| { |
| "cells": [ |
| { |
| "cell_type": "code", |
| "execution_count": 1, |
| "metadata": { |
| "collapsed": false, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "import logging\n", |
| "from conf import LisaLogging\n", |
| "LisaLogging.setup()\n", |
| "\n", |
| "# Generate plots inline\n", |
| "%matplotlib inline\n", |
| "\n", |
| "import os" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# Target Connectivity" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Board specific settings" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Boards specific settings can be collected into a JSON\n", |
| "platform description file:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "total 32\r\n", |
| "drwxrwxr-x 2 derkling derkling 4096 Sep 23 12:12 .\r\n", |
| "drwxrwxr-x 5 derkling derkling 4096 Sep 23 15:58 ..\r\n", |
| "-rw------- 1 derkling derkling 896 Sep 23 12:12 hikey.json\r\n", |
| "-rw-rw-r-- 1 derkling derkling 468 Jan 22 2016 juno.json\r\n", |
| "-rw-rw-r-- 1 derkling derkling 427 Aug 24 12:52 marlin.json\r\n", |
| "-rw-rw-r-- 1 derkling derkling 176 Aug 31 12:18 nexus5x.json\r\n", |
| "-rw-rw-r-- 1 derkling derkling 473 Feb 2 2016 oak.json\r\n", |
| "-rw-rw-r-- 1 derkling derkling 473 Jan 22 2016 tc2.json\r\n" |
| ] |
| } |
| ], |
| "source": [ |
| "!ls -la $LISA_HOME/libs/utils/platforms/" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "{\r\n", |
| "\t// HiKey boards have two SMP clusters\r\n", |
| "\t// Even being an SMP platform, being a two cluster system\r\n", |
| "\t// we can still load the devlib's bl module to get access\r\n", |
| "\t// to all the per-cluster functions it exposes\r\n", |
| "\t\"board\" : {\r\n", |
| "\t\t\"cores\" : [\r\n", |
| "\t\t\t\"A53_0\", \"A53_0\", \"A53_0\", \"A53_0\",\r\n", |
| "\t\t\t\"A53_1\", \"A53_1\", \"A53_1\", \"A53_1\"\r\n", |
| "\t\t],\r\n", |
| "\t\t\"big_core\" : \"A53_1\",\r\n", |
| "\t\t\"modules\" : [ \"bl\", \"cpufreq\" ]\r\n", |
| "\t},\r\n", |
| "\r\n", |
| "\t// Energy Model related functions requires cluster\r\n", |
| "\t// to be named \"big.LITTLE\". Thus, for the time being,\r\n", |
| "\t// let's use this naming also for HiKey. This is going\r\n", |
| "\t// to be updated once we introduce proper SMP and\r\n", |
| "\t// multi-cluster support.\r\n", |
| "\t\"nrg_model\": {\r\n", |
| "\t\t\"little\": {\r\n", |
| "\t\t\t\"cluster\": {\r\n", |
| "\t\t\t\t\"nrg_max\": 112\r\n", |
| "\t\t\t},\r\n", |
| "\t\t\t\"cpu\": {\r\n", |
| "\t\t\t\t\"nrg_max\": 670, \"cap_max\": 1024\r\n", |
| "\t\t\t}\r\n", |
| "\t\t},\r\n", |
| "\t\t\"big\": {\r\n", |
| "\t\t\t\"cluster\": {\r\n", |
| "\t\t\t\t\"nrg_max\": 112\r\n", |
| "\t\t\t},\r\n", |
| "\t\t\t\"cpu\": {\r\n", |
| "\t\t\t\t\"nrg_max\": 670,\r\n", |
| "\t\t\t\t\"cap_max\": 1024\r\n", |
| "\t\t\t}\r\n", |
| "\t\t}\r\n", |
| "\t}\r\n", |
| "\r\n", |
| "}\r\n", |
| "\r\n", |
| "// vim: set tabstop=4:\r\n" |
| ] |
| } |
| ], |
| "source": [ |
| "!cat $LISA_HOME/libs/utils/platforms/hikey.json" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Single configuration dictionary" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": { |
| "collapsed": false, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "List of devices attached\r\n", |
| "00b43d0b08a8a4b8\tdevice\r\n", |
| "607A87C400055E6E\tdevice\r\n", |
| "\r\n" |
| ] |
| } |
| ], |
| "source": [ |
| "# Check which Android devices are available\n", |
| "!adb devices" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "metadata": { |
| "collapsed": true, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "ADB_DEVICE = '00b43d0b08a8a4b8'" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [], |
| "source": [ |
| "# Unified configuration dictionary\n", |
| "my_conf = {\n", |
| "\n", |
| " # Target platform\n", |
| " \"platform\" : 'android',\n", |
| "\n", |
| " # Location of external tools (adb, fastboot, systrace, etc)\n", |
| " # These properties can be used to override the environment variables:\n", |
