZA08AAGA0TE4221Wr6nYsLBh6AsAQ\nM/AEAABGw9RUM2w8cqTO8efnm2FrreMDAK0YeAIAAKPDmZVwztu3b18uv/zy2hmnpLMsneUdPPh0\nLrzwktoZnXDTIgAAAGBkrF+/vnZCKzrL0lnegw9urJ3QGWd4AgAAACNj69attRNa0VnWMHc+/HBy\n7FgyMZGcf/7wdg5aseJ9tRM6Y+AJAAAAjIylS5fWTmhFZ1nD3Ll8ef+j4e0cdPHFS2ondMYl7QAA\nAADA2DDwBAAAAADGhoEnAAAAMDI2bdpUO6EVnWXpLG/37g/WTuiMgScAAAAwMo4ePVo7oRWdZen8\n/9m79zip6sP+/y9g1YBCvDWIINWmgEi8dPESDcZbs1ovqPhLzDaosGpqFdRsBJsYI1ttFBLRGIjG\nn0QatYBpDFGiUSuoJUbaiImiXDTegiIRE0UCFgl8//icgdlhL2fhHD4zw+v5eOxjmDOfOec9s7Mo\n7/2c88ne2rVrYkfIjYWnJEmSJEmqGE1NTbEjpGLObJkzeyeddGXsCLmx8JQkSZIkSZJUNWpiB5Ak\nSZIkSZIq1cSJsHIl9OgBjY2x0wic4SlJkiRJkirIihUrYkdIxZzZKuecEydCU1O4LeecpVatejd2\nhNxYeEqSJEmSpIrR0NAQO0Iq5syWObM3Y8ZlsSPkxsJTkiRJkiRVjHHjxsWOkIo5s2XO7J144pjY\nEXJj4SlJkiRJkipGbW1t7AipmDNb5sxenz4Hx46QGwtPSZIkSZIkSVXDwlOSJEmSJElS1bDwlCRJ\nkiRJFWPKlCmxI6RizmyZM3vz5t0dO0JuLDwlSZIkSVLFmD9/fuwIqZgzW+Wcs39/OOCAcFvOOUst\nXfpc7Ai5qYkdQJIkSZIkKa3JkyfHjpCKObNVzjlnzy6+V745S5111oTYEXLjDE9JkiRJkiRJVcPC\nU5IkSZIkSVLVsPCUJEmSJEmSVDUsPCVJkiRJUsUYOnRo7AipmDNb5szelCnDY0fIjYWnJEmSJEmq\nGKNGjYodIRVzZsuc2Rsy5PzYEXJj4SlJkiRJkipGXV1d7AipmDNb5szegAHHxY6QGwtPSZIkSZIk\nSVXDwlOSJEmSJEnaQscfD4MGhVuVBwtPSZIkSZJUMWbOnBk7QirmzFY551yyBF58MdyWc85Szz//\nYOwIubHwlCRJkiRJFWPatGmxI6RizmyZM3vPPntf7Ai5sfCUJEmSJEkVY8aMGbEjpGLObJkze+ee\ne0fsCLmx8JQkSZIkSZJUNSw8JUmSJEmSJFUNC09JkiRJkiRJVcPCU5IkSZIkVYyRI0fGjpCKObNl\nzuxNnz46doTc1MQOIEmSJEmSlFZdXV3sCKmYM1vlnLOxEVauhB49oFev8s1Zqn//42JHyI2FpyRJ\nkiRJqhj19fWxI6RizmyVc87GxuJ75ZuzVG3tsNgRcuMp7ZIkSZIkSZKqhoWnJEmSJEmSpKrhKe2S\nJEmS1BELF8Y5bvfu0K9fnGNLZWTu3LkMGTIkdox2mTNb5szeK688zUEHnRo7Ri4sPCVJkiQpje7d\nw+3w4fEyLFli6ant3oQJEyqiUDJntsyZvTlzJll4SpIkSdJ2rV+/UDh+8MG2P/bChaFojXFsqcxM\nnz49doRUzJktc2bvnHNujx0hNxaekiRJkpSWsyul6Lp16xY7QirmzJY5s7fjjpWTtaMsPCVJkiRJ\nkqQttHgxrFsHNTUwYEDsNAILT0mSJEmSJGmLnXACvPkm9O4NS5fGTiOAzrEDSJIkSZIkpTVmzJjY\nEVIxZ7bMmb0HHhgXO0JuLDwlSZIkSVLF6Nu3b+wIqZgzW+bM3q679o4dITcWnpIkSZIkqWKMHj06\ndoRUzJktc2bv6KMvjB0hNxaekiRJkiRJkqqGhackSZIkSZKkqmHhKUmSJEmSKsaiRYtiR0jFnNky\nZ/aWL38pdoTcWHhKkiRJkqSKMXbs2NgRUjFntsyZvVmzmmJHyE1N7ACSJEmSJElpTZo0KXaEVMyZ\nrXLO+dhjsG4d1NRA167lm7PUsGE3xI6QGwtPSZIkSZJUMfr27Rs7QirmzFY55xwwoPhe+eYstdtu\nfWJHyI2ntEuSJEmSJEmqGhaekiRJkiRJkqqGhackSZIkSaoY48ePjx0hFXNmy5zZmz37ltgRcmPh\nKUmSJEmSKsbq1atjR0jFnNkyZ/bWrl0TO0JuLDwlSZIkSVLFaGpqih0hFXNmy5zZO+mkK2NHyI2F\npyRJkiRJkqSqURM7gCRJkiRJklSpJk6ElSuhRw9obIydRuAMT0mSJEmSVEFWrFgRO0Iq5sxWOeec\nOBGamsJtOecstWrVu7Ej5MbCU5IkSZIkVYyGhobYEVIxZ7bMmb0ZMy6LHSE3Fp6SJEmSJKlijBs3\nLnaEVMyZLXNm78QTx8SOkBsLT0mSJEmSVDFqa2tjR0jFnNkyZ/b69Dk4doTcWHhKkiRJkiRJqhoW\nnpIkSZIkSZKqhoWnJEmSJEmqGFOmTIkdIRVzZsuc2Zs37+7YEXJj4SlJkiRJkirG/PnzY0dIxZzZ\nKuec/fvDAQeE23LOWWrp0udiR8hNTewAkiRJkiRJaU2ePDl2hFTMma1yzjl7dvG98s1Z6qyzJsSO\nkBtneEqSJEmSJEmqGhaekiRJkiRJkqqGhackSZIkSZKkqmHhKUmSJEmSKsbQoUNjR0jFnNkyZ/am\nTBkeO0JuLDwlSZIkSVLFGDVqVOwIqZgzW+bM3pAh58eOkBsLT0mSJEmSVDHq6upiR0jFnNkyZ/YG\nDDgudoTcWHhKkiRJkiRJqhoWnpIkSZIkSdIWOv54GDQo3Ko8WHhKkiRJkqSKMXPmzNgRUjFntso5\n55Il8OKL4bacc5Z6/vkHY0fIjYWnJEmSJEmqGNOmTYsdIRVzZsuc2Xv22ftiR8hNTewAkiRJkqSU\nFi6Mc9zu3aFfvzjHlkrMmDEjdoRUzJktc2bv3HPviB0hNxaekiRJklTuuncPt8OHx8uwZImlpySp\nIlh4SpIkSVK569cvFI4ffLDtj71wYShaYxxbkqQtYOEpSZIkSZXA2ZWSJKXiokWSJEmSJKlijBw5\nMnaEVMyZLXNmb/r00bEj5MYZnpIkSZIkqWLU1dXFjpCKObNVzjkbG2HlSujRA3r1Kt+cpfr3Py52\nhNxYeEqSJEmSpIpRX18fO0Iq5sxWOedsbCy+V745S9XWDosdITee0i5JkiRJkiSpalh4SpIkSZIk\nSaoaMQvP/YEHgBXA+8Bc4NiSMX2TMauAd4DvAjuUjDkQeAJYDSwFrm7hWMcAzwBrgN8B/9TCmLOA\nF4EPgReAM1oYczHwarKfXwNDWn95kiRJkiQpa3Pnzo0dIRVzZsuc2XvlladjR8hNzMLzweT2WGAw\n8BtgFtAz2d4F+DnQFfgM8EVCKXlj0T56AI8Sis5DgdHAFUDx1RP2S471BHAI8C3gFqD4QgVHAtOB\nqcBBwF3AvcDhRWPOBm4Crk3289/AQ8A+HX3hkiRJkiRpy0yYMCF2hFTMmS1zZm/OnEmxI+QmVuG5\nJ7AvcAOwAHgZ+BrQDTggGVMHDASGA78FHgO+ClwI7JKM+RKwIzCCMDvzp4RCs7jwvAh4Ldm2GJgC\n/JBQjBZcDjwCTACWJLkeS7YXNAJ3JM9dDHwF+D3wz1vyBkiSJEmSpI6bPn167AipmDNb5szeOefc\nHjtCbmIVniuAecB5hJKzhlBMvk049RzCrMvnk20FjwA7EWaEFsY8AXxUMmZv4K+LxjxScvxHCDNC\nuyT3P93KmKOSP+8I1LYzRpIkSZIk5axbt26xI6RizmyZM3s77lg5WTuqJuKxTwceBj4A1gPLgX8A\nViaP75VsK/YnYG3yWGHMKyVjlhc99jrhFPnS/SwnvPY9kz+3dKzCdpJxXVoY84eiMZIkSZIkSdrO\nLF4M69ZBTQ0MGBA7jSD7GZ7jCOVlW1+1hLLxAeBNwsI/hwE/I1zDs7hA7NTO8TZkF12SJEmSJEnq\nmBNOgE99KtyqPGRdeH6PsPp6W18vAJ8jnJb+ReBXhAWLLiGsfn5esq+32bSAUcFuhNPL3y4aUzrD\nsmfRY22NWUc4tb61Y/Us2scK4C+tjFlGGy6//HKGDh3a7GvatGltPUWSJEmStI1NmzZts3+7XX75\n5e0/UdvcmDFjYkdIxZzZMmf2HnhgXOwIucn6lPZ3k6/2dCbMzlxfsn0Dm2Z1/gr4Os1PSa8D/o9N\n1/n8FWGRoh3YdB3POsLM0deLxpxWcpw64H8JJWZhTB3w3ZIxv0z+vDY5Zh1hJmrB5wgLJbXq5ptv\npra2tq0hkiRJkqTI6uvrqa+vb7Zt/vz5DB48uJVnKJa+ffvGjpCKObNlzuztumvv2BFyE2vRol8C\nfwR+BBwE9Ae+TVho6OfJmIcJK6/fDRwCnJCMuR1YlYz5D0IBOhUYBJxJWO19YtGxbkv2eyNh1feG\n5Os7RWO+SygzxxJmoV6ZHO/mojETgQuAkcl+bgL6JPuXJEmSJEnbwOjRo2NHSMWc2TJn9o4++sLY\nEXITa9Gi94ATgeuBxwinqS8gLGT0fDJmPXAK8H1CQbqGUH4Wzw1eSZhlORn4NaFEvZFQRha8Bpyc\nbLuEMPtzNM1nZv6KcHr9dcC1wMvAFwizQAvuBfYAvgn0SnKeDPx+C16/JEmSJEmSpBzEXKX9N4RV\n2dvyezY/Hb3UAuCYdsY8SbhmaFt+kny15dbkS5IkSZIkSVIZinVKuyRJkiRJUoctWrQodoRUzJkt\nc2Zv+fKXYkfIjYWnJEmSJEmqGGPHjo0dIRVzZsuc2Zs1qyl2hNzEPKVdkiRJkiSpQyZNmhQ7Qirm\nzFY553zsMVi3DmpqoGvX8s1ZatiwG2JHyI2FpyRJkiRJqhh9+/aNHSEVc2arnHMOGFB8r3xzltpt\ntz6xI+TGU9olSZIkSZIkVQ0LT0mSJEmSJElVw8JTkiRJkiRVjPHjx8eOkIo5s2XO7M2efUvsCLmx\n8JQkSZIkSRVj9erVsSOkYs5smTN7a9euiR0hNxaekiRJkiSpYjQ1NcWOkIo5s2XO7J100pWxI+TG\nwlOSJEmSJElS1aiJHUCSJEmSJEmqVBMnwsqV0KMHNDbGTiNwhqckSZIkSaogK1asiB0hFXNmq5xz\nTpwITU3htpxzllq16t3YEXJj4SlJkiRJkipGQ0ND7AipmDNb5szejBmXxY6QGwtPSZIkSZJUMcaN\nGxc7QirmzJY5s3fiiWNiR8iNhackSZIkSaoYtbW1sSOkYs5smTN7ffocHDtCbiw8JUmSJEmSJFUN\nC09JkiRJkiRJVcPCU5IkSZIkVYwpU6bEjpCKObNlzuzNm3d37Ai5sfCUJEmSJEkVY/78+bEjpGLO\nbJVzzv794YADwm055yy1dOlzsSPkpiZ2AEmSJEmSpLQmT54cO0Iq5sxWOeecPbv4XvnmLHXWWRNi\nR8iNMzwlSZIkSZIkVQ1neEqSJEmS2rdwYZzjdu8O/frFObYkqSJZeEqSJEmSWte9e7gdPjxehiVL\nLD0lSalZeEqSJEmSWtevXygcP/hg2x974cJQtMY4tsrW0KFDuf/++2PHaJc5s2XO7E2ZMpzLLvtF\n7Bi5sPCUJEmSJLXN2ZUqI6NGjYodIRVzZsuc2Rsy5PzYEXLjokWSJEmSJKli1NXVxY6QijmzZc7s\nDRhwXOwIubHwlCRJkiRJklQ1LDwlSZIkSZKkLXT88TBoULhVebDwlCRJkiRJFWPmzJmxI6RizmyV\nc84lS+DFF8NtOecs9fzzD8aOkBsLT0mSJEmSVDGmTZsWO0Iq5syWObP37LP3xY6QGwtPSZIkSZJU\nMWbMmBE7QirmzJY5s3fuuXfEjpAbC09JkiRJkiRJVcPCU5IkSZIkSVLVsPCUJEmSJEmSVDUsPCVJ\nkiRJUsUYOXJk7AipmDNb5sze9OmjY0fITU3sAJIkSZIkSWnV1dXFjpCKObNVzjkbG2HlSujRA3r1\nKt+cpfr3Py52hNxYeEqSJEmSpIpRX18fO0Iq5sxWOedsbCy+V745S9XWDosdITee0i5JkiRJkiSp\nalh4SpIkSZIkSaoaFp6SJEmSJKlizJ07N3aEVMyZLXNm75VXno4dITcWnpIkSZIkqWJMmDAhdoRU\nzJktc2ZvzpxJsSPkxsJTkiRJkiRVjOnTp8eOkIo5s2XO7J1zzu2xI+TGwlOSJEmSJFWMbt26xY6Q\nijmzZc7s7bhj5WTtqJrYASRJkiRJkqRKtXgxrFsHNTUwYEDsNAILT0mSJEmSJGmLnXACvPkm9O4N\nS5fGTiPwlHZJkiRJklRBxowZEztCKubMljmz98AD42JHyI2FpyRJkiRJqhh9+/aNHSEVc2bLnNnb\nddfesSPkxsJTkiRJkiRVjNGjR8eOkIo5s2XO7B199IWxI+TGwlOSJEmSJElS1bDwlCRJkiRJklQ1\nLDwlSZIkSVLFWLRoUewIqZgzW+bM3vLlL8WOkBsLT0mSJEmSVDHGjh0bO0Iq5syWObM3a1ZT7Ai5\nqYkdQJIkSZIkKa1JkybFjpCKObNVzjkfewzWrYOaGujatXxzlho27IbYEXJj4SlJkiRJkipG3759\nY0dIxZzZKuecAwYU3yvfnKV2261P7Ai58ZR2SZIkSZIkSVXDwlOSJEmSJElS1bDwlCRJkiRJFWP8\n+PGxI6RizmyZM3uzZ98SO0JuLDwlSZIkSVLFWL16dewIqZgzW+bM3tq1a2JHyI2FpyRJkiRJqhhN\nTU2xI6RizmyZM3snnXRl7Ai5sfCUJEmSJEmSVDVqYgeQJEmSJEmSKtXEibByJfToAY2NsdMInOEp\nSZIkSZIqyIoVK2JHSMWc2SrnnBMnQlNTuC3nnKVWrXo3doTcOMNTkiRJklTeFi7cvo6rNjU0NHD/\n/ffHjtEuc2bLnNmbMeMyLrvsF7Fj5MLCU5IkSZJUnrp3D7fDh8fNobIybty42BFSMWe2zJm9E08c\nEztCbiw8JUmSJEnlqV8/WLIEPvggzvEffBCuvjrOsdWq2tra2BFSMWe2zJm9Pn0Ojh0hNxaekiRJ\nkqTy1a9fvGN7SrskVSQXLZIkSZIkSZJUNSw8JUmSJElSxZgyZUrsCKmYM1vmzN68eXfHjpAbC09J\nkiRJklQx5s+fHztCKubMVjnn7N8fDjgg3JZzzlJLlz4XO0JuvIanJEmSJEmqGJMnT44dIRVzZquc\nc86eXXyvfHOWOuusCbEj5MYZnpIkSZIkSZKqhoWnJEmSJEmSpKph4SlJkiRJkiSpalh4SpIkSZKk\nijF06NDYEVIxZ7bMmb0pU4bHjpAbC09JkiRJklQxRo0aFTtCKubMljmzN2TI+bEj5MbCU5IkSZIk\nVYy6urrYEVIxZ7bMmb0BA46LHSE3Fp6SJEmSJEmSqoaFpyRJkiRJkrSFjj8eBg0KtyoPFp6SJEmS\nJKlizJw5M3aEVMyZrXLOuWQJvPhiuC3nnKWef/7B2BFyY+EpSZIkSZIqxrRp02JHSMWc2TJn9p59\n9r7YEXJj4SlJkiRJkirGjBkzYkdIxZzZMmf2zj33jtgRcmPhKUmSJEmSJKlqWHhKkiRJkiRJqhoW\nnpIkSZIkSZKqhoWnJEmSJEmqGCNHjowdIRVzZsuc2Zs+fXTsCLmpiR1AkiRJkiQprbq6utgRUjFn\ntso5Z2MjrFwJPXpAr17lm7NU//7HxY6QGwtPSZIkSZJUMerr62NHSMWc2SrnnI2NxffKN2ep2tph\nsSPkxlPaJUmSJEmSJFUNC09JkiRJkiRJVcPCU5IkSZIkVYy5c+fGjpCKObNlzuy98srTsSPkxsJT\nkiRJkiRVjAkTJsSOkIo5s2XO7M2ZMyl2hNxYeEqSJEmSpIoxffr02BFSMWe2zJm9c865PXaE3Fh4\nSpIkSZKkitGtW7fYEVIxZ7bMmb0dd6ycrB1VEzuAJEmSJEmSVKkWL4Z166CmBgYMiJ1GYOEpSZIk\nSZIkbbETToA334TevWHp0thpBJ7SLkmSJEmSKsiYMWNiR0jFnNkyZ/YeeGBc7Ai5sfCUJEmSJEkV\no2/fvrEjpGLObJkze7vu2jt2hNxYeEqSJEmSVH3GAetLvt6KGSgro0ePjh0hFXNmy5zZO/roC2NH\nyI3X8JQkSZIkqTotAP6+6P5fYgWRpG3JwlOSJEmSpOr0F+APsUNI0rbmKe2SJEmSJFWnfsCbwCvA\nNGC/uHGysWjRotgRUjFntsyZveXLX4odITcWnpIkSZIkVZ+ngXOAOuBCYC/gKWD3mKGyMHbs2NgR\nUjFntsyZvVmzmmJHyI2FpyRJkiRJ1ecXwE+BF4DHgFOS7ee19aSTTz6ZoUOHNvs68sgjmTlzZrNx\njzzyCEOHDt3s+ZdccglTpkxptm3+/PkMHTqUFStWNNt+zTXXMH78+Gbb3njjDYYOHbrZLLnvfe97\njBkzBoBJkyYBsHr1aoYOHcrcuXObjZ02bRojR47cLNvZZ5+9TV/HmjVr2nwdBbFfR+H93NLvx7Z6\nHUOGDGnzdRTEeB1f+9pMFiyAxx4L72fMn480r6OwsNKwYTds3H777Wfzm980/37MmTNnm76OESNG\n8Ld/+7fN/v659NJLNzt+Gp226FlKoxZ45plnnqG2tjZ2FkmSJElSB82/5x4GDx8OMBiYHzlOFh4B\nXgIuaeEx/w0rbSdWrFjBTTfdxx57DGOXXfbc7PFVq1bw7rv38ZWvDGPPPTd/fFuaP38+gwcPhg7+\nPewMT0mSJEmSqt9OwAHAsthBJClvFp6SJEmSJLVk331jJ9ga3wE+S1io6AjgP4FdgH+PGUqStgUL\nT0mSJEmSWtK1a+wEW6M3YWX2RcBPgA+BTwO/jxkqC6XXAyxX5syWObM3e/YtsSPkpiZ2AEmSJEmS\nlLn62AHysnr16tgRUjFntsyZvbVr18SOkJu8ZnheBTwFrAb+1MqYvsADwCrgHeC7wA4lYw4Enkj2\nsxS4uoX9HAM8A6wBfgf8UwtjzgJeJPxG6wXgjBbGXAy8muzn18CQFsaMA95M8swhXP9EkiRJkiRt\nI01NTbEjpGLObJkzeyeddGXsCLnJq/DcAZgBfL+Vx7sAPwe6Ap8BvkgoJW8sGtMDeJRQdB4KjAau\nABqLxuwHPEgoRQ8BvgXcAgwrGnMkMB2YChwE3AXcCxxeNOZs4Cbg2mQ//w08BOxTNOZK4HLCanaH\nAW8n+XZp9V2QJEmSJEmStE3ldUr7uOR2RCuP1wEDgc8RikOArxJKya8TZn1+Cdgx2cdHhBma/QmF\n58TkORcBr7GpBF1MKEevAO5Ltl0OPAJMSO7fQJgVejnwj8m2RuAO4IfJ/a8AJwL/nOTplIz/N2Bm\nMuY8YHmyj9tbeZ2SJEmSJEmqYhMnwsqV0KMHNDa2P175i7Vo0ZHA82wqOyGUkjsBg4vGPEEoO4vH\n7A38ddGYR0r2/Qih9OyS3P90K2OOSv68I1Dbzpj9gJ4lY9Ym+Y5CkiRJkiRtEytWrIgdIRVzZquc\nc06cCE1N4bacc5Zaterd2BFyE2vRor0IsyOL/YlQIu5VNOaVkjHLix57nVBClu5nOeF17Zn8uaVj\nFbaTjOvSwpg/lGShlTF9acPChQvbeliSJEmSVKb891x5amho4P77748do13mzJY5szdjxmVcdtkv\nYsfIRUcKz3HAN9sZcygwP+X+OrXz+IaU+4mttZzLgIXDhw8fuC3DSJIkSZIytZDw7zuViXHjxsWO\nkIo5s2XO7J144pjYEXLTkcLze8B/tDPm9ZT7WkbzRYMAdiOcXl44zf1tNs2sLOhZ9FhbY9YBK4rG\n9GxhTGEfK4C/tDKm8B+1t1t4Xkv3iy0DTgB6tfK4JEmSJKn8LcPCs6zU1tbGjpCKObNlzuz16XNw\n7Ai56Ujh+W7ylYVfAVfR/JT0OuD/gGeKxnyLsOL7R0Vj3mRTsfor4LSSfdcB/0soMQtj6oDvloz5\nZfLntckx64CfFY35HPDT5M+vEorNOuC3ybYdCYsftVWH+x9GSZIkSZIkaRvKa9GivsAhyW0X4ODk\n/s7J448QVl2/O9l+AvBtwmrnq5Ix/0EoQKcCg4Azga+xaYV2gNsICxjdSFj1vSH5+k7RmO8Sisqx\nwP7Alcnxbi4aMxG4ABiZ7OcmoE+yfwinrd9MWLH9DOBTSa5VtD/rVZIkSZIkSdI2klfh+a+Ea3mO\nI5SczxJmURZWYF8PnAJ8SJhpOQO4D7iiaB8rCbMs+wC/BiYRis2bisa8BpwMHAvplCcAACAASURB\nVJsc4ypgNJtmZkKY4flFQpn5W+Bc4AuEWaAF9wKXE65R+iwwJNnv74vGTCCUnt9PntuLUKT+OcX7\nIUmSJEmSMjBlypTYEVIxZ7bMmb158+6OHSE3eRWeI5J9dybM8CzcPlk05veE09F3JqyUfjmbTl0v\nWEA4bbwr0Bu4toVjPUkoUj8GfJIwS7TUTwgzN3cizBad2cKYW4H9kv0cBsxtYUwTsHeS5zjCLFVJ\nkiRJkrSNzJ+fdq3kuMyZrXLO2b8/HHBAuC3nnKWWLn0udoTcdOQanpIkSZIkSVFNnjw5doRUzJmt\ncs45e3bxvfLNWeqssybEjpCbvGZ4SpIkSZIkSdI2Z+EpSZIkSZIkqWpYeEqSJEmSJEmqGhaekiRJ\nkiSpYgwdOjR2hFTMmS1zZm/KlOGxI+TGwlOSJEmSJFWMUaNGxY6QijmzZc7sDRlyfuwIubHwlCRJ\nkiRJFaOuri52hFTMmS1zZm/AgONiR8iNhackSZIkSZKkqmHhKUmSJEmSJG2h44+HQYPCrcqDhack\nSZIkSaoYM2fOjB0hFXNmq5xzLlkCL74Ybss5Z6nnn38wdoTcWHhKkiRJkqSKMW3atNgRUjFntsyZ\nvWefvS92hNxYeEqSJEmSpIoxY8aM2BFSMWe2zJm9c8+9I3aE3Fh4SpIkSZIkSaoaFp6SJEmSJEmS\nqoaFpyRJkiRJkqSqYeEpSZIkSZIqxsiRI2NHSMWc2TJn9qZPHx07Qm5qYgeQJEmSJElKq66uLnaE\nVMyZrXLO2dgIK1dCjx7Qq1f55izVv/9xsSPkxsJTkiRJkiRVjPr6+tgRUjFntso5Z2Nj8b3yzVmq\ntnZY7Ai58ZR2SZIkSZIkSVXDwlOSJEmSJElS1bDwlCRJkiRJFWPu3LmxI6RizmyZM3uvvPJ07Ai5\nsfCUpG1rfcqvz2Z4vO9twfP2LclTfHGXo4BrgI9vbbh2vAY8kPMxtpX+wE3Ab4H3gHeBucBZrYz/\nBDAVeAf4M/AUcPwWHHcErX/GPtGB/ewANALPA6uBPwG/BI5sYexoYBHwIfAK8E02v2b45SVZdm/n\n+FNLxn+YHGMcsFMHXkca+wI/J3yP1gMTM97/9uZxwucmS+MI3xtJ0nZqwoQJsSOkYs5smTN7c+ZM\nih0hNy5aJEnb1qeL/twJuBo4ls3LrIUZHnPDVjz3WkL581LRtkLheSfw/lbsuz0b2Lrs5aQOOBm4\nC3ga6AJ8Efgx4b28tmjsTsBjQA/gUuAPwCjgF8DfA09uwfFHEArCYn9M+dwuwE+BzwDjCeXrLsDf\nAd1Kxl4F/CtwPfAIcDhwHdAb+KeicdOS/VwINKTMsQYoLCO5G/CPhDJ1f8J7mZWbCLlHAm8DyzLc\n9/Yqj5/javm7QZK0BaZPnx47QirmzJY5s3fOObfHjpAbC09J2rb+p+T+CsI/3Eu3l4vf0Xq2Tjkf\nO8/9dyUUaNvKdKD016cPA38FXAncAHyUbD8fGESYPTkv2fY4YXboBJqX5mktAOZvwfMgzNg8iVB0\nF38WHiwZtwfwDeD25BZCObsDofS8mU1F/vLk62TSf5/Xlxz/YcJszC8QZp++lXI/LelEKJo/BD5F\neN/v34r9FeuSfK3NaH8K8v77R5JUxrp1K/2da3kyZ7bMmb0dd6ycrB3lKe2SVH4uIRRFy4FVwHPA\nGDb/JdXfAbOScR8Cbyb3e7ex707Atwjly/lbkG0coXQDeJXNT8E/mzCz7y3Cqc8vEmb7lf6X9G8I\nJeCbSfa3gf8CDm7n+BcTisFrOpD5ccIptZ8lzCr8MzAF+Osk+xWE0vH1JPPjwABCATYhyfgn4CfA\nniX7fo1w2v2ZhO/TGkJJPLpk3IpWsv0P4b0pPqX7TMJszHlF2/4C3E2Yedir7Zfboq0phy4DnqD9\nUv4kwnt2Z8n2O5Pjn7EVGVpTeI/6Jrc9gO8QPpv/BywlzNgs/fwVLvVwEaGE/RA4L9n+SUIRW/hs\nF/bdl/A9KPy8vUgoWovf232T54whlL6vJmOPY9Np2AcSZva+T/hcTCQUogMJJe7K5HlfLcm8E3Aj\n8CybLovwFDC0hfel8PrOSV7fn4HfAKe0MHZ/wozbt5OsrwP/DuxYNGYv4AfA7wnva+FSBV1a2F8a\nHcl3SvJY4RIJpe9LQSfC3w+/Ifwc/5HwPu9XNOaLybEvKXnuOGAdYQa1JElShy1eDC+8EG5VHpzh\nKUnl55OEMvB3hH/kH0I4VXh/NpWUOwOPJmMuJpQwvQinx3dvZb87Ea6F+A+EEuHRLcj2/xNOJx5N\nKOYKp/sWZu71Ax4izOb7gFDiXEko6k4o2s+DhIJiDPAGYabjkcCurRy3M/BtwqndDYRTw9PaQHhv\n7iKckv0vNL/+3yWE2ZMXJa/tRsLsvt8SSqmRhCLrO8nrP7Nk34cQSrVrCKXRcOC7hMLoxnayHUc4\nZf0PRds+RSgYSxWugziIjp9mPYvwHr9PKHS/CbyQ4nn7EIrh+wlF+fmEcnYxoQz+UUnu4pwFbxOK\nvUEdzJzG3ya37xBKzSeAvZOszyWZ/pVQMpaWWWcAQwhl19uEUvtIwun7LxOK8EL+vyKUizWEIvM1\n4DTCZ+KTbF6gXUp4jxoJBebLbLre6b2Ez+KthEsdjCX8PB9H+NyMB75E+Ly/DPwsed5OhFm0EwnF\n4w7A5whFfEs/E6cAhyZ5/5wc56eEMv/VZMzBhGvJ/oFweY2XkvfvNMLndy2h7PwfQiHYRPg756hk\nv/uS/pIEpdLkOyF5/b8k/DKlJhm3F5uf0v4DQmn9XcLfK3sQPudPJa/zD4S/Vz9L+Ll8GngmOcY3\nCJ+Z/9rC1yJJkrZzJ5wAb74JvXvD0qWx00iSFN9UQjHYms6Ef+SfQ5jZWFgoaDChtDutnf2vB24h\nlFT/TSgXD0yRa9/kuee28NgVNJ/51ppOhOyfZdPMNghFxHo2nwVZ6jVC0fYxQqnzRzZdw7EjHk+O\nd0zJ9n2T7aWnel+abP9pyfaJyfadSzKuY/P39GHCLLyubeS6INnfqJLt/wd8v4XxRybjz25jn6VO\nJBR+JxPKvYsJn4EPWsjckk8nx3yPUGSeRSgO7022X1A09nZav0zAYkIRXmoc6Rct+oAwo7CGMNP2\nUsLM18LSkv9C+F7Uljx3WHKMk4q2rSd8nlpaeOs1Nj+d/frkOYeWbJ+cZOiX3N83GbeEzWc/jkse\nu7xk+/xk++lF27oQfonx4xbyFY+pAe4gFHfF1hNmWRd/Vj9BeH+uLNr2GGGm6B5tHOc2QlHep2R7\nY3KcgW08F8LP33NbmO9pQrlbPNt0lyTzX4q2FT6nl5UcpzehTL2haNuOhPfrd0n2t4HZeIq8JEH4\nb+iGZ555ZkO5u+KKK2JHSMWc2SrnnL17b9gA4baccxa88847G77+9R9sOPbYSzb84AcbNvu68cbw\n+DvvvBM76oZnnnmmsLZD6f/nt8lT2iWp/PwdoXBZQSgA1hJOMe1MmP0EYSbWnwiz7P4JOKCN/f0N\n8CtCUfBpsl8xufRY/0GYgVjI/njyWKEY+SOhbBgLfIXwelv679EGQrE1h1DwDkn+vCX+SMuzJmHz\na1EWFvf5eSvbS4veF9j8PZ1GOL3671o55j8QyrIfs/m1PbP0MGGW24OEmXzfB44mvLf/WjSuUE4X\nvgplXeH7shOhNP0JYRbcFwhF3TdzzF5qZ0Lpv5YwW+8mwusqzLg9lfB9+C3NX8sjhNd7bMn+ZpN+\n0a3jCd/nX5dsn0p470qL+PtpXsgVm1VyfxGhrCsuhP9C+Bkp/ax9njDb8QM2vRcNhNnfpeYQyr6C\nwkziwj67EX4JcC+hQGzNqcm+ltH8ff1F8njpLxLSai/fzsBhwH00v/7pKsJlJIoLylMJ3+N7SjIu\nJ5StxxaNXUv4/O7Bpl92/CMugiRJFaVv3/Z+714ezJktc2Zv113buhpaZbPwlKTy0pdw/c5ehBls\nQwizyi4h/AP/Y8m4lYSi4TeEUzEXEK41OY7NL1dyOGEG2r1s3cIu7dmFMIv0MMIp+Mck2Ycljxey\nbyCcRvowofR8hlB0fDfZR0EnoH+S/xeEayZuqbZOAS9drXxtO9tLZ22+3cI+C9tamjl3IqHEeZhw\n6nKpd2l5xuPuRY9vjdcJpVnx4kd3El5f4atwuYPCsRYRZtoVe4Qw62+PorE7sen7XGz3DHKvIXye\nDiXMTv04YYZz4Xvbk3DqcqEILHytTB4v/V505LIAe7QyflnR42n33dLnajWbL2q0luaftWHADML3\n4UuE79+hwA9peSZxS+/3/xWN3Y3w/4HtnXTVk3Cd0NL3dQHhZ7mt2aFtSZOvE23/fBVn7ET4e2Rt\nydcRLWT8HeEXADsRrsva0jEkSWVs9Oj2ThQqD+bMljmzd/TRF8aOkBuv4SlJ5eUMwsymYTQvmFqa\nvr8AqE/+fBAwgjDjbg3hOoAF0wkznf6NTYsW5eF4QlF7DKH4LGipvHuDTadD/y3hNO1xhNNN/znZ\nvoFw/b3/JCwyRPLYlszEynP2VkuLCO2V3JaWOicCMwmz284izIIt9Tzh+1mqcAr6gi3I2JLi9+Qa\nwqUPCgqXWfgdzWfhtbWfwmnLB9F8gaO9CIXT1uZu6fIDxd4hZG3tmpKlC0d15DPxLuHalqUK27Zm\n35DudOrhhEV7vliy/WNbcDwIxetfCNdpbcs7hFmzV7XyeEevJ5vWnwiva68WHivdtiIZO4RQmpYq\n3XYBYcbyPMKlNe6l/UW5JEmSVEGc4SlJ8W1o4c/Fs706Ae396u05wjX13qfl06j/jXDtwGvZ+sKz\nUB6UrnzdUnYIp9y35WVCvgU0z14ogX5EKHlGsunU/nIyiM0Lyn8kzCwsLujqCGXnk4Ri+6NW9vdT\nwinKhxdtqyEUXk+z9bPR/oZwWvuvira9nmQtfL2UbF9HWDTmAMLiRQWdCKfl/45NMxZ/QVhka0TJ\n8UYQPhsztzJ3e6XeLEJ5/keav5bC1xtbcez/IrwHpT9b5ya5tvRSCwVpCsv1bP6Z2Yvm1/7siDWE\nyzx8nrZnac4ilO2v0PL7mlfh+WdCCXkWYSZmQXfCzN7i96xwinufVjIWL9B1IKHc/3fC9YWfI8yc\nbW3BNEmSJFWgcvtHoyRtj4pndz1CKAynERZZOZNw6nPpP8ZPJVy/8ELCIjKfI6z6/HFaX339lmT8\nWMLp41uqMJPvMsJCOocSTkX/JWFW1m2EQu/U5HWUloEHEUq/UYTXeDxwHaGIaC37T5J9nkWYjbVD\nBzPnuSDJMsI1G0cQXs/dhO/JdYQCEMLMs5nJ2OsJM3Y/XfTVvWh/PyQUND8mzOAtLBLUj+YLuqTx\nKPA1winJxxO+Z/9NKDKvTrmPbxLKp18QZuIWruV5IGGhoII/EV7zPyW3xxAWuLqGsLr9IrZOe9/D\nmwmLIz1JuDbs3xNK5gsIhdbhrT+1XTcRLhnx82R/dYSfoYsJ10V9eSv2Da2/tuLtswjX8J1M+F6e\nR/hevtXG89s7TiPhZ2ke4XUdR/jlwj1surzENwlF61PARcmxTya89gcICwN19Lhpx11NKHUfJRS7\nZxEWWlpVMvYpwqJZdxJmt5+avJZ/JHx/LkrG7Uz4Wfpdkv8jwvU8dyP83EmSKsSiRVv7vxXbhjmz\nZc7sLV/+UvuDKpSFpyTFVVhxrmAx4R/1uxGu83gLYYbSpSXjlhAKprGEGXj3AocQSpAptO6HhOv/\n/TNhdectKQKfIJR2pxEKl3mEAu+PwCmE6xHeneRYyearii8jFEQXE0q9mcnzGgnlWEHprLeHCEVL\nYaZkS9eKbEnpe5z2OWm3zycUbF9Nch1JmE37naIxJxDy/jVhsZynir5+SfOZg2uT8XOA7xHK1J6E\nGZXFlwpI43nC9/suQmE5hjBb8VDSXxP1FcKM0JcJpdJ/sum6jj8pGfstwmv//whF/SWEz8olHcxd\nKs33cHWScyqh2H+AUHSOJlwe4rUOHKvUCuAowvfu+mTfnyMUumkv0tTaa0i7fSqhYP4HQvE6Jsny\nH608v7UMxZ4jFMHPJPt6iLCi+Ydsmqn9NuHz8khyzIcIs67PA54l/D3U3jG3NN9/EX7R0YPwvfwO\n4e+MH7Yw9iLCL1E+S/hFyyygiXBN0HnJmNsIs0A/T5jhCvAqcH5ynEtT5pQkRTZ27NjYEVIxZ7bM\nmb1Zs5piR8hNnjNeytnXCNfHG0D4H96nCLNmlrTzvGOAiYTT2t4irI78g/xiSlI0+xKKrvMJZVlL\n15pUKNGeI5R/6pgawuzBbwB7svliPpIkaduqBZ555plnqK1t6fLx5eONN96oiJWwzZmtcs65eDGs\nWwc1NdC1a/nmLFixYgU33XQfnTsfzj77HLLZ46tWreDdd+/jK18Zxp577hkh4Sbz589n8ODBAINp\n+5r+zWyvMzw/S5g1cwRhhkYNYeZC6fXoiu1HOH30CcIsqm8RZl4Na+M5klTpphBmevl3nbJ0OeFz\n9Q3yXVBKkiRVoXIvkwrMma1yzjlgAAwaFG7LOWep3XbrEztCbrbXVdr/oeT+SOAPhN9ozW3lORcR\nZvI0JvcXE07xuoJw2qkkVZM3CX/HFbwSK0gbutD2mQobCKtQ5ylWWdeJ8PrbUs6zcu8hXGuz4P1Y\nQSRJkiRVn+218CxVWAykrdPpjiTMAi32COF0zy7k/49qSdqWPqIDpwtE8hhhxn5rXiOsSJ6n/XLe\nf2vuJKwQ3poNtF+IxvRO8iVJkiRJmbPwDLNkbiIsBNHWAg49geUl25YT3sM9W3gMoFfyJUnK3s2E\nRXRas5Ywc78a3cvmv4QrVa2vXZKkbW1Z8qUyMX78eK688srYMdplzmyZM3uzZ9/C0KH/GjtGLiw8\nYRIwCBiS8X577b333m+99dZbGe9WkiRJkrQNLQROwNKzbKxevTp2hFTMmS1zZm/t2jWxI+Rmey88\nvwecSjglsr1m8m1gr5JtPQnXSFvRwvheb731FnfffTcDBw7c6qBSubn88su5+eabY8eQcuHnW9XM\nz7eqmZ9vZW3hwoUMHz58IOHMPQvPMtHU1BQ7QirmzJY5s3fSSZUxE3VLbK+FZydC2Xk6cCzweorn\n/Ao4rWRbHfC/tHH9zoEDB1Jb61mFqj677rqrn21VLT/fqmZ+vlXN/HxLkiTYfgvPyUA9ofD8M5tm\nbr4HfJj8+Xpgb+C85P5twCjgRuAOwiJGDcAXt01kSZIkSZIklZuJE2HlSujRAxobY6cRQOfYASK5\nCOgBPE44lb3w9YWiMXsB+xTdfw04mTAj9FngKmA08NO8w0qSJEmSpGDFipauKld+zJmtcs45cSI0\nNYXbcs5ZatWqd2NHyM32Wnh2Brokt8VfPyoaMxI4vuR5TwKDgY8Bn6Tt1YElSZIkSVLGGhoaYkdI\nxZzZMmf2Zsy4LHaE3GyvhaekrVRfXx87gpQbP9+qZn6+Vc38fEvbh3HjxsWOkIo5s2XO7J144pjY\nEXJj4Slpi/gPClUzP9+qZn6+Vc38fEvbh0pZnMyc2TJn9vr0OTh2hNxsr4sWSZIkSdoOvPTSS3zw\nwQexY6hMde/enX79+sWOIUnKmIWnJEmSpKr00ksv0b9//9gxVOaWLFli6SlJVcbCU5IkSVJVKszs\nvPvuuxk4cGDkNCo3CxcuZPjw4c4ArkBTpkzh/PPPjx2jXebMljmzN2/e3ZxwwuWxY+TCwlOSJElS\nVRs4cGBFXVNNUtvmz59fEYWSObNVzjn794ePfxx69izvnKWWLn0udoTcWHhKkiRJkqSKMXny5NgR\nUjFntso55+zZxffKN2eps86aEDtCblylXZIkSZIkSVLVsPCUJEmSJEmSVDUsPCVJkiRJkiRVDQtP\nSZIkSaogU6dOpXPnzhu/dthhB/bZZx8aGhp46623MjvO2rVrueiii+jVqxc1NTUbF37ad999Oe20\n0zI7DsCIESPYb7/9Mt2nqtfQoUNjR0jFnNkyZ/amTBkeO0JuXLRIkiRJkirQ1KlT2X///VmzZg1P\nPPEE119/PU888QQLFiyga9euW73/W2+9ldtvv51JkyYxePBgdtllFwA6depEp06dtnr/pfLYp6rT\nqFGjYkdIxZzZMmf2hgypjNXkt4SFpyRJkiRVoE996lMbZ10ec8wx/OUvf+Haa69l5syZ1NfXb/F+\n16xZQ9euXVmwYAHdunXj4osvbvb4hg0btip3a/Lar6pPXV1d7AipmDNb5szegAHHxY6QG09plyRJ\nkqQqcMQRRwDw+uuvA/D973+fQw45hG7durH77rvz+c9/nldffbXZc4499lgOPPBAnnzySY466ih2\n3nlnGhoa6Ny5M1OmTGH16tUbT53/0Y9+1OJxX3vtNTp37syNN97IxIkT2W+//ejevTtHHXUU8+bN\n22z81KlTGTBgAB/72Mc44IADuOuuu1rc79q1a7nuuuvYf//9+djHPsYnPvEJGhoaWLFixcYxN9xw\nA126dGHWrFnNnjtixAh23nlnXnjhhfRvoCSpalh4SpIkSVIVePnllwH4q7/6K7785S/zla98hbq6\nOn72s5/x/e9/nxdeeIGjjjqKP/zhDxuf06lTJ5YtW8Y555zD8OHDeeihh7jkkkt4+umnOfnkk+na\ntStPP/00Tz/9NKecckqbx588eTKPPfYYt9xyC/fccw9//vOfOfnkk1m5cuXGMVOnTqWhoYFBgwZx\n33338Y1vfINrr72WOXPmNDulff369Zx++umMHz+e4cOH8+CDD3LDDTfw6KOPcuyxx/Lhhx8C8C//\n8i+cdNJJnHfeebzxxhsA3HnnnfzoRz/ie9/7HoMGDcrs/ZWk1hx/PAwaFG5VHjylXZIkSZKA1ath\n0aJ8j7H//tCtWzb7WrduHevWrePDDz/kiSee4LrrrqN79+7069ePCy+8kJtuuonLLrts4/ijjz6a\n/v37M3HiRG644QYgnEb+xz/+kZ/85Cccc8wxzfa/55570rlzZw4//PBUeXr06MGsWbM2Fpd77703\nhx9+OA899BBnn30269ev56qrruLQQw/lvvvu2/i8IUOG0K9fP3r37r1x27333svDDz/MT3/6U04/\n/fSN2w8++GAOO+wwpk6dykUXXQTAXXfdxSGHHMIXvvAFbr31VkaNGsXw4cNpaGjo4DuqSjFz5kzO\nOOOM2DHaZc5slXPOJUvgzTfh/ffLO2ep559/kCOPPDd2jFxYeEqSJEkSoewcPDjfYzzzDCSX3dxq\nn/70p5vdP+igg7j11lv5+c9/TqdOnfjSl77EunXrNj7es2dPDjroIB5//PFmz9t99903Kzu3xCmn\nnNJsluaBBx4IsHHm5eLFi1m2bBlXXHFFs+f17duXo446auOp+ACzZs1it91245RTTmn2Gg4++GB6\n9uzJ448/vrHw3H333ZkxYwbHHHMMn/nMZ9h333257bbbtvr1qHxNmzatIgolc2bLnNl79tn7LDwl\nSZIkqZrtv38oJPM+RlbuuusuBg4cSE1NDT179qRnz54A/PCHP2TDhg184hOfaPF5n/zkJ5vd79Wr\nVyZ59thjj2b3d9ppJyAsggTw7rvvArDXXntt9tyePXvy2muvbby/fPly/vSnP7Hjjju2eKzCvgoO\nP/xwDjjgAJ577jkuvvhiumU1jVZlacaMGbEjpGLObJkze+eee0fsCLmx8JQkSZIkwqnmWc2+3BYG\nDhy4cZX2YnvuuSedOnVi7ty5G0vHYqXbimdl5qlQiC5btmyzx95+++1mOfbcc0/22GMPHn744Rb3\n1b1792b3r7nmGhYsWMChhx7K1Vdfzamnnsq+++6bXXhJUkVx0SJJkiRJqiKnnXYaGzZsYOnSpdTW\n1m721ZGFfLIsQwcMGECvXr2YNm1as+2vv/46Tz31VLNtp512Gu+++y7r1q1r8TX069dv49hHH32U\nG264gauvvppHHnmEj3/843zhC1/go48+yiy7JKmyOMNTkiRJkqrIUUcdxZe//GVGjhzJr3/9a44+\n+mh23nlnli1bxty5cznooIM2Xv8SwsJFrWnrsY7q3Lkz1157LRdccAFnnnkmF1xwAe+99x5NTU30\n6tWr2bG++MUvcs8993DyySdz2WWXcdhhh7HDDjuwdOlSHn/8cU4//XTOOOMMli1bxvDhwznuuOO4\n5pprgHA66dFHH83YsWO56aabMssvSaoczvCUJEmSpArT3szL2267jUmTJvHkk09SX1/PqaeeyjXX\nXMOaNWs44ogjmu2ntX219tjWzPpsaGjgjjvu4MUXX+Sss87iuuuu46qrruL4449vtt/OnTtz//33\n8/Wvf5377ruPYcOGceaZZzJ+/Hi6du3KQQcdxPr166mvr6dLly7cc889G597xBFHcP3113PLLbdw\n//33b3FWla+RI0fGjpCKObNlzuxNnz46doTcOMNTkiRJkirIiBEjGDFiRCbj5syZ0+pjd955J3fe\needm21999dVm9/fdd1/Wr1/f4j5a2t7Q0EBDQ0Ozbeedd95m47p06UJjYyONjY2tZixdcb7gq1/9\nKl/96ldbfZ4qW11dXewIqZgzW+Wcs7ERVq6EHj2gV6/yzVmqf//jYkfIjYWnJEmSJEmqGPX19bEj\npGLObJVzzua/lynfnKVqa4fFjpAbT2mXJEmSJEmSVDUsPCVJkiRJkiRVDQtPSZIkSZJUMebOnRs7\nQirmzJY5s/fKK0/HjpAbC09JkiRJklQxJkyYEDtCKubMljmzN2fOpNgRcmPhKUmSJEmSKsb06dNj\nR0jFnNkyZ/bOOef22BFyY+EpSZIkSZIqRrdu3WJHSMWc2TJn9nbcsXKydlRN7ACSJEmSlKeFCxfG\njqAy5OdCUlYWL4Z166CmBgYMiJ1GYOEpSZIkqUp1794dgOHDh0dOonJW+JxI0pY64QR4803o3RuW\nLoX333+fjz76qM3n7LDDDnz84x/fRgm3PxaekiRJkqpSv379WLJkCR98wTpcWgAAIABJREFU8EHs\nKCpT3bt3p1+/frFjqIPGjBnDt7/97dgx2mXObFVKzksvvZR99vkU773X9rhdd4Uvf/nsqKXnAw+M\no76+Ohcu2p4Lz88CY4BaoBdwJvCzNsYfC8xuYfv+wJKsw0mSJEnaepZZUvXp27dv7AipmDNblZKz\nV69evPcedO16PN267drimNWr3+O992a3Ows0b7vu2jvq8fO0PS9a1A14Frgkub8h5fP6AXsVfb2c\nfTRJkiRJktSS0aNHx46QijmzVSk5L7zwQgC6dduVXXbZs8Wv1orQbe3ooy+MHSE32/MMz18kXx21\nAng/4yySJEmSJEmSMrA9z/DcUs8CbwH/RTjNXZIkSZIkSVKZsPBM7y3gQmBY8rUYeAwYEjOUJEmS\nJEnbk0WLFsWOkIo5s1UpOV966aXYEVJbvrxysnaUhWd6S4ApwG+ApwnX/vw5YeEjSZIkSZK0DYwd\nOzZ2hFTMma1KydnU1BQ7QmqzZlVO1o7anq/hmYV5wJfaGnD55Zez667NL0ZbX19PfX19nrkkSZIk\nSR0wbdo0pk2b1mzbe++9FymN2jJp0qTYEVIxZ7bKOedjj8G6dVBTA2vW3MCPf/w/sSOlMmzYDbEj\n5MbCc+v8HeFU91bdfPPN1NbWbqM4kiRJkqQt0dLElPnz5zN48OBIidSavn37xo6QijmzVc45BwzY\n9OcVK/oAlVF47rZbn9gRcrM9F547A/2K7v8NcAjwLvB74Hpgb+C85PHLgVeBF4EdgeFsup6nJEmS\nJEmSpDKwPReehwGzkz9vACYmf54KNAB7AfsUjd8B+DbQB1gDLABOBn6xDbJKkiRJkiRJSmF7XrTo\nccLr7wx0KfpzQ/L4SOD4ovHfBvoD3YA9gGOw7JQkSZIkaZsaP3587AipmDNblZLzlltuiR0htdmz\nKydrR23PhackSZIkSaowq1evjh0hFXNmq1JyrlmzJnaE1NaurZysHWXhKUmSJEmSKkZTU1PsCKmY\nM1uVkvPKK6+MHSG1k06qnKwdZeEpSZIkSZIkqWpsz4sWSZIkSZL+X3v3H2dXXd95/E2AEEJCgsES\nEKNSQIEiNlSKLiBEDa4PjFtksUGkEKutFSrkUXDrrj5CbVWgTaiFllLi1l1KyK7lly7yS7ACQlYJ\nv7oOxhJUkCSSlEkISSQk7h9n8mCYzCRnknvne8/M8/l43MfMvffcm1e+DJO5nznnHgB2yty5yZo1\nyd57J2edVbqGxB6eAAAAQIOsXLmydEItOlurkzvnzk0uvrj6uGrVqtI5ta1d25zWwTLwBAAAABpj\n1qxZpRNq0dlaTen89Kc/XTqhtoULm9M6WAaeAAAAQGPMmTOndEItOlurKZ0XXnhh6YTaTj65Oa2D\nZeAJAAAANMbUqVNLJ9Sis7Wa0nnUUUeVTqjtwAOb0zpYBp4AAAAAwLBh4AkAAAAADBsGngAAAEBj\nzJ8/v3RCLTpbqymd1157bemE2hYtak7rYBl4AgAAAI2xePHi0gm16GytTu489NDk8MOrj4899ljp\nnNqeeaY5rYO1W+kAAAAAgLquvPLK0gm16GytTu68++5XPl+58tLMm3dDuZhB+NCHLi2d0Db28AQA\nAAAAhg0DTwAAAABg2DDwBAAAAACGDQNPAAAAoDFmzJhROqEWna3VlM4zzzyzdEJt8+c3p3WwDDwB\nAACAxjj33HNLJ9Sis7Wa0vmxj32sdEJtxx3XnNbBMvAEAAAAGmP69OmlE2rR2VpN6TzppJNKJ9T2\n5jc3p3WwDDwBAAAAgGHDwBMAAAAAdtC0ackRR1Qf6QwGngAAAEBj3HTTTaUTatHZWp3cuWRJ8sMf\nVh9vvfXW0jm1Pf54c1oHy8ATAAAAaIwFCxaUTqhFZ2s1pfOGG24onVDbww83p3WwDDwBAACAxli4\ncGHphFp0tlZTOq+55prSCbWddVZzWgfLwBMAAAAAGDYMPAEAAACAYcPAEwAAAAAYNgw8AQAAgMY4\n55xzSifUorO1mtJ53nnnlU6o7frrm9M6WLuVDgAAAACoa/r06aUTatHZWp3cOXt2smZNsvfeybhx\nJ+WnPy1dVM+hh55UOqFtDDwBAACAxpg5c2bphFp0tlYnd86e/crnK1eemnnzbigXMwhTp55aOqFt\nHNIOAAAAAAwbBp4AAAAAwLBh4AkAAAA0xn333Vc6oRadrdWUzgcffLB0Qm1LlzandbBG8sDzhCTf\nSPLzJJuTfLDGY96V5KEk65M8meQP2lYHAAAAbOXSSy8tnVCLztZqSucVV1xROqG2e+5pTutgjeST\nFo1N8nCS+UluSPKr7Wz/piS3Jvn7JGckOS7J3yZ5rufxAAAAQJtdf/31pRNq0dlandS5evXqbNy4\nsd/7vvzlL+faa7+73efYsGF9Vq1aNeD9u+++eyZMmLDDjXV89KNXt/X5SxrJA8/bei51/WGSnyTZ\ncu6tHyX5rSR/EgNPAAAAGBJjx44tnVCLz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IxM3/O8TN/OFEfzrnU7w/0Wxm5mVEAO5oIsi2M9Hs9fC8ZSYk70s7BZ8NXE/r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\nTjq8xtTwMGBvHY/PNG1E6SBnA/8J/JFcDV7UgG08/qo5aWwND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RnRh9nri+GkdcL/yHuL9YJSP2a4nv4YOI758XyX2XQ1y3/JW4rvoBcf19OPFd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\na9KOEFsINbz11tq0I8QWwjxAOHUkpZQbo18FngHeA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O5eeAv8ssNzOJ8U/E9eRQIum4HriI+NHndOLYTRPn2f5Y+5Jp9wG/BI4irq83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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Parse the RT-App generate log files to compute performance metrics\n", + "pa = PerfAnalysis(te.res_dir)\n", + "\n", + "# For each task which has generated a logfile, plot its performance metrics\n", + "for task in pa.tasks():\n", + " pa.plotPerf(task, \"Performance plots for task [{}] \".format(task))" + ] + } + ], + "metadata": { + "celltoolbar": "Raw Cell Format", + "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": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ipynb/trappy/example_custom_events.ipynb b/ipynb/trappy/example_custom_events.ipynb deleted file mode 100644 index 2b1489d0..00000000 --- a/ipynb/trappy/example_custom_events.ipynb +++ /dev/null @@ -1,716 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import logging\n", - "from conf import LisaLogging\n", - "LisaLogging.setup()\n", - "\n", - "# Uncomment the follwing line to enabled 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", - "%matplotlib inline\n", - "\n", - "import copy\n", - "import json\n", - "import os\n", - "import time\n", - "\n", - "# Support to access the remote target\n", - "import devlib\n", - "from env import TestEnv\n", - "\n", - "# Support to configure and run RTApp based workloads\n", - "from wlgen import RTA\n", - "\n", - "# Support for performance analysis of RTApp workloads\n", - "from perf_analysis import PerfAnalysis\n", - "\n", - "# Support for trace events analysis\n", - "from trace import Trace\n", - "\n", - "# Suport for FTrace events parsing and visualization\n", - "import trappy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test environment setup" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup a target configuration\n", - "my_target_conf = {\n", - " \n", - " # Define the kind of target platform to use for the experiments\n", - " \"platform\" : 'linux', # Linux system, valid other options are:\n", - " # android - access via ADB\n", - " # linux - access via SSH\n", - " # host - direct access\n", - " \n", - " # Preload settings for a specific target\n", - " \"board\" : 'juno', # load JUNO specific settings, e.g.\n", - " # - HWMON based energy sampling\n", - " # - Juno energy model\n", - " # valid options are:\n", - " # - juno - JUNO Development Board\n", - " # - tc2 - TC2 Development Board\n", - " # - oak - Mediatek MT63xx based target\n", - "\n", - " # Define devlib module to load\n", - " \"modules\" : [\n", - " 'bl', # enable big.LITTLE support\n", - " 'cpufreq', # enable CPUFreq support\n", - " ],\n", - "\n", - " # Account to access the remote target\n", - " \"host\" : '192.168.0.10',\n", - " \"username\" : 'root',\n", - " \"password\" : '',\n", - "\n", - "\n", - " # Comment the following line to force rt-app calibration on your target\n", - " \"rtapp-calib\" : {\n", - " '0': 361, '1': 138, '2': 138, '3': 352, '4': 360, '5': 353\n", - " }\n", - "\n", - "}\n", - "\n", - "# Setup the required Test Environment supports\n", - "my_tests_conf = {\n", - " \n", - " # Binary tools required to run this experiment\n", - " # These tools must be present in the tools/ folder for the architecture\n", - " \"tools\" : ['trace-cmd'],\n", - " \n", - " # FTrace events buffer configuration\n", - " # events listed here MUST be \n", - " \"ftrace\" : {\n", - " \n", - " \n", - "##############################################################################\n", - "# EVENTS SPECIFICATIPON\n", - "##############################################################################\n", - "# Here is where we specify the list of events we are interested into:\n", - "# Events are of two types:\n", - "# 1. FTrace tracepoints that _must_ be supported by the target's kernel in use.\n", - "# These events will be enabled at ftrace start time, thus if the kernel does\n", - "# not support one of them, ftrace starting will fails.\n", - "\n", - " \"events\" : [\n", - " \"sched_switch\",\n", - " \"cpu_frequency\",\n", - " ],\n", - "\n", - "# 2. FTrace events generated via trace_printk, from either kernel or user\n", - "# space. These events are different from the previous because they do not\n", - "# need to be explicitely enabled at ftrace start time.\n", - "# It's up to the user to ensure that the generated events satisfies these\n", - "# formatting requirements:\n", - "# a) the name must be a unique word into the trace\n", - "# b) values must be reported as a sequence of key=value paires\n", - "# For example, a valid custom event string is:\n", - "# my_math_event: kay1=val1 key2=val2 key3=val3\n", - "\n", - " \"custom\" : [\n", - " \"my_math_event\",\n", - " ],\n", - " \n", - "# For each of these events, TRAPpy will generate a Pandas dataframe accessible\n", - "# via a TRAPpy::FTrace object, whith the same name of the event.