| " # ANDROID_HOME and CATAPULT_HOME\n", |
| " \"ANDROID_HOME\" : \"/opt/android-sdk-linux\",\n", |
| " \"CATAPULT_HOME\" : \"/home/derkling/Code/catapult\",\n", |
| "\n", |
| " # Boards specific settings can be collected into a JSON\n", |
| " # platform description file, to be placed under:\n", |
| " # LISA_HOME/libs/utils/platforms\n", |
| " \"board\" : 'hikey',\n", |
| " \n", |
| " # If you have multiple Android device connected, here\n", |
| " # we can specify which one to target\n", |
| " \"device\" : ADB_DEVICE,\n", |
| "\n", |
| " # Folder where all the results will be collected\n", |
| " \"results_dir\" : \"ReleaseNotes_v16.09\",\n", |
| "}" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:26:53,404 INFO : Target - Using base path: /home/derkling/Code/lisa\n", |
| "2016-09-23 18:26:53,404 INFO : Target - Loading custom (inline) target configuration\n", |
| "2016-09-23 18:26:53,404 INFO : Target - External tools using:\n", |
| "2016-09-23 18:26:53,405 INFO : Target - ANDROID_HOME: /opt/android-sdk-linux\n", |
| "2016-09-23 18:26:53,405 INFO : Target - CATAPULT_HOME: /home/derkling/Code/lisa/tools/catapult\n", |
| "2016-09-23 18:26:53,405 INFO : Platform - Loading board:\n", |
| "2016-09-23 18:26:53,405 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/hikey.json\n", |
| "2016-09-23 18:26:53,406 INFO : Target - Devlib modules to load: [u'bl', u'cpufreq']\n", |
| "2016-09-23 18:26:53,406 INFO : Target - Connecting Android target [607A87C400055E6E]\n", |
| "2016-09-23 18:26:53,406 INFO : Target - Connection settings:\n", |
| "2016-09-23 18:26:53,406 INFO : Target - {'device': '607A87C400055E6E'}\n", |
| "2016-09-23 18:26:53,951 INFO : Target - Initializing target workdir:\n", |
| "2016-09-23 18:26:53,952 INFO : Target - /data/local/tmp/devlib-target\n", |
| "2016-09-23 18:26:54,746 INFO : Target - Topology:\n", |
| "2016-09-23 18:26:54,747 INFO : Target - [[0, 1, 2, 3], [4, 5, 6, 7]]\n", |
| "2016-09-23 18:26:54,946 INFO : Platform - Loading default EM:\n", |
| "2016-09-23 18:26:54,946 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/hikey.json\n", |
| "2016-09-23 18:26:54,947 WARNING : TestEnv - Wipe previous contents of the results folder:\n", |
| "2016-09-23 18:26:54,947 WARNING : TestEnv - /home/derkling/Code/lisa/results/ReleaseNotes_v16.09\n", |
| "2016-09-23 18:26:54,960 INFO : AEP - AEP configuration\n", |
| "2016-09-23 18:26:54,960 INFO : AEP - {'instrument': 'aep', 'channel_map': {'LITTLE': 'LITTLE'}, 'conf': {'resistor_values': [0.033], 'device_entry': '/dev/ttyACM0'}}\n", |
| "2016-09-23 18:26:54,967 INFO : AEP - Channels selected for energy sampling:\n", |
| "[CHAN(LITTLE_current), CHAN(LITTLE_power), CHAN(LITTLE_voltage)]\n", |
| "2016-09-23 18:26:54,967 INFO : TestEnv - Set results folder to:\n", |
| "2016-09-23 18:26:54,968 INFO : TestEnv - /home/derkling/Code/lisa/results/ReleaseNotes_v16.09\n", |
| "2016-09-23 18:26:54,968 INFO : TestEnv - Experiment results available also in:\n", |
| "2016-09-23 18:26:54,968 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n" |
| ] |
| } |
| ], |
| "source": [ |
| "from env import TestEnv\n", |
| "\n", |
| "te = TestEnv(my_conf, force_new=True)\n", |
| "target = te.target" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# Energy Meters Support" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "- Simple unified interface for multiple acquisition board\n", |
| " - exposes two simple methods: **reset()** and **report()**\n", |
| " - reporting **energy** consumptions\n", |
| " - reports additional info supported by the specific probe,<br>\n", |
| " e.g. collected samples, stats on current and voltages, etc." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 8, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "from time import sleep\n", |
| "\n", |
| "def sample_energy(energy_meter, time_s):\n", |
| " \n", |
| " # Reset the configured energy counters\n", |