\n", - "# Thus for example, ftrace.my_math_event will be the object exposing the\n", - "# dataframe with all the event matching the \"my_math_event\" unique word.\n", - " \n", - "##############################################################################\n", - " \n", - " \"buffsize\" : 10240,\n", - " },\n", - "\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "05:26:51 INFO : Target - Using base path: /home/derkling/Code/schedtest\n", - "05:26:51 INFO : %14s - Loading custom (inline) target configuration\n", - "05:26:51 INFO : Target - Connecting linux target with: {'username': 'root', 'host': '192.168.0.10', 'password': ''}\n", - "05:26:55 INFO : Target - Initializing target workdir [/root/devlib-target]\n", - "05:26:58 INFO : Target topology: [[0, 3, 4, 5], [1, 2]]\n", - "05:27:00 INFO : FTrace - Enabled events:\n", - "05:27:00 INFO : FTrace - ['sched_switch', 'cpu_frequency']\n", - "05:27:00 INFO : EnergyMeter - HWMON module not enabled\n", - "05:27:00 WARNING : EnergyMeter - Energy sampling disabled by configuration\n" - ] - } - ], - "source": [ - "# Initialize a test environment using:\n", - "# the provided target configuration (my_target_conf)\n", - "# the provided test configuration (my_test_conf)\n", - "te = TestEnv(target_conf=my_target_conf, test_conf=my_tests_conf)\n", - "target = te.target" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "05:27:00 INFO : Target ABI: arm64, CPus: ['A53', 'A57', 'A57', 'A53', 'A53', 'A53']\n" - ] - } - ], - "source": [ - "logging.info(\"Target ABI: %s, CPus: %s\",\n", - " target.abi,\n", - " target.cpuinfo.cpu_names)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example of custom event definition" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "05:27:05 INFO : Generating events from user-space (will take ~140[s])...\n" - ] - } - ], - "source": [ - "# Define the format string for the custom events we will inject from user-space\n", - "my_math_event_fmt = \"my_math_event: sin={} cos={}\"\n", - "\n", - "# Start FTrace\n", - "te.ftrace.start()\n", - "\n", - "# Let's generate some interesting \"custom\" events from userspace\n", - "logging.info('Generating events from user-space (will take ~140[s])...')\n", - "for angle in range(360):\n", - " v_sin = int(1e6 * math.sin(math.radians(angle)))\n", - " v_cos = int(1e6 * math.cos(math.radians(angle)))\n", - " my_math_event = my_math_event_fmt.format(v_sin, v_cos)\n", - " # custom events can be generated either from userspace, like in this\n", - " # example, or also from kernelspace (using a trace_printk call)\n", - " target.execute('echo {} > /sys/kernel/debug/tracing/trace_marker'\\\n", - " .format(my_math_event))\n", - "\n", - "# Stop FTrace\n", - "te.ftrace.stop()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Collect the generate trace\n", - "trace_file = '/tmp/trace.dat'\n", - "te.ftrace.get_trace(trace_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "05:29:37 INFO : Collected events spans a 146.767 [s] time interval\n" - ] - } - ], - "source": [ - "# Parse trace\n", - "events_to_parse = my_tests_conf['ftrace']['events'] + my_tests_conf['ftrace']['custom']\n", - "trace = Trace(te.platform, '/tmp', events_to_parse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Inspection of the generated TRAPpy FTrace object" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Get the TRAPpy FTrace object which has been generated from the trace parsing\n", - "ftrace = trace.ftrace" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - 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" - ], - "text/plain": [ - " __comm __cpu __pid cpu frequency\n", - "Time \n", - "0.000000 kworker/0:2 0 30494 0 575000\n", - "0.000009 kworker/0:2 0 30494 3 575000\n", - "0.000014 kworker/0:2 0 30494 4 575000\n", - "0.000019 kworker/0:2 0 30494 5 575000\n", - "0.016023 kworker/0:2 0 30494 0 450000\n", - "0.016034 kworker/0:2 0 30494 3 450000\n", - "0.016042 kworker/0:2 0 30494 4 450000\n", - "0.016048 kworker/0:2 0 30494 5 450000\n", - "0.017447 kworker/2:2 2 30600 1 450000\n", - "0.017458 kworker/2:2 2 30600 2 450000" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logging.info(\"First 10 events of our 'cpu_frequency' tracepoint:\")\n", - "ftrace.cpu_frequency.data_frame.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plotting tracepoint and/or custom events" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "code_folding": [], - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/derkling/.local/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", - " warnings.warn(self.msg_depr % (key, alt_key))\n" - ] - }, - { - "data": { - "image/png": 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GwtbWFqmpqfoeRp3z9PRETEyMvodRJxjIISIiIiIiIoPRo0cPtGnTBgqFAp06dYKvry8e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\nd1z2gcM5NH344Z5+tpPUiSd6W36V1zxO+5nTvjF1+unFy3D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bicGgPXS8f2Ob+je2KRTK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HLkAJiLU76HUCije39IrhygVk75HkKZkH40fi+NPD6TGAzqrnsWaMVRafKKAS67LXyr9gSyOabI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\nsWJ8pKwb5z1Fu5mbg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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# It is possible to mix in the same plot tracepoints and custom events\n", - "\n", - "# The LinePlot module requires to specify a list of signals to plot.\n", - "# Each signal is defined as:\n", - "# :\n", - "# where:\n", - "# is one of the events collected from the trace by the FTrace object\n", - "# is one of the column of the previously defined event\n", - "my_signals = [\n", - " 'cpu_frequency:frequency',\n", - " 'my_math_event:sin',\n", - " 'my_math_event:cos'\n", - "]\n", - "\n", - "# These two paramatere are passed to the LinePlot call as long with the\n", - "# TRAPpy FTrace object\n", - "trappy.LinePlot(\n", - " ftrace, # FTrace object\n", - " signals=my_signals, # Signals to be plotted\n", - " drawstyle='steps-post', # Plot style options\n", - " marker = '+'\n", - ").view()" - ] - } - ], - "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 -} diff --git a/ipynb/utils/executor_example.ipynb b/ipynb/utils/executor_example.ipynb deleted file mode 100644 index cc8fbe39..00000000 --- a/ipynb/utils/executor_example.ipynb +++ /dev/null @@ -1,409 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import logging\n", - "from conf import LisaLogging\n", - "LisaLogging.setup()" - ] - }, - { - "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": [ - "%pylab inline\n", - "\n", - "import datetime\n", - "import devlib\n", - "import os\n", - "import json\n", - "import pandas as pd\n", - "import re\n", - "import subprocess\n", - "import trappy\n", - "from trappy.plotter.Utils import get_trace_event_data\n", - "\n", - "import matplotlib.gridspec as gridspec\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Support to access the remote target\n", - "#import devlib\n", - "#from env import TestEnv\n", - "\n", - "from env import TestEnv\n", - "from executor import Executor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Target Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:16:08 INFO : Target - Using base path: /home/bjackman/sources/lisa\n", - "06:16:08 INFO : Target - Loading custom (inline) target configuration\n", - "06:16:08 INFO : Target - Devlib modules to load: ['bl', 'cpufreq']\n", - "06:16:08 INFO : Target - Connecting linux target:\n", - "06:16:08 INFO : Target - username : brendan\n", - "06:16:08 INFO : Target - host : 192.168.0.1\n", - "06:16:08 INFO : Target - password : \n", - "06:16:08 INFO : Target - Connection settings:\n", - "06:16:08 INFO : Target - {'username': 'brendan', 'host': '192.168.0.1', 'password': ''}\n", - "06:16:14 INFO : Target - Initializing target workdir:\n", - "06:16:14 INFO : Target - /home/brendan/devlib-target\n", - "06:16:18 INFO : Target - Topology:\n", - "06:16:18 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n", - "06:16:23 INFO : FTrace - Enabled tracepoints:\n", - "06:16:23 INFO : FTrace - sched_switch\n", - "06:16:23 INFO : FTrace - sched_wakeup\n", - "06:16:23 INFO : FTrace - sched_wakeup_new\n", - "06:16:23 INFO : FTrace - cpu_frequency\n", - "06:16:23 WARNING : TestEnv - Wipe previous contents of the results folder:\n", - "06:16:23 WARNING : TestEnv - /home/bjackman/sources/lisa/results/ExecutorExample\n", - "06:16:23 INFO : TestEnv - Set results folder to:\n", - "06:16:23 INFO : TestEnv - /home/bjackman/sources/lisa/results/ExecutorExample\n", - "06:16:23 INFO : TestEnv - Experiment results available also in:\n", - "06:16:23 INFO : TestEnv - /home/bjackman/sources/lisa/results_latest\n" - ] - } - ], - "source": [ - "# Setup a target configuration\n", - "env = TestEnv({\n", - " \n", - " # Target platform and board\n", - " \"platform\" : 'linux',\n", - " \"board\" : 'aboard',\n", - " \n", - " # Target board IP/MAC address\n", - " \"host\" : '192.168.0.1',\n", - " \n", - " # Login credentials\n", - " \"username\" : 'brendan',\n", - " \"password\" : '',\n", - " \n", - " # Folder where all the results will be collected\n", - " \"results_dir\" : \"ExecutorExample\",\n", - " \n", - " # FTrace events to collect for all the tests configuration which have\n", - " # the \"ftrace\" flag enabled\n", - " \"ftrace\" : {\n", - " \"events\" : [\n", - " \"sched_switch\",\n", - " \"sched_wakeup\",\n", - " \"sched_wakeup_new\",\n", - " \"cpu_frequency\",\n", - " ],\n", - " \"buffsize\" : 80 * 1024,\n", - " },\n", - " \n", - " # Tools required by the experiments\n", - " \"tools\" : [ 'trace-cmd', 'perf' ],\n", - " \n", - " # Modules required by these experiments\n", - " \"modules\" : [ 'bl', 'cpufreq' ],\n", - "\n", - "})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tests Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:16:23 INFO : Target - Loading custom (inline) test configuration\n", - "06:16:23 INFO : \n", - "06:16:23 INFO : ################################################################################\n", - "06:16:23 INFO : Executor - Experiments configuration\n", - "06:16:23 INFO : ################################################################################\n", - "06:16:23 INFO : Executor - Configured to run:\n", - "06:16:23 INFO : Executor - 2 target configurations:\n", - "06:16:23 INFO : Executor - base, eas\n", - "06:16:23 INFO : Executor - 2 workloads (1 iterations each)\n", - "06:16:23 INFO : Executor - rta, perf\n", - "06:16:23 INFO : Executor - Total: 4 experiments\n", - "06:16:23 INFO : Executor - Results will be collected under:\n", - "06:16:23 INFO : Executor - /home/bjackman/sources/lisa/results/ExecutorExample\n" - ] - } - ], - "source": [ - "executor = Executor(env, {\n", - " # Platform configurations to test\n", - " \"confs\" : [\n", - " {\n", - " \"tag\" : \"base\",\n", - " \"flags\" : \"ftrace\", # Enable FTrace events\n", - " \"sched_features\" : \"NO_ENERGY_AWARE\", # Disable EAS\n", - " \"cpufreq\" : { # Use PERFORMANCE CpuFreq\n", - " \"governor\" : \"performance\",\n", - " },\n", - " },\n", - " {\n", - " \"tag\" : \"eas\",\n", - " \"flags\" : \"ftrace\", # Enable FTrace events\n", - " \"sched_features\" : \"ENERGY_AWARE\", # Enable EAS\n", - " \"cpufreq\" : { # Use PERFORMANCE CpuFreq\n", - " \"governor\" : \"performance\",\n", - " },\n", - " },\n", - " ],\n", - " \n", - " # Workloads to run (on each platform configuration)\n", - " \"wloads\" : {\n", - " # Run hackbench with 1 group using pipes\n", - " \"perf\" : {\n", - " \"type\" : \"perf_bench\",\n", - " \"conf\" : {\n", - " \"class\" : \"messaging\",\n", - " \"params\" : {\n", - " \"group\" : 1,\n", - " \"loop\" : 10,\n", - " \"pipe\" : True,\n", - " \"thread\": True,\n", - " }\n", - " }\n", - " },\n", - " # Run a 20% duty-cycle periodic task\n", - " \"rta\" : {\n", - " \"type\" : \"rt-app\",\n", - " \"loadref\" : \"big\",\n", - " \"conf\" : {\n", - " \"class\" : \"profile\",\n", - " \"params\" : {\n", - " \"p20\" : {\n", - " \"kind\" : \"Periodic\",\n", - " \"params\" : {\n", - " \"duty_cycle_pct\" : 20,\n", - " },\n", - " },\n", - " },\n", - " },\n", - " },\n", - " },\n", - " \n", - " # Number of iterations for each workload\n", - " \"iterations\" : 1,\n", - "})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tests execution" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:16:23 INFO : \n", - "06:16:23 INFO : ################################################################################\n", - "06:16:23 INFO : Executor - Experiments execution\n", - "06:16:23 INFO : ################################################################################\n", - "06:16:23 INFO : \n", - "06:16:23 INFO : ================================================================================\n", - "06:16:23 INFO : TargetConfig - configuring target for [base] experiments\n", - "06:16:25 INFO : SchedFeatures - Set scheduler feature: NO_ENERGY_AWARE\n", - "06:16:26 INFO : CPUFreq - Configuring all CPUs to use [performance] governor\n", - "06:16:27 INFO : WlGen - Setup new workload rta\n", - "06:16:27 INFO : RTApp - Workload duration defined by longest task\n", - "06:16:27 INFO : RTApp - Default policy: SCHED_OTHER\n", - "06:16:27 INFO : RTApp - ------------------------\n", - "06:16:27 INFO : RTApp - task [task_p200], sched: using default policy\n", - "06:16:27 INFO : RTApp - | calibration CPU: 1\n", - "06:16:27 INFO : RTApp - | loops count: 1\n", - "06:16:27 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n", - "06:16:27 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n", - "06:16:27 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n", - "06:16:28 INFO : WlGen - Setup new workload perf\n", - "06:16:28 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "06:16:28 INFO : Executor - Experiment 0/4, [base:rta] 1/1\n", - "06:16:28 WARNING : Executor - FTrace events collection enabled\n", - "06:16:36 INFO : WlGen - Workload execution START:\n", - "06:16:36 INFO : WlGen - /home/brendan/devlib-target/bin/rt-app /home/brendan/devlib-target/run_dir/rta_00.json 2>&1\n", - "06:16:44 INFO : Executor - Collected FTrace binary trace:\n", - "06:16:44 INFO : Executor - /rtapp:base:rta/1/trace.dat\n", - "06:16:44 INFO : Executor - Collected FTrace function profiling:\n", - "06:16:44 INFO : Executor - /rtapp:base:rta/1/trace_stat.json\n", - "06:16:44 INFO : --------------------------------------------------------------------------------\n", - "06:16:44 