| " energy_meter.reset()\n", |
| "\n", |
| " # Run the workload you want to measure\n", |
| " #\n", |
| " # In this simple example we just wait some time while the\n", |
| " # energy counters accumulate power samples\n", |
| " sleep(time_s)\n", |
| "\n", |
| " # Read and report the measured energy (since last reset)\n", |
| " return energy_meter.report(te.res_dir)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "- Channels mapping support\n", |
| " - allows to give a custom name to each channel used" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## ARM Energy Probe (AEP)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Requirements:\n", |
| "1. the **caimin binary tool** must be availabe in PATH<br>\n", |
| " https://github.com/ARM-software/lisa/wiki/Energy-Meters-Requirements#arm-energy-probe-aep\n", |
| "2. the **ttyACMx device** created once you plug in the AEP device" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 15, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "crw-rw---- 1 root dialout 166, 1 Sep 23 18:28 /dev/ttyACM1\r\n" |
| ] |
| } |
| ], |
| "source": [ |
| "!ls -la /dev/ttyACM*" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 16, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "ACM_DEVICE = '/dev/ttyACM1'" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "### Direct usage" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 17, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:28:22,422 INFO : AEP - AEP configuration\n", |
| "2016-09-23 18:28:22,422 INFO : AEP - {'channel_map': {'BAT': 'CH0'}, 'conf': {'resistor_values': [0.01], 'device_entry': '/dev/ttyACM1'}}\n", |
| "2016-09-23 18:28:22,472 INFO : AEP - Channels selected for energy sampling:\n", |
| "[CHAN(BAT_current), CHAN(BAT_power), CHAN(BAT_voltage)]\n" |
| ] |
| } |
| ], |
| "source": [ |
| "# Energy Meters Configuration for ARM Energy Probe\n", |
| "aep_conf = {\n", |
| " 'conf' : {\n", |
| " # Value of the shunt resistor [Ohm] for each channel\n", |
| " 'resistor_values' : [0.010],\n", |
| " # Device entry assigned to the probe on the host\n", |
| " 'device_entry' : ACM_DEVICE,\n", |
| " },\n", |
| " 'channel_map' : {\n", |
| " 'BAT' : 'CH0'\n", |
| " }\n", |
| "}\n", |
| "\n", |
| "from energy import AEP\n", |
| "ape_em = AEP(target, aep_conf, '/tmp')" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 18, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "EnergyReport(channels={'BAT': 0.00341733929419313}, report_file='/home/derkling/Code/lisa/results/ReleaseNotes_v16.09/energy.json')\n" |
| ] |
| } |
| ], |
| "source": [ |
| "nrg_report = sample_energy(ape_em, 2)\n", |
| "print nrg_report" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 19, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "{\r\n", |
| " \"BAT\": 0.00341733929419313\r\n", |
| "}" |
| ] |
| } |
| ], |
| "source": [ |
| "!cat $nrg_report.report_file" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "### Usage via TestEnv" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 43, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [], |
| "source": [ |
| "my_conf = {\n", |
| "\n", |
| " # Configure the energy meter to use\n", |
| " \"emeter\" : {\n", |
| " \n", |
| " # Require usage of an AEP meter\n", |
| " \"instrument\" : \"aep\",\n", |
| " \n", |
| " # Configuration parameters require by the AEP device\n", |
| " \"conf\" : {\n", |
| " # Value of the shunt resistor in Ohm\n", |
| " 'resistor_values' : [0.099],\n", |
| " # Device entry assigned to the probe on the host\n", |
| " 'device_entry' : ACM_DEVICE,\n", |
| " },\n", |
| " \n", |
| " # Map AEP's channels to logical names (used to generate reports)\n", |
| " 'channel_map' : {\n", |
| " 'BAT' : 'CH0'\n", |
| " }\n", |
| " },\n", |
| "\n", |
| " # Other target configurations\n", |
| " \"platform\" : 'android',\n", |
| " \"board\" : 'hikey',\n", |
| " \"device\" : ADB_DEVICE,\n", |
| " \"results_dir\" : \"ReleaseNotes_v16.09\",\n", |