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "06:16:44 INFO : Executor - Experiment 1/4, [base:perf] 1/1\n", - "06:16:44 WARNING : Executor - FTrace events collection enabled\n", - "06:16:52 INFO : WlGen - Workload execution START:\n", - "06:16:52 INFO : WlGen - /home/brendan/devlib-target/bin/perf bench sched messaging --pipe --thread --group 1 --loop 10\n", - "06:16:53 INFO : PerfBench - Completion time: 0.007000, Performance 142.857143\n", - "06:16:58 INFO : Executor - Collected FTrace binary trace:\n", - "06:16:58 INFO : Executor - /perf_bench_messaging:base:perf/1/trace.dat\n", - "06:16:58 INFO : Executor - Collected FTrace function profiling:\n", - "06:16:58 INFO : Executor - /perf_bench_messaging:base:perf/1/trace_stat.json\n", - "06:16:58 INFO : --------------------------------------------------------------------------------\n", - "06:16:58 INFO : \n", - "06:16:58 INFO : ================================================================================\n", - "06:16:58 INFO : TargetConfig - configuring target for [eas] experiments\n", - "06:17:00 INFO : SchedFeatures - Set scheduler feature: ENERGY_AWARE\n", - "06:17:01 INFO : CPUFreq - Configuring all CPUs to use [performance] governor\n", - "06:17:02 INFO : WlGen - Setup new workload rta\n", - "06:17:02 INFO : RTApp - Workload duration defined by longest task\n", - "06:17:02 INFO : RTApp - Default policy: SCHED_OTHER\n", - "06:17:02 INFO : RTApp - ------------------------\n", - "06:17:02 INFO : RTApp - task [task_p200], sched: using default policy\n", - "06:17:02 INFO : RTApp - | calibration CPU: 1\n", - "06:17:02 INFO : RTApp - | loops count: 1\n", - "06:17:02 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n", - "06:17:02 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n", - "06:17:02 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n", - "06:17:03 INFO : WlGen - Setup new workload perf\n", - "06:17:03 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "06:17:03 INFO : Executor - Experiment 0/4, [base:rta] 1/1\n", - "06:17:03 WARNING : Executor - FTrace events collection enabled\n", - "06:17:11 INFO : WlGen - Workload execution START:\n", - "06:17:11 INFO : WlGen - /home/brendan/devlib-target/bin/rt-app /home/brendan/devlib-target/run_dir/rta_00.json 2>&1\n", - "06:17:19 INFO : Executor - Collected FTrace binary trace:\n", - "06:17:19 INFO : Executor - /rtapp:base:rta/1/trace.dat\n", - "06:17:19 INFO : Executor - Collected FTrace function profiling:\n", - "06:17:19 INFO : Executor - /rtapp:base:rta/1/trace_stat.json\n", - "06:17:19 INFO : --------------------------------------------------------------------------------\n", - "06:17:19 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "06:17:19 INFO : Executor - Experiment 1/4, [base:perf] 1/1\n", - "06:17:19 WARNING : Executor - FTrace events collection enabled\n", - "06:17:27 INFO : WlGen - Workload execution START:\n", - "06:17:27 INFO : WlGen - /home/brendan/devlib-target/bin/perf bench sched messaging --pipe --thread --group 1 --loop 10\n", - "06:17:28 INFO : PerfBench - Completion time: 0.034000, Performance 29.411765\n" - ] - } - ], - "source": [ - "executor.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[01;34m/home/brejac01/sources/lisa/results/ExecutorExample\u001b[00m\r\n", - "├── \u001b[01;34mperf_bench_messaging:base:perf\u001b[00m\r\n", - "│   ├── \u001b[01;34m1\u001b[00m\r\n", - "│   │   ├── output.log\r\n", - "│   │   ├── performance.json\r\n", - "│   │   └── trace.dat\r\n", - "│   ├── kernel.config\r\n", - "│   ├── kernel.version\r\n", - "│   └── platform.json\r\n", - "├── \u001b[01;34mperf_bench_messaging:eas:perf\u001b[00m\r\n", - "│   ├── \u001b[01;34m1\u001b[00m\r\n", - "│   │   ├── output.log\r\n", - "│   │   ├── performance.json\r\n", - "│   │   └── trace.dat\r\n", - "│   ├── kernel.config\r\n", - "│   ├── kernel.version\r\n", - "│   └── platform.json\r\n", - "├── \u001b[01;34mrtapp:base:rta\u001b[00m\r\n", - "│   ├── \u001b[01;34m1\u001b[00m\r\n", - "│   │   ├── output.log\r\n", - "│   │   ├── rta_00.json\r\n", - "│   │   ├── rt-app-task_p200-0.log\r\n", - "│   │   └── trace.dat\r\n", - "│   ├── kernel.config\r\n", - "│   ├── kernel.version\r\n", - "│   └── platform.json\r\n", - "└── \u001b[01;34mrtapp:eas:rta\u001b[00m\r\n", - " ├── \u001b[01;34m1\u001b[00m\r\n", - " │   ├── output.log\r\n", - " │   ├── rta_00.json\r\n", - " │   ├── rt-app-task_p200-0.log\r\n", - " │   └── trace.dat\r\n", - " ├── kernel.config\r\n", - " ├── kernel.version\r\n", - " └── platform.json\r\n", - "\r\n", - "8 directories, 26 files\r\n" - ] - } - ], - "source": [ - "!tree {executor.te.res_dir}" - ] - } - ], - "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": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/ipynb/utils/testenv_example.ipynb b/ipynb/utils/testenv_example.ipynb deleted file mode 100644 index f3b950e0..00000000 --- a/ipynb/utils/testenv_example.ipynb +++ /dev/null @@ -1,1129 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import logging\n", - "from conf import LisaLogging\n", - "LisaLogging.setup()\n", - "\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": [], - "source": [ - "import json\n", - "import time\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test environment setup" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Setup a target configuration\n", - "conf = {\n", - "\n", - " # Platform and board to target\n", - " \"platform\" : \"linux\",\n", - " \"board\" : \"juno\",\n", - "\n", - " # Login credentials\n", - " \"host\" : \"192.168.0.1\",\n", - " \"username\" : \"root\",\n", - " \"password\" : \"\",\n", - "\n", - " # Local installation path\n", - " \"tftp\" : {\n", - " \"folder\" : \"/var/lib/tftpboot\",\n", - " \"kernel\" : \"kern.bin\",\n", - " \"dtb\" : \"dtb.bin\",\n", - " },\n", - "\n", - " # Tools to deploy\n", - " \"tools\" : [ \"rt-app\", \"taskset\" ],\n", - "\n", - " # RTApp calibration values (comment to let LISA do a calibration run)\n", - " \"rtapp-calib\" : {\n", - " \"0\": 358, \"1\": 138, \"2\": 138, \"3\": 357, \"4\": 359, \"5\": 355\n", - " },\n", - "\n", - " # FTrace configuration\n", - " \"ftrace\" : {\n", - " \"events\" : [\n", - " \"cpu_idle\",\n", - " \"sched_switch\",\n", - " ],\n", - " \"buffsize\" : 10240,\n", - " },\n", - " \n", - " # Where results are collected\n", - " \"results_dir\" : \"TestEnvExample\",\n", - " \n", - " # Tune which devlib module are required\n", - " #\"exclude_modules\" : [ \"hwmon\" ],\n", - "\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "05:59:27 INFO : Target - Using base path: /home/derkling/Code/lisa\n", - "05:59:27 INFO : Target - Loading custom (inline) target configuration\n", - "05:59:27 INFO : Target - Devlib modules to load: ['bl', 'hwmon', 'cpufreq']\n", - "05:59:27 INFO : Target - Connecting linux target with: {'username': 'root', 'host': '192.168.0.1', 'password': ''}\n", - "05:59:27 DEBUG : Logging in root@192.168.0.1\n", - "06:00:19 DEBUG : id\n", - "06:00:19 DEBUG : echo $PATH\n", - "06:00:20 DEBUG : ls -1 /usr/local/bin\n", - "06:00:20 DEBUG : cat /proc/cpuinfo\n", - "06:00:20 DEBUG : sudo -- sh -c 'dmidecode -s system-version'\n", - "06:00:21 DEBUG : if [ -e '/sys/class/hwmon' ]; then echo 1; else echo 0; fi\n", - "06:00:21 DEBUG : ls -1 /sys/class/hwmon\n", - "06:00:22 DEBUG : if [ -e '/sys/class/hwmon/hwmon0/name' ]; then echo 1; else echo 0; fi\n", - "06:00:22 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon0/name'\\'''\n", - "06:00:23 DEBUG : ls -1 /sys/class/hwmon/hwmon0/\n", - "06:00:23 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon0/curr1_label'\\'''\n", - "06:00:23 DEBUG : if [ -e '/sys/class/hwmon/hwmon1/name' ]; then echo 1; else echo 0; fi\n", - "06:00:24 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon1/name'\\'''\n", - "06:00:24 DEBUG : ls -1 /sys/class/hwmon/hwmon1/\n", - "06:00:25 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon1/curr1_label'\\'''\n", - "06:00:25 DEBUG : if [ -e '/sys/class/hwmon/hwmon10/name' ]; then echo 1; else echo 0; fi\n", - "06:00:25 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon10/name'\\'''\n", - "06:00:26 DEBUG : ls -1 /sys/class/hwmon/hwmon10/\n", - "06:00:26 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon10/power1_label'\\'''\n", - "06:00:27 DEBUG : if [ -e '/sys/class/hwmon/hwmon11/name' ]; then echo 1; else echo 0; fi\n", - "06:00:27 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon11/name'\\'''\n", - "06:00:28 DEBUG : ls -1 /sys/class/hwmon/hwmon11/\n", - "06:00:28 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon11/power1_label'\\'''\n", - "06:00:28 DEBUG : if [ -e '/sys/class/hwmon/hwmon12/name' ]; then echo 1; else echo 0; fi\n", - "06:00:29 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon12/name'\\'''\n", - "06:00:29 DEBUG : ls -1 /sys/class/hwmon/hwmon12/\n", - "06:00:30 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon12/energy1_label'\\'''\n", - "06:00:30 DEBUG : if [ -e '/sys/class/hwmon/hwmon13/name' ]; then echo 1; else echo 0; fi\n", - "06:00:30 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon13/name'\\'''\n", - "06:00:31 DEBUG : ls -1 /sys/class/hwmon/hwmon13/\n", - "06:00:31 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon13/energy1_label'\\'''\n", - "06:00:32 DEBUG : if [ -e '/sys/class/hwmon/hwmon14/name' ]; then echo 1; else echo 0; fi\n", - "06:00:32 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon14/name'\\'''\n", - "06:00:33 DEBUG : ls -1 /sys/class/hwmon/hwmon14/\n", - "06:00:33 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon14/energy1_label'\\'''\n", - "06:00:33 DEBUG : if [ -e '/sys/class/hwmon/hwmon15/name' ]; then echo 1; else echo 0; fi\n", - "06:00:34 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon15/name'\\'''\n", - "06:00:34 DEBUG : ls -1 /sys/class/hwmon/hwmon15/\n", - "06:00:35 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon15/energy1_label'\\'''\n", - "06:00:35 DEBUG : if [ -e '/sys/class/hwmon/hwmon16/name' ]; then echo 1; else echo 0; fi\n", - "06:00:35 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/name'\\'''\n", - "06:00:36 DEBUG : ls -1 /sys/class/hwmon/hwmon16/\n", - "06:00:36 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in0_label'\\'''\n", - "06:00:37 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in1_label'\\'''\n", - "06:00:37 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in2_label'\\'''\n", - "06:00:38 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in3_label'\\'''\n", - "06:00:38 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in4_label'\\'''\n", - "06:00:38 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in5_label'\\'''\n", - "06:00:39 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in6_label'\\'''\n", - "06:00:39 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/temp1_label'\\'''\n", - "06:00:40 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/temp2_label'\\'''\n", - "06:00:40 DEBUG : if [ -e '/sys/class/hwmon/hwmon2/name' ]; then echo 1; else echo 0; fi\n", - "06:00:40 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon2/name'\\'''\n", - "06:00:41 DEBUG : ls -1 /sys/class/hwmon/hwmon2/\n", - "06:00:41 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon2/curr1_label'\\'''\n", - "06:00:42 DEBUG : if [ -e '/sys/class/hwmon/hwmon3/name' ]; then echo 1; else echo 0; fi\n", - "06:00:42 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon3/name'\\'''\n", - "06:00:43 DEBUG : ls -1 /sys/class/hwmon/hwmon3/\n", - "06:00:43 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon3/curr1_label'\\'''\n", - "06:00:43 DEBUG : if [ -e '/sys/class/hwmon/hwmon4/name' ]; then echo 1; else echo 0; fi\n", - "06:00:44 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon4/name'\\'''\n", - "06:00:44 DEBUG : ls -1 /sys/class/hwmon/hwmon4/\n", - "06:00:45 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon4/in1_label'\\'''\n", - "06:00:45 DEBUG : if [ -e '/sys/class/hwmon/hwmon5/name' ]; then echo 1; else echo 0; fi\n", - "06:00:45 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon5/name'\\'''\n", - "06:00:46 DEBUG : ls -1 /sys/class/hwmon/hwmon5/\n", - "06:00:46 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon5/in1_label'\\'''\n", - "06:00:47 DEBUG : if [ -e '/sys/class/hwmon/hwmon6/name' ]; then echo 1; else echo 0; fi\n", - "06:00:47 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon6/name'\\'''\n", - "06:00:48 DEBUG : ls -1 /sys/class/hwmon/hwmon6/\n", - "06:00:48 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon6/in1_label'\\'''\n", - "06:00:48 DEBUG : if [ -e '/sys/class/hwmon/hwmon7/name' ]; then echo 1; else echo 0; fi\n", - "06:00:49 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon7/name'\\'''\n", - "06:00:49 DEBUG : ls -1 /sys/class/hwmon/hwmon7/\n", - "06:00:50 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon7/in1_label'\\'''\n", - "06:00:50 DEBUG : if [ -e '/sys/class/hwmon/hwmon8/name' ]; then echo 1; else echo 0; fi\n", - "06:00:50 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon8/name'\\'''\n", - "06:00:51 DEBUG : ls -1 /sys/class/hwmon/hwmon8/\n", - "06:00:51 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon8/power1_label'\\'''\n", - "06:00:52 DEBUG : if [ -e '/sys/class/hwmon/hwmon9/name' ]; then echo 1; else echo 0; fi\n", - "06:00:52 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon9/name'\\'''\n", - "06:00:52 DEBUG : ls -1 /sys/class/hwmon/hwmon9/\n", - "06:00:53 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon9/power1_label'\\'''\n", - "06:00:53 DEBUG : uname -m\n", - "06:00:54 DEBUG : if [ -e '/sys/devices/system/cpu/cpufreq' ]; then echo 1; else echo 0; fi\n", - "06:00:54 DEBUG : sudo -- sh -c 'mount -o remount,rw /'\n", - "06:00:54 INFO : Target - Initializing target workdir [/root/devlib-target]\n", - "06:00:55 DEBUG : mkdir -p /root/devlib-target\n", - "06:00:55 DEBUG : mkdir -p /root/devlib-target/bin\n", - "06:00:55 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/libs/devlib/devlib/bin/arm64/busybox root@192.168.0.1:/root/devlib-target/bin/busybox\n", - "06:00:56 DEBUG : chmod a+x /root/devlib-target/bin/busybox\n", - "06:00:56 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/libs/devlib/devlib/bin/scripts/shutils root@192.168.0.1:/root/devlib-target/bin/shutils\n", - "06:00:56 DEBUG : chmod a+x /root/devlib-target/bin/shutils\n", - "06:00:56 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/tools/scripts/cgroup_run_into.sh root@192.168.0.1:/root/devlib-target/bin/cgroup_run_into.sh\n", - "06:00:57 DEBUG : chmod a+x /root/devlib-target/bin/cgroup_run_into.sh\n", - "06:00:57 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/tools/arm64/perf root@192.168.0.1:/root/devlib-target/bin/perf\n", - "06:01:00 DEBUG : chmod a+x /root/devlib-target/bin/perf\n", - "06:01:00 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/tools/arm64/taskset root@192.168.0.1:/root/devlib-target/bin/taskset\n", - "06:01:00 DEBUG : chmod a+x /root/devlib-target/bin/taskset\n", - "06:01:01 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/tools/arm64/rt-app root@192.168.0.1:/root/devlib-target/bin/rt-app\n", - "06:01:02 DEBUG : chmod a+x /root/devlib-target/bin/rt-app\n", - "06:01:03 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/tools/arm64/trace-cmd root@192.168.0.1:/root/devlib-target/bin/trace-cmd\n", - "06:01:03 DEBUG : chmod a+x /root/devlib-target/bin/trace-cmd\n", - "06:01:03 INFO : Target topology: [[0, 3, 4, 5], [1, 2]]\n", - "06:01:03 DEBUG : sudo -- sh -c 'cat '\\''/sys/devices/system/cpu/online'\\'''\n", - "06:01:04 DEBUG : cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_available_frequencies\n", - "06:01:04 DEBUG : sudo -- sh -c 'cat '\\''/sys/devices/system/cpu/online'\\'''\n", - "06:01:05 DEBUG : cat /sys/devices/system/cpu/cpu1/cpufreq/scaling_available_frequencies\n", - "06:01:05 INFO : Platform - Loading default EM [/home/derkling/Code/lisa/libs/utils/platforms/juno.json]...\n", - "06:01:05 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/libs/devlib/devlib/bin/arm64/trace-cmd root@192.168.0.1:/root/devlib-target/bin/trace-cmd\n", - "06:01:05 DEBUG : chmod a+x /root/devlib-target/bin/trace-cmd\n", - "06:01:06 DEBUG : cat /sys/kernel/debug/tracing/available_events\n", - "06:01:06 INFO : FTrace - Enabled events:\n", - "06:01:06 INFO : FTrace - ['cpu_idle', 'sched_switch']\n", - "06:01:06 INFO : FTrace - None\n", - "06:01:06 INFO : EnergyMeter - Scanning for HWMON channels, may take some time...\n", - "06:01:06 INFO : EnergyMeter - Channels selected for energy sampling:\n", - "[CHAN(v2m_juno_energy/energy1, a57_energy), CHAN(v2m_juno_energy/energy1, a53_energy)]\n", - "06:01:06 INFO : Loading RTApp calibration from configuration file...\n", - "06:01:06 INFO : Using RT-App calibration values: {\"0\": 358, \"1\": 138, \"2\": 138, \"3\": 357, \"4\": 359, \"5\": 355}\n", - "06:01:06 INFO : TestEnv - Set results folder to:\n", - "06:01:06 INFO : TestEnv - /home/derkling/Code/lisa/results/TestEnvExample\n", - "06:01:06 INFO : TestEnv - Experiment results available also in:\n", - "06:01:06 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n" - ] - } - ], - "source": [ - "from env import TestEnv\n", - "\n", - "# Initialize a test environment using the provided configuration\n", - "te = TestEnv(conf)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Attributes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The initialization of the test environment pre-initialize some useful
\n", - "environment variables which are available to write test cases.\n", - "\n", - "These are some of the information available via the TestEnv object." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"username\": \"root\", \n", - " \"ftrace\": {\n", - " \"buffsize\": 10240, \n", - " \"events\": [\n", - " \"cpu_idle\", \n", - " \"sched_switch\"\n", - " ]\n", - " }, \n", - " \"rtapp-calib\": {\n", - " \"1\": 138, \n", - " \"0\": 358, \n", - " \"3\": 357, \n", - " \"2\": 138, \n", - " \"5\": 355, \n", - " \"4\": 359\n", - " }, \n", - " \"host\": \"192.168.0.1\", \n", - " \"password\": \"\", \n", - " \"tools\": [\n", - " \"rt-app\", \n", - " \"taskset\", \n", - " \"trace-cmd\", \n", - " \"taskset\", \n", - " \"trace-cmd\", \n", - " \"perf\", \n", - " \"cgroup_run_into.sh\"\n", - " ], \n", - " \"results_dir\": \"TestEnvExample\", \n", - " \"platform\": \"linux\", \n", - " \"board\": \"juno\", \n", - " \"__features__\": [], \n", - " \"tftp\": {\n", - " \"kernel\": \"kern.bin\", \n", - " \"folder\": \"/var/lib/tftpboot\", \n", - " \"dtb\": \"dtb.bin\"\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "# The complete configuration of the target we have configured\n", - "print json.dumps(te.conf, indent=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "None\n", - "None\n" - ] - } - ], - "source": [ - "# Last configured kernel and DTB image\n", - "print te.kernel\n", - "print te.dtb" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "192.168.0.1\n", - "None\n" - ] - } - ], - "source": [ - "# The IP and MAC address of the target\n", - "print te.ip\n", - "print te.mac" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"nrg_model\": {\n", - " \"big\": {\n", - " \"cluster\": {\n", - " \"nrg_max\": 64\n", - " }, \n", - " \"cpu\": {\n", - " \"cap_max\": 1024, \n", - " \"nrg_max\": 616\n", - " }\n", - " }, \n", - " \"little\": {\n", - " \"cluster\": {\n", - " \"nrg_max\": 57\n", - " }, \n", - " \"cpu\": {\n", - " \"cap_max\": 447, \n", - " \"nrg_max\": 93\n", - " }\n", - " }\n", - " }, \n", - " \"clusters\": {\n", - " \"big\": [\n", - " 1, \n", - " 2\n", - " ], \n", - " \"little\": [\n", - " 0, \n", - " 3, \n", - " 4, \n", - " 5\n", - " ]\n", - " }, \n", - " \"cpus_count\": 6, \n", - " \"freqs\": {\n", - " \"big\": [\n", - " 450000, \n", - " 625000, \n", - " 800000, \n", - " 950000, \n", - " 1100000\n", - " ], \n", - " \"little\": [\n", - " 450000, \n", - " 575000, \n", - " 700000, \n", - " 775000, \n", - " 850000\n", - " ]\n", - " }, \n", - " \"topology\": [\n", - " [\n", - " 0, \n", - " 3, \n", - " 4, \n", - " 5\n", - " ], \n", - " [\n", - " 1, \n", - " 2\n", - " ]\n", - " ]\n", - "}\n" - ] - } - ], - "source": [ - "# A full platform descriptor\n", - "print json.dumps(te.platform, indent=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/derkling/Code/lisa/results/TestEnvExample'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# A pre-created folder to host the tests results generated using this\n", - "# test environment, notice that the suite could add additional information\n", - "# in this folder, like for example a copy of the target configuration\n", - "# and other target specific collected information\n", - "te.res_dir" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'/data/local/schedtest'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The working directory on the target\n", - "te.workdir" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "cluster [[0, 3, 4, 5], [1, 2]]\n", - "cpu [[0], [1], [2], [3], [4], [5]]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The target topology, which can be used to build BART assertions\n", - "te.topology" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Functions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some methods are also exposed to test developers which could be used to easy\n", - "the creation of tests.\n", - "\n", - "These are some of the methods available:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: 358, 1: 138, 2: 138, 3: 357, 4: 359, 5: 355}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Calibrate RT-App (if required) and get the most updated calibration value\n", - "te.calibration()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "({'clusters': {'big': [1, 2], 'little': [0, 3, 4, 5]},\n", - " 'cpus_count': 6,\n", - " 'freqs': {'big': [450000, 625000, 800000, 950000, 1100000],\n", - " 'little': [450000, 575000, 700000, 775000, 850000]},\n", - " 'nrg_model': {u'big': {u'cluster': {u'nrg_max': 64},\n", - " u'cpu': {u'cap_max': 1024, u'nrg_max': 616}},\n", - " u'little': {u'cluster': {u'nrg_max': 57},\n", - " u'cpu': {u'cap_max': 447, u'nrg_max': 93}}},\n", - " 'topology': [[0, 3, 4, 5], [1, 2]]},\n", - " '/tmp/platform.json')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Generate a JSON file with the complete platform description\n", - "te.platform_dump(dest_dir='/tmp')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:01:08 INFO : HostResolver - Target (00:02:f7:00:5a:5b) at IP address: 192.168.0.1\n", - "06:01:08 DEBUG : sudo -- sh -c 'sleep 2 && reboot -f &'\n", - "06:01:08 INFO : Reboot - Waiting up to 60[s] for target [192.168.0.1] to reboot...\n", - "06:02:13 INFO : Target - Devlib modules to load: ['bl', 'hwmon', 'cpufreq']\n", - "06:02:13 INFO : Target - Connecting linux target with: {'username': 'root', 'host': '192.168.0.1', 'password': ''}\n", - "06:02:13 DEBUG : Logging in root@192.168.0.1\n", - "06:02:14 DEBUG : id\n", - "06:02:15 DEBUG : echo $PATH\n", - "06:02:15 DEBUG : ls -1 /usr/local/bin\n", - "06:02:16 DEBUG : cat /proc/cpuinfo\n", - "06:02:16 DEBUG : sudo -- sh -c 'dmidecode -s system-version'\n", - "06:02:16 DEBUG : if [ -e '/sys/class/hwmon' ]; then echo 1; else echo 0; fi\n", - "06:02:17 DEBUG : ls -1 /sys/class/hwmon\n", - "06:02:17 DEBUG : if [ -e '/sys/class/hwmon/hwmon0/name' ]; then echo 1; else echo 0; fi\n", - "06:02:18 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon0/name'\\'''\n", - "06:02:18 DEBUG : ls -1 /sys/class/hwmon/hwmon0/\n", - "06:02:19 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon0/curr1_label'\\'''\n", - "06:02:19 DEBUG : if [ -e '/sys/class/hwmon/hwmon1/name' ]; then echo 1; else echo 0; fi\n", - "06:02:19 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon1/name'\\'''\n", - "06:02:20 DEBUG : ls -1 /sys/class/hwmon/hwmon1/\n", - "06:02:20 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon1/curr1_label'\\'''\n", - "06:02:21 DEBUG : if [ -e '/sys/class/hwmon/hwmon10/name' ]; then echo 1; else echo 0; fi\n", - "06:02:21 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon10/name'\\'''\n", - "06:02:21 DEBUG : ls -1 /sys/class/hwmon/hwmon10/\n", - "06:02:22 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon10/power1_label'\\'''\n", - "06:02:22 DEBUG : if [ -e '/sys/class/hwmon/hwmon11/name' ]; then echo 1; else echo 0; fi\n", - "06:02:23 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon11/name'\\'''\n", - "06:02:23 DEBUG : ls -1 /sys/class/hwmon/hwmon11/\n", - "06:02:23 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon11/power1_label'\\'''\n", - "06:02:24 DEBUG : if [ -e '/sys/class/hwmon/hwmon12/name' ]; then echo 1; else echo 0; fi\n", - "06:02:24 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon12/name'\\'''\n", - "06:02:25 DEBUG : ls -1 /sys/class/hwmon/hwmon12/\n", - "06:02:25 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon12/energy1_label'\\'''\n", - "06:02:26 DEBUG : if [ -e '/sys/class/hwmon/hwmon13/name' ]; then echo 1; else echo 0; fi\n", - "06:02:26 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon13/name'\\'''\n", - "06:02:26 DEBUG : ls -1 /sys/class/hwmon/hwmon13/\n", - "06:02:27 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon13/energy1_label'\\'''\n", - "06:02:27 DEBUG : if [ -e '/sys/class/hwmon/hwmon14/name' ]; then echo 1; else echo 0; fi\n", - "06:02:28 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon14/name'\\'''\n", - "06:02:28 DEBUG : ls -1 /sys/class/hwmon/hwmon14/\n", - "06:02:28 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon14/energy1_label'\\'''\n", - "06:02:29 DEBUG : if [ -e '/sys/class/hwmon/hwmon15/name' ]; then echo 1; else echo 0; fi\n", - "06:02:29 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon15/name'\\'''\n", - "06:02:30 DEBUG : ls -1 /sys/class/hwmon/hwmon15/\n", - "06:02:30 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon15/energy1_label'\\'''\n", - "06:02:31 DEBUG : if [ -e '/sys/class/hwmon/hwmon16/name' ]; then echo 1; else echo 0; fi\n", - "06:02:31 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/name'\\'''\n", - "06:02:31 DEBUG : ls -1 /sys/class/hwmon/hwmon16/\n", - "06:02:32 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in0_label'\\'''\n", - "06:02:32 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in1_label'\\'''\n", - "06:02:33 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in2_label'\\'''\n", - "06:02:33 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in3_label'\\'''\n", - "06:02:33 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in4_label'\\'''\n", - "06:02:34 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in5_label'\\'''\n", - "06:02:34 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/in6_label'\\'''\n", - "06:02:35 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/temp1_label'\\'''\n", - "06:02:35 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon16/temp2_label'\\'''\n", - "06:02:36 DEBUG : if [ -e '/sys/class/hwmon/hwmon2/name' ]; then echo 1; else echo 0; fi\n", - "06:02:36 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon2/name'\\'''\n", - "06:02:36 DEBUG : ls -1 /sys/class/hwmon/hwmon2/\n", - "06:02:37 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon2/curr1_label'\\'''\n", - "06:02:37 DEBUG : if [ -e '/sys/class/hwmon/hwmon3/name' ]; then echo 1; else echo 0; fi\n", - "06:02:38 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon3/name'\\'''\n", - "06:02:38 DEBUG : ls -1 /sys/class/hwmon/hwmon3/\n", - "06:02:38 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon3/curr1_label'\\'''\n", - "06:02:39 DEBUG : if [ -e '/sys/class/hwmon/hwmon4/name' ]; then echo 1; else echo 0; fi\n", - "06:02:39 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon4/name'\\'''\n", - "06:02:40 DEBUG : ls -1 /sys/class/hwmon/hwmon4/\n", - "06:02:40 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon4/in1_label'\\'''\n", - "06:02:41 DEBUG : if [ -e '/sys/class/hwmon/hwmon5/name' ]; then echo 1; else echo 0; fi\n", - "06:02:41 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon5/name'\\'''\n", - "06:02:41 DEBUG : ls -1 /sys/class/hwmon/hwmon5/\n", - "06:02:42 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon5/in1_label'\\'''\n", - "06:02:42 DEBUG : if [ -e '/sys/class/hwmon/hwmon6/name' ]; then echo 1; else echo 0; fi\n", - "06:02:43 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon6/name'\\'''\n", - "06:02:43 DEBUG : ls -1 /sys/class/hwmon/hwmon6/\n", - "06:02:43 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon6/in1_label'\\'''\n", - "06:02:44 DEBUG : if [ -e '/sys/class/hwmon/hwmon7/name' ]; then echo 1; else echo 0; fi\n", - "06:02:44 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon7/name'\\'''\n", - "06:02:45 DEBUG : ls -1 /sys/class/hwmon/hwmon7/\n", - "06:02:45 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon7/in1_label'\\'''\n", - "06:02:46 DEBUG : if [ -e '/sys/class/hwmon/hwmon8/name' ]; then echo 1; else echo 0; fi\n", - "06:02:46 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon8/name'\\'''\n", - "06:02:46 DEBUG : ls -1 /sys/class/hwmon/hwmon8/\n", - "06:02:47 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon8/power1_label'\\'''\n", - "06:02:47 DEBUG : if [ -e '/sys/class/hwmon/hwmon9/name' ]; then echo 1; else echo 0; fi\n", - "06:02:48 