| " \"ANDROID_HOME\" : \"/opt/android-sdk-linux\",\n", |
| " \"CATAPULT_HOME\" : \"/home/derkling/Code/catapult\",\n", |
| "}" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 46, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:53:00,113 INFO : Target - Using base path: /home/derkling/Code/lisa\n", |
| "2016-09-23 18:53:00,113 INFO : Target - Loading custom (inline) target configuration\n", |
| "2016-09-23 18:53:00,113 INFO : Target - External tools using:\n", |
| "2016-09-23 18:53:00,114 INFO : Target - ANDROID_HOME: /opt/android-sdk-linux\n", |
| "2016-09-23 18:53:00,114 INFO : Target - CATAPULT_HOME: /home/derkling/Code/lisa/tools/catapult\n", |
| "2016-09-23 18:53:00,114 INFO : Platform - Loading board:\n", |
| "2016-09-23 18:53:00,115 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/hikey.json\n", |
| "2016-09-23 18:53:00,116 INFO : Target - Devlib modules to load: [u'bl', u'cpufreq']\n", |
| "2016-09-23 18:53:00,116 INFO : Target - Connecting Android target [00b43d0b08a8a4b8]\n", |
| "2016-09-23 18:53:00,117 INFO : Target - Connection settings:\n", |
| "2016-09-23 18:53:00,118 INFO : Target - {'device': '00b43d0b08a8a4b8'}\n", |
| "2016-09-23 18:53:00,352 INFO : Target - Initializing target workdir:\n", |
| "2016-09-23 18:53:00,353 INFO : Target - /data/local/tmp/devlib-target\n", |
| "2016-09-23 18:53:01,166 INFO : Target - Topology:\n", |
| "2016-09-23 18:53:01,166 INFO : Target - [[0, 1, 2, 3], [4, 5, 6, 7]]\n", |
| "2016-09-23 18:53:01,275 INFO : Platform - Loading default EM:\n", |
| "2016-09-23 18:53:01,276 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/hikey.json\n", |
| "2016-09-23 18:53:01,277 WARNING : TestEnv - Wipe previous contents of the results folder:\n", |
| "2016-09-23 18:53:01,277 WARNING : TestEnv - /home/derkling/Code/lisa/results/ReleaseNotes_v16.09\n", |
| "2016-09-23 18:53:01,278 INFO : AEP - AEP configuration\n", |
| "2016-09-23 18:53:01,278 INFO : AEP - {'instrument': 'aep', 'channel_map': {'BAT': 'CH0'}, 'conf': {'resistor_values': [0.099], 'device_entry': '/dev/ttyACM1'}}\n", |
| "2016-09-23 18:53:01,283 INFO : AEP - Channels selected for energy sampling:\n", |
| "[CHAN(BAT_current), CHAN(BAT_power), CHAN(BAT_voltage)]\n", |
| "2016-09-23 18:53:01,284 INFO : TestEnv - Set results folder to:\n", |
| "2016-09-23 18:53:01,284 INFO : TestEnv - /home/derkling/Code/lisa/results/ReleaseNotes_v16.09\n", |
| "2016-09-23 18:53:01,284 INFO : TestEnv - Experiment results available also in:\n", |
| "2016-09-23 18:53:01,284 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n" |
| ] |
| } |
| ], |
| "source": [ |
| "from env import TestEnv\n", |
| "\n", |
| "te = TestEnv(my_conf, force_new=True)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [], |
| "source": [ |
| "for i in xrange(1,11):\n", |
| " nrg_report = sample_energy(te.emeter, 1)\n", |
| " nrg_bat = float(nrg_report.channels['BAT'])\n", |
| " print \"Sample {:2d}: {:.3f}\".format(i, nrg_bat)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## BayLibre's ACME board (ACME)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Requirements:\n", |
| "1. the **iio-capture tool** must be available in PATH<br>\n", |
| " https://github.com/ARM-software/lisa/wiki/Energy-Meters-Requirements#iiocapture---baylibre-acme-cape\n", |
| "2. the ACME CAPE should be reacable by network" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 22, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "64 bytes from 192.168.0.1: icmp_seq=1 ttl=64 time=1.60 ms\r\n" |
| ] |
| } |
| ], |
| "source": [ |
| "!ping -c1 baylibre-acme.local | grep '64 bytes'" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "### Direct usage" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 23, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:49:31,422 INFO : ACME - ACME configuration:\n", |
| "2016-09-23 18:49:31,422 INFO : ACME - binary: iio-capture\n", |