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon9/name'\\'''\n", - "06:02:48 DEBUG : ls -1 /sys/class/hwmon/hwmon9/\n", - "06:02:48 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon9/power1_label'\\'''\n", - "06:02:49 DEBUG : uname -m\n", - "06:02:49 DEBUG : if [ -e '/sys/devices/system/cpu/cpufreq' ]; then echo 1; else echo 0; fi\n", - "06:02:50 DEBUG : sudo -- sh -c 'mount -o remount,rw /'\n", - "06:02:50 INFO : Target - Initializing target workdir [/root/devlib-target]\n", - "06:02:50 DEBUG : mkdir -p /root/devlib-target\n", - "06:02:50 DEBUG : mkdir -p /root/devlib-target/bin\n", - "06:02:51 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/libs/devlib/devlib/bin/arm64/busybox root@192.168.0.1:/root/devlib-target/bin/busybox\n", - "06:02:51 DEBUG : chmod a+x /root/devlib-target/bin/busybox\n", - "06:02:51 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/libs/devlib/devlib/bin/scripts/shutils root@192.168.0.1:/root/devlib-target/bin/shutils\n", - "06:02:52 DEBUG : chmod a+x /root/devlib-target/bin/shutils\n", - "06:02:52 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/tools/scripts/cgroup_run_into.sh root@192.168.0.1:/root/devlib-target/bin/cgroup_run_into.sh\n", - "06:02:52 DEBUG : chmod a+x /root/devlib-target/bin/cgroup_run_into.sh\n", - "06:02:52 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/tools/arm64/perf root@192.168.0.1:/root/devlib-target/bin/perf\n", - "06:02:55 DEBUG : chmod a+x /root/devlib-target/bin/perf\n", - "06:02:56 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/tools/arm64/taskset root@192.168.0.1:/root/devlib-target/bin/taskset\n", - "06:02:56 DEBUG : chmod a+x /root/devlib-target/bin/taskset\n", - "06:02:56 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/tools/arm64/rt-app root@192.168.0.1:/root/devlib-target/bin/rt-app\n", - "06:02:56 DEBUG : chmod a+x /root/devlib-target/bin/rt-app\n", - "06:02:57 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/tools/arm64/trace-cmd root@192.168.0.1:/root/devlib-target/bin/trace-cmd\n", - "06:02:57 DEBUG : chmod a+x /root/devlib-target/bin/trace-cmd\n", - "06:02:57 INFO : Target topology: [[0, 3, 4, 5], [1, 2]]\n", - "06:02:57 DEBUG : sudo -- sh -c 'cat '\\''/sys/devices/system/cpu/online'\\'''\n", - "06:02:58 DEBUG : cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_available_frequencies\n", - "06:02:58 DEBUG : sudo -- sh -c 'cat '\\''/sys/devices/system/cpu/online'\\'''\n", - "06:02:59 DEBUG : cat /sys/devices/system/cpu/cpu1/cpufreq/scaling_available_frequencies\n", - "06:02:59 INFO : Platform - Loading default EM [/home/derkling/Code/lisa/libs/utils/platforms/juno.json]...\n", - "06:02:59 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/libs/devlib/devlib/bin/arm64/trace-cmd root@192.168.0.1:/root/devlib-target/bin/trace-cmd\n", - "06:02:59 DEBUG : chmod a+x /root/devlib-target/bin/trace-cmd\n", - "06:03:00 DEBUG : cat /sys/kernel/debug/tracing/available_events\n", - "06:03:00 INFO : FTrace - Enabled events:\n", - "06:03:00 INFO : FTrace - ['cpu_idle', 'sched_switch']\n", - "06:03:00 INFO : FTrace - None\n", - "06:03:00 INFO : EnergyMeter - Scanning for HWMON channels, may take some time...\n", - "06:03:00 INFO : EnergyMeter - Channels selected for energy sampling:\n", - "[CHAN(v2m_juno_energy/energy1, a57_energy), CHAN(v2m_juno_energy/energy1, a53_energy)]\n" - ] - } - ], - "source": [ - "# Force a reboot of the target (and wait specified [s] before reconnect)\n", - "te.reboot(reboot_time=60, ping_time=15)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:03:00 INFO : HostResolver - Target (00:02:F7:00:5A:5B) at IP address: 192.168.0.1\n" - ] - }, - { - "data": { - "text/plain": [ - "('00:02:F7:00:5A:5B', '192.168.0.1')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Resolve a MAC address into an IP address\n", - "te.resolv_host(host='00:02:F7:00:5A:5B')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:03:00 INFO : TFTP - Deploy /etc/group into /var/lib/tftpboot/group\n" - ] - } - ], - "source": [ - "# Copy the specified file into the TFTP server folder defined by configuration\n", - "te.tftp_deploy('/etc/group')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A special TestEnv attribute is target, which represent a devlib instance.
\n", - "Using the target attribute we can access to the full set of devlib provided
\n", - "functionalities. Which are summarized in the following sections." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Access to the devlib API" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:03:01 DEBUG : echo -n 'Hello Test Environment'\n" - ] - }, - { - "data": { - "text/plain": [ - "'Hello Test Environment'" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Run a command on the target\n", - "te.target.execute(\"echo -n 'Hello Test Environment'\", as_root=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:03:02 DEBUG : sudo -- sh -c 'sh -c \"sleep 10\" 1>/dev/null 2>/dev/null &'\n" - ] - }, - { - "data": { - "text/plain": [ - "''" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Spawn a command in background on the target\n", - "te.target.kick_off(\"sleep 10\", as_root=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ABI : arm64\n", - "big Core Family : A57\n", - "LITTLE Core Family : A53\n", - "CPU's Clusters IDs : [0, 1, 1, 0, 0, 0]\n", - "CPUs type : ['A53', 'A57', 'A57', 'A53', 'A53', 'A53']\n" - ] - } - ], - "source": [ - "# Acces to many target specific information\n", - "print \"ABI : \", te.target.abi\n", - "print \"big Core Family : \", te.target.big_core\n", - "print \"LITTLE Core Family : \", te.target.little_core\n", - "print \"CPU's Clusters IDs : \", te.target.core_clusters\n", - "print \"CPUs type : \", te.target.core_names" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:03:02 DEBUG : sudo -- sh -c 'cat '\\''/sys/devices/system/cpu/online'\\'''\n", - "06:03:03 DEBUG : sudo -- sh -c 'cat '\\''/sys/devices/system/cpu/cpu1/cpufreq/scaling_cur_freq'\\'''\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "big CPUs IDs : [1, 2]\n", - "LITTLE CPUs IDs : [0, 3, 4, 5]\n", - "big CPUs freqs : 1100000" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:03:03 DEBUG : sudo -- sh -c 'cat '\\''/sys/devices/system/cpu/online'\\'''\n", - "06:03:04 DEBUG : sudo -- sh -c 'cat '\\''/sys/devices/system/cpu/cpu1/cpufreq/scaling_governor'\\'''\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "big CPUs governor : performance\n" - ] - } - ], - "source": [ - "# Access to big.LITTLE specific information\n", - "print \"big CPUs IDs : \", te.target.bl.bigs\n", - "print \"LITTLE CPUs IDs : \", te.target.bl.littles\n", - "print \"big CPUs freqs : {}\".format(te.target.bl.get_bigs_frequency())\n", - "print \"big CPUs governor : {}\".format(te.target.bl.get_bigs_governor())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sample energy from the target" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:03:04 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon13/energy1_input'\\'''\n", - "06:03:05 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon14/energy1_input'\\'''\n", - "06:03:05 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon13/energy1_input'\\'''\n", - "06:03:05 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon14/energy1_input'\\'''\n", - "06:03:06 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon13/energy1_input'\\'''\n", - "06:03:06 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon14/energy1_input'\\'''\n", - "06:03:09 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon13/energy1_input'\\'''\n", - "06:03:09 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon14/energy1_input'\\'''\n", - "06:03:10 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon13/energy1_input'\\'''\n", - "06:03:10 DEBUG : sudo -- sh -c 'cat '\\''/sys/class/hwmon/hwmon14/energy1_input'\\'''\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "First read: {\n", - " \"a53\": {\n", - " \"total\": 0.37534899999999993, \n", - " \"last\": 51.372362, \n", - " \"delta\": 0.18520200000000386\n", - " }, \n", - " \"a57\": {\n", - " \"total\": 1.0124409999999955, \n", - " \"last\": 59.688784, \n", - " \"delta\": 0.5027259999999956\n", - " }\n", - "}\n", - "Second read: {\n", - " \"a53\": {\n", - " \"total\": 1.7191619999999972, \n", - " \"last\": 52.716175, \n", - " \"delta\": 0.17561400000000305\n", - " }, \n", - " \"a57\": {\n", - " \"total\": 2.4685759999999988, \n", - " \"last\": 61.144919, \n", - " \"delta\": 0.4992990000000006\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "# Reset and sample energy counters\n", - "te.emeter.reset()\n", - "nrg = te.emeter.sample()\n", - "nrg = json.dumps(te.emeter.sample(), indent=4)\n", - "print \"First read: \", nrg\n", - "time.sleep(2)\n", - "nrg = te.emeter.sample()\n", - "nrg = json.dumps(te.emeter.sample(), indent=4)\n", - "print \"Second read: \", nrg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Configure FTrace for a sepcific experiment" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:03:10 DEBUG : /usr/bin/scp -r /home/derkling/Code/lisa/libs/devlib/devlib/bin/arm64/trace-cmd root@192.168.0.1:/root/devlib-target/bin/trace-cmd\n", - "06:03:11 DEBUG : chmod a+x /root/devlib-target/bin/trace-cmd\n", - "06:03:11 DEBUG : cat /sys/kernel/debug/tracing/available_events\n", - "06:03:11 INFO : FTrace - Enabled events:\n", - "06:03:11 INFO : FTrace - ['cpu_idle', 'cpu_capacity', 'cpu_frequency', 'sched_switch']\n", - "06:03:11 INFO : FTrace - None\n" - ] - } - ], - "source": [ - "# Configure a specific set of events to trace\n", - "te.ftrace_conf(\n", - " { \n", - " \"events\" : [ \n", - " \"cpu_idle\", \n", - " \"cpu_capacity\",\n", - " \"cpu_frequency\",\n", - " \"sched_switch\",\n", - " ], \n", - " \"buffsize\" : 10240 \n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:03:11 DEBUG : sudo -- sh -c 'echo 10240 > '\\''/sys/kernel/debug/tracing/buffer_size_kb'\\'''\n", - "06:03:12 DEBUG : sudo -- sh -c 'cat '\\''/sys/kernel/debug/tracing/buffer_size_kb'\\'''\n", - "06:03:12 DEBUG : sudo -- sh -c '/root/devlib-target/bin/trace-cmd reset'\n", - "06:03:13 DEBUG : sudo -- sh -c '/root/devlib-target/bin/trace-cmd start -e cpu_idle -e cpu_capacity -e cpu_frequency -e sched_switch'\n", - "06:03:14 DEBUG : sudo -- sh -c 'echo TRACE_MARKER_START > '\\''/sys/kernel/debug/tracing/trace_marker'\\'''\n", - "06:03:15 DEBUG : sudo -- sh -c '/root/devlib-target/bin/shutils cpufreq_trace_all_frequencies'\n", - "06:03:15 DEBUG : uname -a\n", - "06:03:15 DEBUG : sudo -- sh -c '/root/devlib-target/bin/shutils cpufreq_trace_all_frequencies'\n", - "06:03:16 DEBUG : sudo -- sh -c 'echo TRACE_MARKER_STOP > '\\''/sys/kernel/debug/tracing/trace_marker'\\'''\n", - "06:03:16 DEBUG : sudo -- sh -c '/root/devlib-target/bin/trace-cmd stop'\n" - ] - } - ], - "source": [ - "# Start/Stop a FTrace session\n", - "te.ftrace.start()\n", - "te.target.execute(\"uname -a\")\n", - "te.ftrace.stop()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:03:17 DEBUG : sudo -- sh -c '/root/devlib-target/bin/trace-cmd extract -o /root/devlib-target/trace.dat'\n", - "06:03:18 DEBUG : /usr/bin/scp -r root@192.168.0.1:/root/devlib-target/trace.dat /home/derkling/Code/lisa/results/TestEnvExample/trace.dat\n" - ] - } - ], - "source": [ - "# Collect and visualize the trace\n", - "trace_file = os.path.join(te.res_dir, 'trace.dat')\n", - "te.ftrace.get_trace(trace_file)\n", - "output = os.popen(\"DISPLAY=:0.0 kernelshark {}\".format(trace_file))" - ] - } - ], - "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 -} diff --git a/ipynb/wlgen/rtapp_examples.ipynb b/ipynb/wlgen/rtapp_examples.ipynb deleted file mode 100644 index a109d14a..00000000 --- a/ipynb/wlgen/rtapp_examples.ipynb +++ /dev/null @@ -1,737 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import logging\n", - "from conf import LisaLogging\n", - "LisaLogging.setup()" - ] - }, - { - "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", - "# Support to access the remote target\n", - "import devlib\n", - "from env import TestEnv\n", - "\n", - "# Support to configure and run RTApp based workloads\n", - "from wlgen import RTA, Ramp, Step, Pulse, Periodic" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test environment setup" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11:39:33 INFO : Target - Using base path: /home/derkling/Code/lisa\n", - "11:39:33 INFO : Target - Loading custom (inline) target configuration\n", - "11:39:33 INFO : Target - Devlib modules to load: []\n", - "11:39:33 INFO : Target - Connecting host target:\n", - "11:39:33 INFO : Target - username : put_here_your_username\n", - "11:39:33 INFO : Target - password : \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sudo password:········\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11:39:36 INFO : Target - Initializing target workdir:\n", - "11:39:36 INFO : Target - /tmp\n", - "11:39:36 INFO : Target - Topology:\n", - "11:39:36 INFO : Target - [[0, 1, 2, 3, 4, 5, 6, 7]]\n", - "11:39:36 WARNING : Target - Unable to identify cluster frequencies\n", - "11:39:36 INFO : TestEnv - Set results folder to:\n", - "11:39:36 INFO : TestEnv - /home/derkling/Code/lisa/results/20160226_113936\n", - "11:39:36 INFO : TestEnv - Experiment results available also in:\n", - "11:39:36 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n" - ] - } - ], - "source": [ - "# Let's use the local host as a target\n", - "te = TestEnv(\n", - " target_conf={\n", - " \"platform\": 'host',\n", - " \"username\": 'put_here_your_username'\n", - " })" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create a new RTA workload generator object" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The wlgen::RTA class is a workload generator which exposes an API to configure\n", - "RTApp based workload as well as to execute them on a target." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11:39:36 INFO : WlGen - Setup new workload example\n" - ] - } - ], - "source": [ - "# Create a new RTApp workload generator\n", - "rtapp = RTA(\n", - " \n", - " target=te.target, # Target execution on the local machine\n", - " \n", - " name='example', # This is the name of the JSON configuration file reporting\n", - " # the generated RTApp configuration\n", - " \n", - " calibration={0: 10, 1: 11, 2: 12, 3: 13} # These are a set of fake\n", - " # calibration values\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Workload Generation Examples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single periodic task" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An RTApp workload is defined by specifying a **kind**, which represents the way\n", - "we want to defined the behavior of each task.