| "2016-09-23 18:49:31,422 INFO : ACME - device: baylibre-acme.local\n", |
| "2016-09-23 18:49:31,423 INFO : ACME - channels:\n", |
| "2016-09-23 18:49:31,423 INFO : ACME - Device1 (iio:device1)\n", |
| "2016-09-23 18:49:31,423 INFO : ACME - Device0 (iio:device0)\n" |
| ] |
| } |
| ], |
| "source": [ |
| "# Energy Meters Configuration for BayLibre's ACME\n", |
| "acme_conf = {\n", |
| " \"conf\" : {\n", |
| " #'iio-capture' : '/usr/bin/iio-capture',\n", |
| " #'ip_address' : 'baylibre-acme.local',\n", |
| " },\n", |
| " \"channel_map\" : {\n", |
| " \"Device0\" : 0,\n", |
| " \"Device1\" : 1,\n", |
| " },\n", |
| "}\n", |
| "\n", |
| "from energy import ACME\n", |
| "acme_em = ACME(target, acme_conf, '/tmp')" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 24, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "EnergyReport(channels={'Device1': 654.82, 'Device0': 0.0}, report_file='/home/derkling/Code/lisa/results/ReleaseNotes_v16.09/energy.json')\n" |
| ] |
| } |
| ], |
| "source": [ |
| "nrg_report = sample_energy(acme_em, 2)\n", |
| "print nrg_report" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 25, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "{\r\n", |
| " \"Device0\": 0.0, \r\n", |
| " \"Device1\": 654.82\r\n", |
| "}" |
| ] |
| } |
| ], |
| "source": [ |
| "!cat $nrg_report.report_file" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "### Usage via TestEnv" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 4, |
| "metadata": { |
| "collapsed": false, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "my_conf = {\n", |
| "\n", |
| " # Configure the energy meter to use\n", |
| " \"emeter\" : {\n", |
| " \n", |
| " # Require usage of an AEP meter\n", |
| " \"instrument\" : \"acme\",\n", |
| " \n", |
| " \"conf\" : {\n", |
| " #'iio-capture' : '/usr/bin/iio-capture',\n", |
| " #'ip_address' : 'baylibre-acme.local',\n", |
| " },\n", |
| " 'channel_map' : {\n", |
| " 'Device0' : 0,\n", |
| " 'Device1' : 1,\n", |
| " },\n", |
| " },\n", |
| "\n", |
| " # Other target configurations\n", |
| " \"platform\" : 'android',\n", |
| " \"board\" : 'hikey',\n", |
| " \"device\" : ADB_DEVICE,\n", |
| " \"results_dir\" : \"ReleaseNotes_v16.09\",\n", |
| " \"ANDROID_HOME\" : \"/opt/android-sdk-linux\",\n", |
| " \"CATAPULT_HOME\" : \"/home/derkling/Code/catapult\",\n", |
| "}" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 5, |
| "metadata": { |
| "collapsed": false, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:54:37,835 INFO : Target - Using base path: /home/derkling/Code/lisa\n", |
| "2016-09-23 18:54:37,835 INFO : Target - Loading custom (inline) target configuration\n", |
| "2016-09-23 18:54:37,836 INFO : Target - External tools using:\n", |
| "2016-09-23 18:54:37,836 INFO : Target - ANDROID_HOME: /opt/android-sdk-linux\n", |
| "2016-09-23 18:54:37,836 INFO : Target - CATAPULT_HOME: /home/derkling/Code/lisa/tools/catapult\n", |
| "2016-09-23 18:54:37,837 INFO : Platform - Loading board:\n", |
| "2016-09-23 18:54:37,837 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/hikey.json\n", |
| "2016-09-23 18:54:37,838 INFO : Target - Devlib modules to load: [u'bl', u'cpufreq']\n", |
| "2016-09-23 18:54:37,838 INFO : Target - Connecting Android target [00b43d0b08a8a4b8]\n", |
| "2016-09-23 18:54:37,839 INFO : Target - Connection settings:\n", |
| "2016-09-23 18:54:37,839 INFO : Target - {'device': '00b43d0b08a8a4b8'}\n", |
| "2016-09-23 18:54:38,177 INFO : Target - Initializing target workdir:\n", |
| "2016-09-23 18:54:38,177 INFO : Target - /data/local/tmp/devlib-target\n", |
| "2016-09-23 18:54:39,025 INFO : Target - Topology:\n", |
| "2016-09-23 18:54:39,026 INFO : Target - [[0, 1, 2, 3], [4, 5, 6, 7]]\n", |
| "2016-09-23 18:54:39,134 INFO : Platform - Loading default EM:\n", |
| "2016-09-23 18:54:39,135 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/hikey.json\n", |
| "2016-09-23 18:54:39,135 WARNING : TestEnv - Wipe previous contents of the results folder:\n", |