\n", - "The most common kind is **profile**, which allows to define each task using one\n", - "of the predefined **profile** supported by the RTA base class.
\n", - "
\n", - "The following example shows how to generate a \"periodic\" task
" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "code_folding": [], - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11:39:36 INFO : RTApp - Workload duration defined by longest task\n", - "11:39:36 INFO : RTApp - Default policy: SCHED_OTHER\n", - "11:39:36 INFO : RTApp - ------------------------\n", - "11:39:36 INFO : RTApp - task [task_per20], sched: {'policy': 'FIFO'}\n", - "11:39:36 INFO : RTApp - | calibration CPU: 0\n", - "11:39:36 INFO : RTApp - | loops count: 1\n", - "11:39:36 INFO : RTApp - + phase_000001: duration 5.000000 [s] (50 loops)\n", - "11:39:36 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n", - "11:39:36 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n" - ] - } - ], - "source": [ - "# Configure this RTApp instance to:\n", - "rtapp.conf(\n", - " \n", - " # 1. generate a \"profile based\" set of tasks\n", - " kind='profile',\n", - " \n", - " # 2. define the \"profile\" of each task\n", - " params={\n", - " \n", - " # 3. PERIODIC task\n", - " # \n", - " # This class defines a task which load is periodic with a configured\n", - " # period and duty-cycle.\n", - " # \n", - " # This class is a specialization of the 'pulse' class since a periodic\n", - " # load is generated as a sequence of pulse loads.\n", - " # \n", - " # Args:\n", - " # cuty_cycle_pct (int, [0-100]): the pulses load [%]\n", - " # default: 50[%]\n", - " # duration_s (float): the duration in [s] of the entire workload\n", - " # default: 1.0[s]\n", - " # period_ms (float): the period used to define the load in [ms]\n", - " # default: 100.0[ms]\n", - " # delay_s (float): the delay in [s] before ramp start\n", - " # default: 0[s]\n", - " # sched (dict): the scheduler configuration for this task\n", - " 'task_per20': Periodic(\n", - " period_ms=100, # period\n", - " duty_cycle_pct=20, # duty cycle\n", - " duration_s=5, # duration\n", - " cpus=None, # run on all CPUS\n", - " sched={\n", - " \"policy\": \"FIFO\", # Run this task as a SCHED_FIFO task\n", - " },\n", - " delay_s=0 # start at the start of RTApp\n", - " ).get(),\n", - " \n", - " },\n", - " \n", - " # 4. use this folder for task logfiles\n", - " run_dir='/tmp'\n", - " \n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output of the previous cell reports the main properties of the generated\n", - "tasks. Thus for example we see that the first task is configure to be:\n", - "1. named **task_per20**\n", - "2. will be executed as a **SCHED_FIFO** task\n", - "3. generating a load which is **calibrated** with respect to the **CPU 0**\n", - "3. with one single \"phase\" which defines a peripodic load for the **duration** of **5[s]**\n", - "4. that periodic load consistes of **50 cycles**\n", - "5. each cycle has a **period** of **100[ms]** and a **duty-cycle** of **20%**\n", - "6. which means that the task, for every cycle, will **run** for **20[ms]** and then sleep for **20[ms]** \n", - "\n", - "All these properties are translated into a JSON configuration file for RTApp.
\n", - "Let see what it looks like the generated configuration file:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"tasks\": {\n", - " \"task_per20\": {\n", - " \"policy\": \"SCHED_FIFO\", \n", - " \"phases\": {\n", - " \"p000001\": {\n", - " \"run\": 20000, \n", - " \"timer\": {\n", - " \"ref\": \"task_per20\", \n", - " \"period\": 100000\n", - " }, \n", - " \"loop\": 50\n", - " }\n", - " }, \n", - " \"loop\": 1\n", - " }\n", - " }, \n", - " \"global\": {\n", - " \"duration\": -1, \n", - " \"logdir\": \"/tmp\", \n", - " \"default_policy\": \"SCHED_OTHER\", \n", - " \"calibration\": 10\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "# Dump the configured JSON file for that task\n", - "with open(\"./example_00.json\") as fh:\n", - " rtapp_config = json.load(fh)\n", - "print json.dumps(rtapp_config, indent=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Workload mix" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the wlgen::RTA workload generator we can easily create multiple tasks, each one with different \"profiles\", which are executed once the rtapp application is started in the target.
\n", - "
\n", - "In the following example we configure a workload mix composed by a RAMP task, a STEP task and a PULSE task:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "code_folding": [], - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11:39:37 INFO : RTApp - Workload duration defined by longest task\n", - "11:39:37 INFO : RTApp - Default policy: SCHED_OTHER\n", - "11:39:37 INFO : RTApp - ------------------------\n", - "11:39:37 INFO : RTApp - task [task_pls5-80], sched: using default policy\n", - "11:39:37 INFO : RTApp - | start delay: 0.500000 [s]\n", - "11:39:37 INFO : RTApp - | calibration CPU: 0\n", - "11:39:37 INFO : RTApp - | loops count: 1\n", - "11:39:37 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 65 %\n", - "11:39:37 INFO : RTApp - | run_time 65000 [us], sleep_time 35000 [us]\n", - "11:39:37 INFO : RTApp - + phase_000002: duration 1.000000 [s] (10 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 5 %\n", - "11:39:37 INFO : RTApp - | run_time 5000 [us], sleep_time 95000 [us]\n", - "11:39:37 INFO : RTApp - ------------------------\n", - "11:39:37 INFO : RTApp - task [task_rmp20_5-60], sched: using default policy\n", - "11:39:37 INFO : RTApp - | calibration CPU: 0\n", - "11:39:37 INFO : RTApp - | loops count: 1\n", - "11:39:37 INFO : RTApp - | CPUs affinity: 0\n", - "11:39:37 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 5 %\n", - "11:39:37 INFO : RTApp - | run_time 5000 [us], sleep_time 95000 [us]\n", - "11:39:37 INFO : RTApp - + phase_000002: duration 1.000000 [s] (10 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n", - "11:39:37 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n", - "11:39:37 INFO : RTApp - + phase_000003: duration 1.000000 [s] (10 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n", - "11:39:37 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n", - "11:39:37 INFO : RTApp - + phase_000004: duration 1.000000 [s] (10 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 65 %\n", - "11:39:37 INFO : RTApp - | run_time 65000 [us], sleep_time 35000 [us]\n", - "11:39:37 INFO : RTApp - ------------------------\n", - "11:39:37 INFO : RTApp - task [task_stp10-50], sched: using default policy\n", - "11:39:37 INFO : RTApp - | start delay: 0.500000 [s]\n", - "11:39:37 INFO : RTApp - | calibration CPU: 0\n", - "11:39:37 INFO : RTApp - | loops count: 1\n", - "11:39:37 INFO : RTApp - + phase_000001: sleep 1.000000 [s]\n", - "11:39:37 INFO : RTApp - + phase_000002: duration 1.000000 [s] (10 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 50 %\n", - "11:39:37 INFO : RTApp - | run_time 50000 [us], sleep_time 50000 [us]\n" - ] - } - ], - "source": [ - "# Configure this RTApp instance to:\n", - "rtapp.conf(\n", - " # 1. generate a \"profile based\" set of tasks\n", - " kind='profile',\n", - " \n", - " # 2. define the \"profile\" of each task\n", - " params={\n", - " \n", - " # 3. RAMP task\n", - " #\n", - " # This class defines a task which load is a ramp with a configured number\n", - " # of steps according to the input parameters.\n", - " # \n", - " # Args:\n", - " # start_pct (int, [0-100]): the initial load [%], (default 0[%])\n", - " # end_pct (int, [0-100]): the final load [%], (default 100[%])\n", - " # delta_pct (int, [0-100]): the load increase/decrease [%],\n", - " # default: 10[%]\n", - " # increase if start_prc < end_prc\n", - " # decrease if start_prc > end_prc\n", - " # time_s (float): the duration in [s] of each load step\n", - " # default: 1.0[s]\n", - " # period_ms (float): the period used to define the load in [ms]\n", - " # default: 100.0[ms]\n", - " # delay_s (float): the delay in [s] before ramp start\n", - " # default: 0[s]\n", - " # loops (int): number of time to repeat the ramp, with the\n", - " # specified delay in between\n", - " # default: 0\n", - " # sched (dict): the scheduler configuration for this task\n", - " # cpus (list): the list of CPUs on which task can run\n", - " 'task_rmp20_5-60': Ramp(\n", - " period_ms=100, # period\n", - " start_pct=5, # intial load\n", - " end_pct=65, # end load\n", - " delta_pct=20, # load % increase...\n", - " time_s=1, # ... every 1[s]\n", - " cpus=\"0\" # run just on first CPU\n", - " ).get(),\n", - " \n", - " # 4. STEP task\n", - " # \n", - " # This class defines a task which load is a step with a configured\n", - " # initial and final load.\n", - " # \n", - " # Args:\n", - " # start_pct (int, [0-100]): the initial load [%]\n", - " # default 0[%])\n", - " # end_pct (int, [0-100]): the final load [%]\n", - " # default 100[%]\n", - " # time_s (float): the duration in [s] of the start and end load\n", - " # default: 1.0[s]\n", - " # period_ms (float): the period used to define the load in [ms]\n", - " # default 100.0[ms]\n", - " # delay_s (float): the delay in [s] before ramp start\n", - " # default 0[s]\n", - " # loops (int): number of time to repeat the ramp, with the\n", - " # specified delay in between\n", - " # default: 0\n", - " # sched (dict): the scheduler configuration for this task\n", - " # cpus (list): the list of CPUs on which task can run\n", - " 'task_stp10-50': Step(\n", - " period_ms=100, # period\n", - " start_pct=0, # intial load\n", - " end_pct=50, # end load\n", - " time_s=1, # ... every 1[s]\n", - " delay_s=0.5 # start .5[s] after the start of RTApp\n", - " ).get(),\n", - " \n", - " # 5. PULSE task\n", - " #\n", - " # This class defines a task which load is a pulse with a configured\n", - " # initial and final load.\n", - " # \n", - " # The main difference with the 'step' class is that a pulse workload is\n", - " # by definition a 'step down', i.e. the workload switch from an finial\n", - " # load to a final one which is always lower than the initial one.\n", - " # Moreover, a pulse load does not generate a sleep phase in case of 0[%]\n", - " # load, i.e. the task ends as soon as the non null initial load has\n", - " # completed.\n", - " # \n", - " # Args:\n", - " # start_pct (int, [0-100]): the initial load [%]\n", - " # default: 0[%]\n", - " # end_pct (int, [0-100]): the final load [%]\n", - " # default: 100[%]\n", - " # NOTE: must be lower than start_pct value\n", - " # time_s (float): the duration in [s] of the start and end load\n", - " # default: 1.0[s]\n", - " # NOTE: if end_pct is 0, the task end after the\n", - " # start_pct period completed\n", - " # period_ms (float): the period used to define the load in [ms]\n", - " # default: 100.0[ms]\n", - " # delay_s (float): the delay in [s] before ramp start\n", - " # default: 0[s]\n", - " # loops (int): number of time to repeat the ramp, with the\n", - " # specified delay in between\n", - " # default: 0\n", - " # sched (dict): the scheduler configuration for this task\n", - " # cpus (list): the list of CPUs on which task can run\n", - " 'task_pls5-80': Pulse(\n", - " period_ms=100, # period\n", - " start_pct=65, # intial load\n", - " end_pct=5, # end load\n", - " time_s=1, # ... every 1[s]\n", - " delay_s=0.5 # start .5[s] after the start of RTApp\n", - " ).get(),\n", - " \n", - " \n", - " },\n", - " \n", - " # 6. use this folder for task logfiles\n", - " run_dir='/tmp'\n", - " \n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"tasks\": {\n", - " \"task_rmp20_5-60\": {\n", - " \"policy\": \"SCHED_OTHER\", \n", - " \"phases\": {\n", - " \"p000004\": {\n", - " \"run\": 65000, \n", - " \"timer\": {\n", - " \"ref\": \"task_rmp20_5-60\", \n", - " \"period\": 100000\n", - " }, \n", - " \"loop\": 10\n", - " }, \n", - " \"p000003\": {\n", - " \"run\": 45000, \n", - " \"timer\": {\n", - " \"ref\": \"task_rmp20_5-60\", \n", - " \"period\": 100000\n", - " }, \n", - " \"loop\": 10\n", - " }, \n", - " \"p000002\": {\n", - " \"run\": 25000, \n", - " \"timer\": {\n", - " \"ref\": \"task_rmp20_5-60\", \n", - " \"period\": 100000\n", - " }, \n", - " \"loop\": 10\n", - " }, \n", - " \"p000001\": {\n", - " \"run\": 5000, \n", - " \"timer\": {\n", - " \"ref\": \"task_rmp20_5-60\", \n", - " \"period\": 100000\n", - " }, \n", - " \"loop\": 10\n", - " }\n", - " }, \n", - " \"cpus\": [\n", - " 0\n", - " ], \n", - " \"loop\": 1\n", - " }, \n", - " \"task_pls5-80\": {\n", - " \"policy\": \"SCHED_OTHER\", \n", - " \"phases\": {\n", - " \"p000002\": {\n", - " \"run\": 5000, \n", - " \"timer\": {\n", - " \"ref\": \"task_pls5-80\", \n", - " \"period\": 100000\n", - " }, \n", - " \"loop\": 10\n", - " }, \n", - " \"p000001\": {\n", - " \"run\": 65000, \n", - " \"timer\": {\n", - " \"ref\": \"task_pls5-80\", \n", - " \"period\": 100000\n", - " }, \n", - " \"loop\": 10\n", - " }, \n", - " \"p000000\": {\n", - " \"delay\": 500000\n", - " }\n", - " }, \n", - " \"loop\": 1\n", - " }, \n", - " \"task_stp10-50\": {\n", - " \"policy\": \"SCHED_OTHER\", \n", - " \"phases\": {\n", - " \"p000002\": {\n", - " \"run\": 50000, \n", - " \"timer\": {\n", - " \"ref\": \"task_stp10-50\", \n", - " \"period\": 100000\n", - " }, \n", - " \"loop\": 10\n", - " }, \n", - " \"p000001\": {\n", - " \"sleep\": 1000000, \n", - " \"loop\": 1\n", - " }, \n", - " \"p000000\": {\n", - " \"delay\": 500000\n", - " }\n", - " }, \n", - " \"loop\": 1\n", - " }\n", - " }, \n", - " \"global\": {\n", - " \"duration\": -1, \n", - " \"logdir\": \"/tmp\", \n", - " \"default_policy\": \"SCHED_OTHER\", \n", - " \"calibration\": 10\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "# Dump the configured JSON file for that task\n", - "with open(\"./example_00.json\") as fh:\n", - " rtapp_config = json.load(fh)\n", - "print json.dumps(rtapp_config, indent=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Workload composition" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Initial phase and pinning parameters\n", - "ramp = Ramp(period_ms=100, start_pct=5, end_pct=65, delta_pct=20, time_s=1,\n", - " cpus=\"0\")\n", - "\n", - "# Following phases\n", - "medium_slow = Periodic(duty_cycle_pct=10, duration_s=5, period_ms=100)\n", - "high_fast = Periodic(duty_cycle_pct=60, duration_s=5, period_ms=10)\n", - "medium_fast = Periodic(duty_cycle_pct=10, duration_s=5, period_ms=1)\n", - "high_slow = Periodic(duty_cycle_pct=60, duration_s=5, period_ms=100)\n", - "\n", - "#Compose the task\n", - "complex_task = ramp + medium_slow + high_fast + medium_fast + high_slow" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11:39:37 INFO : RTApp - Workload duration defined by longest task\n", - "11:39:37 INFO : RTApp - Default policy: SCHED_OTHER\n", - "11:39:37 INFO : RTApp - ------------------------\n", - "11:39:37 INFO : RTApp - task [complex], sched: using default policy\n", - "11:39:37 INFO : RTApp - | calibration CPU: 0\n", - "11:39:37 INFO : RTApp - | loops count: 1\n", - "11:39:37 INFO : RTApp - | CPUs affinity: 0\n", - "11:39:37 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 5 %\n", - "11:39:37 INFO : RTApp - | run_time 5000 [us], sleep_time 95000 [us]\n", - "11:39:37 INFO : RTApp - + phase_000002: duration 1.000000 [s] (10 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n", - "11:39:37 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n", - "11:39:37 INFO : RTApp - + phase_000003: duration 1.000000 [s] (10 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n", - "11:39:37 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n", - "11:39:37 INFO : RTApp - + phase_000004: duration 1.000000 [s] (10 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 65 %\n", - "11:39:37 INFO : RTApp - | run_time 65000 [us], sleep_time 35000 [us]\n", - "11:39:37 INFO : RTApp - + phase_000005: duration 5.000000 [s] (50 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 10 %\n", - "11:39:37 INFO : RTApp - | run_time 10000 [us], sleep_time 90000 [us]\n", - "11:39:37 INFO : RTApp - + phase_000006: duration 5.000000 [s] (500 loops)\n", - "11:39:37 INFO : RTApp - | period 10000 [us], duty_cycle 60 %\n", - "11:39:37 INFO : RTApp - | run_time 6000 [us], sleep_time 4000 [us]\n", - "11:39:37 INFO : RTApp - + phase_000007: duration 5.000000 [s] (5000 loops)\n", - "11:39:37 INFO : RTApp - | period 1000 [us], duty_cycle 10 %\n", - "11:39:37 INFO : RTApp - | run_time 100 [us], sleep_time 900 [us]\n", - "11:39:37 INFO : RTApp - + phase_000008: duration 5.000000 [s] (50 loops)\n", - "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 60 %\n", - "11:39:37 INFO : RTApp - | run_time 60000 [us], sleep_time 40000 [us]\n" - ] - }, - { - "data": { - "text/plain": [ - "'example_00'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Configure this RTApp instance to:\n", - "rtapp.conf(\n", - " # 1. generate a \"profile based\" set of tasks\n", - " kind='profile',\n", - " \n", - " # 2. define the \"profile\" of each task\n", - " params={\n", - " 'complex' : complex_task.get()\n", - " },\n", - "\n", - " # 6. use this folder for task logfiles\n", - " run_dir='/tmp'\n", - ")" - ] - } - ], - "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 -} diff --git a/ipynb/wlgen/simple_rtapp.ipynb b/ipynb/wlgen/simple_rtapp.ipynb deleted file mode 100644 index 866a0c4f..00000000 --- a/ipynb/wlgen/simple_rtapp.ipynb +++ /dev/null @@ -1,882 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import logging\n", - "from conf import LisaLogging\n", - "LisaLogging.setup()\n", - "\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" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test environment setup" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Setup a target configuration\n", - "my_target_conf = {\n", - " \n", - " # Define the kind of target platform to use for the experiments\n", - " \"platform\" : 'linux', # Linux system, valid other options are:\n", - " # android - access via ADB\n", - " # linux - access via SSH\n", - " # host - direct access\n", - " \n", - " # Preload settings for a specific target\n", - " \"board\" : 'juno', # load JUNO specific settings, e.g.