| "2016-09-23 18:54:39,135 WARNING : TestEnv - /home/derkling/Code/lisa/results/ReleaseNotes_v16.09\n", |
| "2016-09-23 18:54:39,136 INFO : ACME - ACME configuration:\n", |
| "2016-09-23 18:54:39,136 INFO : ACME - binary: iio-capture\n", |
| "2016-09-23 18:54:39,136 INFO : ACME - device: baylibre-acme.local\n", |
| "2016-09-23 18:54:39,137 INFO : ACME - channels:\n", |
| "2016-09-23 18:54:39,137 INFO : ACME - Device1 (iio:device1)\n", |
| "2016-09-23 18:54:39,137 INFO : ACME - Device0 (iio:device0)\n", |
| "2016-09-23 18:54:39,142 INFO : TestEnv - Set results folder to:\n", |
| "2016-09-23 18:54:39,142 INFO : TestEnv - /home/derkling/Code/lisa/results/ReleaseNotes_v16.09\n", |
| "2016-09-23 18:54:39,143 INFO : TestEnv - Experiment results available also in:\n", |
| "2016-09-23 18:54:39,143 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n" |
| ] |
| } |
| ], |
| "source": [ |
| "from env import TestEnv\n", |
| "\n", |
| "te = TestEnv(my_conf, force_new=True)\n", |
| "target = te.target" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 28, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Sample 1: 342.110\n", |
| "Sample 2: 325.660\n", |
| "Sample 3: 324.120\n", |
| "Sample 4: 369.300\n", |
| "Sample 5: 331.140\n", |
| "Sample 6: 315.130\n", |
| "Sample 7: 326.100\n", |
| "Sample 8: 345.180\n", |
| "Sample 9: 328.730\n", |
| "Sample 10: 328.510\n" |
| ] |
| } |
| ], |
| "source": [ |
| "for i in xrange(1,11):\n", |
| " nrg_report = sample_energy(te.emeter, 1)\n", |
| " nrg_bat = float(nrg_report.channels['Device1'])\n", |
| " print \"Sample {:2d}: {:.3f}\".format(i, nrg_bat)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# Android Integration" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "A new Android library has been added which provides APIs to:\n", |
| " - simplify the interaction with a device\n", |
| " - execute interesting workloads and benchmarks\n", |
| " - make it easy the integration of new workloads and benchmarks\n", |
| " \n", |
| "Not intended to replace WA, but instead to provide a Python based<br>\n", |
| "programming interface to **automate reproducible experiments** on<br>\n", |
| "and Android device." |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## System control APIs" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Supported functions:\n", |
| " back\n", |
| " force_stop\n", |
| " gfxinfo_get\n", |
| " gfxinfo_reset\n", |
| " home\n", |
| " hswipe\n", |
| " list_packages\n", |
| " menu\n", |
| " monkey\n", |
| " packages_info\n", |
| " set_airplane_mode\n", |
| " start_action\n", |
| " start_activity\n", |
| " start_app\n", |
| " tap\n", |
| " vswipe\n" |
| ] |
| } |
| ], |
| "source": [ |
| "from android import System\n", |
| "\n", |
| "print \"Supported functions:\"\n", |
| "for f in dir(System):\n", |
| " if \"__\" in f:\n", |
| " continue\n", |
| " print \" \", f" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Capturing main useful actions, for example:\n", |
| " - ensure we set AIRPLAIN_MODE before measuring scheduler energy\n", |
| " - provide simple support for input actions (relative swipes)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 34, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "# logging.getLogger().setLevel(logging.DEBUG)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [], |
| "source": [ |
| "# Example (use tab to complete)\n", |
| "System." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [], |
| "source": [ |
| "System.menu(target)\n", |
| "System.back(target)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 8, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [], |
| "source": [ |
| "youtube_apk = System.list_packages(target, 'YouTube')\n", |
| "if youtube_apk:\n", |