\n", - " # - HWMON based energy sampling\n", - " # - Juno energy model\n", - " # valid options are:\n", - " # - juno - JUNO Development Board\n", - " # - tc2 - TC2 Development Board\n", - " # - oak - Mediatek MT63xx based target\n", - "\n", - " # Define devlib module to load\n", - " #\"modules\" : [\n", - " # 'bl', # enable big.LITTLE support\n", - " # 'cpufreq' # enable CPUFreq support\n", - " #],\n", - "\n", - " # Account to access the remote target\n", - " \"host\" : '192.168.0.1',\n", - " \"username\" : 'root',\n", - " \"password\" : '',\n", - "\n", - " # Comment the following line to force rt-app calibration on your target\n", - " \"rtapp-calib\" : {\n", - " '0': 361, '1': 138, '2': 138, '3': 352, '4': 360, '5': 353\n", - " }\n", - "\n", - "}\n", - "\n", - "# Setup the required Test Environment supports\n", - "my_tests_conf = {\n", - " \n", - " # Binary tools required to run this experiment\n", - " # These tools must be present in the tools/ folder for the architecture\n", - " #\"tools\" : ['rt-app', 'taskset', 'trace-cmd'],\n", - " \n", - " # FTrace events end buffer configuration\n", - " \"ftrace\" : {\n", - " \"events\" : [\n", - " \"sched_switch\",\n", - " \"cpu_frequency\"\n", - " ],\n", - " \"buffsize\" : 10240\n", - " },\n", - "\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:28:57 INFO : Target - Using base path: /home/derkling/Code/lisa\n", - "06:28:57 INFO : Target - Loading custom (inline) target configuration\n", - "06:28:57 INFO : Target - Loading custom (inline) test configuration\n", - "06:28:57 INFO : Target - Devlib modules to load: ['bl', 'hwmon', 'cpufreq']\n", - "06:28:57 INFO : Target - Connecting linux target:\n", - "06:28:57 INFO : Target - username : root\n", - "06:28:57 INFO : Target - host : 192.168.0.1\n", - "06:28:57 INFO : Target - password : \n", - "06:29:34 INFO : Target - Initializing target workdir:\n", - "06:29:34 INFO : Target - /root/devlib-target\n", - "06:29:37 INFO : Target - Topology:\n", - "06:29:37 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n", - "06:29:39 INFO : Platform - Loading default EM:\n", - "06:29:39 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/juno.json\n", - "06:29:40 INFO : FTrace - Enabled tracepoints:\n", - "06:29:40 INFO : FTrace - sched_switch\n", - "06:29:40 INFO : FTrace - cpu_frequency\n", - "06:29:40 INFO : EnergyMeter - Scanning for HWMON channels, may take some time...\n", - "06:29:40 INFO : EnergyMeter - Channels selected for energy sampling:\n", - "06:29:40 INFO : EnergyMeter - a57_energy\n", - "06:29:40 INFO : EnergyMeter - a53_energy\n", - "06:29:40 INFO : TestEnv - Set results folder to:\n", - "06:29:40 INFO : TestEnv - /home/derkling/Code/lisa/results/20160225_182940\n", - "06:29:40 INFO : TestEnv - Experiment results available also in:\n", - "06:29:40 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n" - ] - } - ], - "source": [ - "# Support to access the remote target\n", - "import devlib\n", - "from env import TestEnv\n", - "\n", - "# Initialize a test environment using:\n", - "# the provided target configuration (my_target_conf)\n", - "# the provided test configuration (my_test_conf)\n", - "te = TestEnv(target_conf=my_target_conf, test_conf=my_tests_conf)\n", - "target = te.target" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Workload configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:29:40 INFO : WlGen - Setup new workload simple\n", - "06:29:40 INFO : RTApp - Workload duration defined by longest task\n", - "06:29:40 INFO : RTApp - Default policy: SCHED_OTHER\n", - "06:29:40 INFO : RTApp - ------------------------\n", - "06:29:40 INFO : RTApp - task [task_p20], sched: using default policy\n", - "06:29:40 INFO : RTApp - | calibration CPU: 1\n", - "06:29:40 INFO : RTApp - | loops count: 1\n", - "06:29:40 INFO : RTApp - | CPUs affinity: 0\n", - "06:29:40 INFO : RTApp - + phase_000001: duration 5.000000 [s] (50 loops)\n", - "06:29:40 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n", - "06:29:40 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n", - "06:29:40 INFO : RTApp - ------------------------\n", - "06:29:40 INFO : RTApp - task [task_r20_5-60], sched: using default policy\n", - "06:29:40 INFO : RTApp - | calibration CPU: 1\n", - "06:29:40 INFO : RTApp - | loops count: 1\n", - "06:29:40 INFO : RTApp - | CPUs affinity: 5\n", - "06:29:40 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n", - "06:29:40 INFO : RTApp - | period 100000 [us], duty_cycle 5 %\n", - "06:29:40 INFO : RTApp - | run_time 5000 [us], sleep_time 95000 [us]\n", - "06:29:40 INFO : RTApp - + phase_000002: duration 1.000000 [s] (10 loops)\n", - "06:29:40 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n", - "06:29:40 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n", - "06:29:40 INFO : RTApp - + phase_000003: duration 1.000000 [s] (10 loops)\n", - "06:29:40 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n", - "06:29:40 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n", - "06:29:40 INFO : RTApp - + phase_000004: duration 1.000000 [s] (10 loops)\n", - "06:29:40 INFO : RTApp - | period 100000 [us], duty_cycle 65 %\n", - "06:29:40 INFO : RTApp - | run_time 65000 [us], sleep_time 35000 [us]\n" - ] - } - ], - "source": [ - "# Support to configure and run RTApp based workloads\n", - "from wlgen import RTA, Periodic, Ramp\n", - "\n", - "# Create a new RTApp workload generator using the calibration values\n", - "# reported by the TestEnv module\n", - "rtapp = RTA(target, 'simple', calibration=te.calibration())\n", - "\n", - "# Configure this RTApp instance to:\n", - "rtapp.conf(\n", - " # 1. generate a \"profile based\" set of tasks\n", - " kind='profile',\n", - " \n", - " # 2. define the \"profile\" of each task\n", - " params={\n", - " \n", - " # 3. PERIODIC task with\n", - " 'task_p20': Periodic(\n", - " period_ms=100, # period\n", - " duty_cycle_pct=20, # duty cycle\n", - " duration_s=5, # duration \n", - " cpus='0' # pinned on CPU0\n", - " ).get(),\n", - " \n", - " # 4. RAMP task (i.e. increasing load) with\n", - " 'task_r20_5-60': Ramp(\n", - " start_pct=5, # intial load\n", - " end_pct=65, # end load\n", - " delta_pct=20, # load % increase...\n", - " time_s=1, # ... every 1[s]\n", - " # pinned on last CPU of the target\n", - " cpus=str(len(target.core_names)-1)\n", - " ).get(),\n", - " },\n", - " \n", - " # 4. use this folder for task logfiles\n", - " run_dir=target.working_directory\n", - " \n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Workload execution" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:29:40 INFO : #### Setup FTrace\n", - "06:29:43 INFO : #### Start energy sampling\n", - "06:29:44 INFO : #### Start RTApp execution\n", - "06:29:44 INFO : WlGen - Workload execution START:\n", - "06:29:44 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/simple_00.json\n", - "06:29:50 INFO : #### Read energy consumption: /home/derkling/Code/lisa/results/20160225_182940/energy.json\n", - "06:29:51 INFO : EnergyReport - Energy [ a53]: 9.734375\n", - "06:29:51 INFO : EnergyReport - Energy [ a57]: 4.190309\n", - "06:29:51 INFO : #### Stop FTrace\n", - "06:29:52 INFO : #### Save FTrace: /home/derkling/Code/lisa/results/20160225_182940/trace.dat\n", - "06:29:54 INFO : #### Save platform description: /home/derkling/Code/lisa/results/20160225_182940/platform.json\n" - ] - } - ], - "source": [ - "logging.info('#### Setup FTrace')\n", - "te.ftrace.start()\n", - "\n", - "logging.info('#### Start energy sampling')\n", - "te.emeter.reset()\n", - "\n", - "logging.info('#### Start RTApp execution')\n", - "rtapp.run(out_dir=te.res_dir, cgroup=\"\")\n", - "\n", - "logging.info('#### Read energy consumption: %s/energy.json', te.res_dir)\n", - "nrg_report = te.emeter.report(out_dir=te.res_dir)\n", - "\n", - "logging.info('#### Stop FTrace')\n", - "te.ftrace.stop()\n", - "\n", - "trace_file = os.path.join(te.res_dir, 'trace.dat')\n", - "logging.info('#### Save FTrace: %s', trace_file)\n", - "te.ftrace.get_trace(trace_file)\n", - "\n", - "logging.info('#### Save platform description: %s/platform.json', te.res_dir)\n", - "(plt, plt_file) = te.platform_dump(te.res_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Collected results" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:29:54 INFO : Content of the output folder /home/derkling/Code/lisa/results/20160225_182940\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 4064\r\n", - "drwxrwxr-x 2 derkling derkling 4096 Feb 25 18:29 .\r\n", - "drwxrwxr-x 99 derkling derkling 4096 Feb 25 18:29 ..\r\n", - "-rw-rw-r-- 1 derkling derkling 52 Feb 25 18:29 energy.json\r\n", - "-rw-rw-r-- 1 derkling derkling 450 Feb 25 18:29 output.log\r\n", - "-rw-rw-r-- 1 derkling derkling 1075 Feb 25 18:29 platform.json\r\n", - "-rw-r--r-- 1 derkling derkling 6360 Feb 25 18:29 rt-app-task_p20-0.log\r\n", - "-rw-r--r-- 1 derkling derkling 6360 Feb 25 18:29 rt-app-task_p20-3.log\r\n", - "-rw-r--r-- 1 derkling derkling 5120 Feb 25 18:29 rt-app-task_r20_5-60-1.log\r\n", - "-rw-r--r-- 1 derkling derkling 3880 Feb 25 18:29 rt-app-task_r20_5-60-3.log\r\n", - "-rw-r--r-- 1 derkling derkling 3880 Feb 25 18:29 rt-app-task_r20_5-60-6.log\r\n", - "-rw-r--r-- 1 derkling derkling 1831 Feb 25 18:29 simple_00.json\r\n", - "-rw-r--r-- 1 derkling derkling 4104192 Feb 25 18:29 trace.dat\r\n" - ] - } - ], - "source": [ - "# All data are produced in the output folder defined by the TestEnv module\n", - "logging.info('Content of the output folder %s', te.res_dir)\n", - "!ls -la {te.res_dir}" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:29:54 INFO : Generated RTApp JSON file:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"global\": {\n", - " \"calibration\": \"CPU1\", \n", - " \"default_policy\": \"SCHED_OTHER\", \n", - " \"duration\": -1, \n", - " \"logdir\": \"/root/devlib-target\"\n", - " }, \n", - " \"tasks\": {\n", - " \"task_p20\": {\n", - " \"cpus\": [\n", - " 0\n", - " ], \n", - " \"loop\": 1, \n", - " \"phases\": {\n", - " \"p000001\": {\n", - " \"loop\": 50, \n", - " \"run\": 20000, \n", - " \"timer\": {\n", - " \"period\": 100000, \n", - " \"ref\": \"task_p20\"\n", - " }\n", - " }\n", - " }, \n", - " \"policy\": \"SCHED_OTHER\"\n", - " }, \n", - " \"task_r20_5-60\": {\n", - " \"cpus\": [\n", - " 5\n", - " ], \n", - " \"loop\": 1, \n", - " \"phases\": {\n", - " \"p000001\": {\n", - " \"loop\": 10, \n", - " \"run\": 5000, \n", - " \"timer\": {\n", - " \"period\": 100000, \n", - " \"ref\": \"task_r20_5-60\"\n", - " }\n", - " }, \n", - " \"p000002\": {\n", - " \"loop\": 10, \n", - " \"run\": 25000, \n", - " \"timer\": {\n", - " \"period\": 100000, \n", - " \"ref\": \"task_r20_5-60\"\n", - " }\n", - " }, \n", - " \"p000003\": {\n", - " \"loop\": 10, \n", - " \"run\": 45000, \n", - " \"timer\": {\n", - " \"period\": 100000, \n", - " \"ref\": \"task_r20_5-60\"\n", - " }\n", - " }, \n", - " \"p000004\": {\n", - " \"loop\": 10, \n", - " \"run\": 65000, \n", - " \"timer\": {\n", - " \"period\": 100000, \n", - " \"ref\": \"task_r20_5-60\"\n", - " }\n", - " }\n", - " }, \n", - " \"policy\": \"SCHED_OTHER\"\n", - " }\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "# Inspect the JSON file used to run the application\n", - "with open('{}/simple_00.json'.format(te.res_dir), 'r') as fh:\n", - " rtapp_json = json.load(fh, )\n", - "logging.info('Generated RTApp JSON file:')\n", - "print json.dumps(rtapp_json, indent=4, sort_keys=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:29:54 INFO : Energy: /home/derkling/Code/lisa/results/20160225_182940/energy.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"LITTLE\": \"9.734375\", \n", - " \"big\": \"4.190309\"\n", - "}\n" - ] - } - ], - "source": [ - "# Dump the energy measured for the LITTLE and big clusters\n", - "logging.info('Energy: %s', nrg_report.report_file)\n", - "print json.dumps(nrg_report.channels, indent=4, sort_keys=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:29:54 INFO : Platform description: /home/derkling/Code/lisa/results/20160225_182940/platform.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"clusters\": {\n", - " \"big\": [\n", - " 1, \n", - " 2\n", - " ], \n", - " \"little\": [\n", - " 0, \n", - " 3, \n", - " 4, \n", - " 5\n", - " ]\n", - " }, \n", - " \"cpus_count\": 6, \n", - " \"freqs\": {\n", - " \"big\": [\n", - " 450000, \n", - " 625000, \n", - " 800000, \n", - " 950000, \n", - " 1100000\n", - " ], \n", - " \"little\": [\n", - " 450000, \n", - " 575000, \n", - " 700000, \n", - " 775000, \n", - " 850000\n", - " ]\n", - " }, \n", - " \"nrg_model\": {\n", - " \"big\": {\n", - " \"cluster\": {\n", - " \"nrg_max\": 64\n", - " }, \n", - " \"cpu\": {\n", - " \"cap_max\": 1024, \n", - " \"nrg_max\": 616\n", - " }\n", - " }, \n", - " \"little\": {\n", - " \"cluster\": {\n", - " \"nrg_max\": 57\n", - " }, \n", - " \"cpu\": {\n", - " \"cap_max\": 447, \n", - " \"nrg_max\": 93\n", - " }\n", - " }\n", - " }, \n", - " \"topology\": [\n", - " [\n", - " 0, \n", - " 3, \n", - " 4, \n", - " 5\n", - " ], \n", - " [\n", - " 1, \n", - " 2\n", - " ]\n", - " ]\n", - "}\n" - ] - } - ], - "source": [ - "# Dump the platform descriptor, which could be useful for further analysis\n", - "# of the generated results\n", - "logging.info('Platform description: %s', plt_file)\n", - "print json.dumps(plt, indent=4, sort_keys=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Trace inspection" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Suport for FTrace events parsing and visualization\n", - "import trappy\n", - "\n", - "# NOTE: The interactive trace visualization is available only if you run\n", - "# the workload to generate a new trace-file\n", - "trappy.plotter.plot_trace(te.res_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# RTApp task performance plots" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "06:29:55 INFO : PerfIndex, Task [task_p20] avg: 0.59, std: 0.00\n", - "06:29:55 INFO : PerfIndex, Task [task_r20_5-60] avg: -0.25, std: 1.33\n" - ] - }, - { - "data": { - "image/png": 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Ar2fmJTNkm8qmlBOW3SQiHkbpo/7UhPWOAP4xIu4La2ZZPL5mgBs/52DtSVl+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\n7n5eg9fPqP6VnwEAGVRR0fQySKd8XMjqS0kXufvC8JS3Z81sjru/Xru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QWTXkhTqhKAZ6ZoKke5WmSA9TGn3Lj+CeHREbK53UaUOlwrTkZ5L+KukPtpfp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\nwuVnnHGGRo4cqUcffVR/+ct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0FAPoWqv2FAP1wPsd6HNF9xTbHiPpdkkfiYi3bV8j6U8R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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Support for performance analysis of RTApp workloads\n", - "from perf_analysis import PerfAnalysis\n", - "\n", - "# Parse the RT-App generate log files to compute performance metrics\n", - "pa = PerfAnalysis(te.res_dir)\n", - "\n", - "# For each task which has generated a logfile, plot its performance metrics\n", - "for task in pa.tasks():\n", - " pa.plotPerf(task, \"Performance plots for task [{}] \".format(task))" - ] - } - ], - "metadata": { - "celltoolbar": "Raw Cell Format", - "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 -}