| " System.start_app(target, youtube_apk[0])" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 45, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "logging.getLogger().setLevel(logging.INFO)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Screen control APIs" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 9, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Supported functions:\n", |
| " set_brightness\n", |
| " set_defaults\n", |
| " set_dim\n", |
| " set_orientation\n", |
| " set_timeout\n" |
| ] |
| } |
| ], |
| "source": [ |
| "from android import Screen\n", |
| "\n", |
| "print \"Supported functions:\"\n", |
| "for f in dir(Screen):\n", |
| " if \"__\" in f:\n", |
| " continue\n", |
| " print \" \", f" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "# logging.getLogger().setLevel(logging.DEBUG)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [], |
| "source": [ |
| "# Example (use TAB to complete)\n", |
| "Screen." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 10, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:55:19,490 INFO : Set brightness: 100%\n" |
| ] |
| } |
| ], |
| "source": [ |
| "Screen.set_brightness(target, auto=False, percent=100)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 11, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:55:19,494 INFO : Force manual orientation\n", |
| "2016-09-23 18:55:19,494 INFO : Set orientation: LANDSCAPE\n" |
| ] |
| } |
| ], |
| "source": [ |
| "Screen.set_orientation(target, auto=False, portrait=False)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "# logging.getLogger().setLevel(logging.INFO)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Workloads Execution" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "A simple workload class allows to easily add a wrapper for the exection\n", |
| "of a specific Android application.\n", |
| "\n", |
| "**NOTE:** To keep things simple, LISA does not provide APKs installation support.\n", |
| "\n", |
| "*All the exposes APIs assume that the required packages are already installed<br>\n", |
| "in the target. Whenever a package is missing, LISA reports that and it's up<br>\n", |
| "to the user to install it before using it.*\n", |
| "\n", |
| "A wrapper class usually requires to specify:\n", |
| "- a package name<br>\n", |
| " which will be used to verify if the APK is available in the target\n", |
| "- a run method<br>\n", |
| " which usually exploits the other Android APIs to defined a **reproducible\n", |
| " exection** of the specified workload\n", |
| " \n", |
| "A reproducible experiment should take care of:\n", |
| "- setups wirelesse **connections status**\n", |
| "- setup **screen orientation and backlight** level\n", |
| "- properly collect **energy measurements** across the sensible part of the experiment\n", |
| "- possibly collect **frames statistics** whenever available" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "### Example of YouTube integration" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": { |
| "collapsed": true |
| }, |
| "source": [ |
| "Here is an example wrapper which allows to play a YouTube<br>\n", |
| "video for a specified number of seconds:\n", |
| "\n", |
| "https://github.com/ARM-software/lisa/blob/master/libs/utils/android/workloads/youtube.py" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "### Example usage of the Workload API" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 40, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "# logging.getLogger().setLevel(logging.DEBUG)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 12, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:55:25,384 WARNING : Workload - Package [com.android.benchmark] not installed\n", |
| "2016-09-23 18:55:25,385 WARNING : Workload - Workload [Jankbench] disabled\n", |
| "2016-09-23 18:55:25,385 INFO : Workload - Workloads available on target:\n", |
| "2016-09-23 18:55:25,385 INFO : Workload - ['YouTube', 'UiBench']\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/plain": [ |
| "['YouTube', 'UiBench']" |
| ] |
| }, |
| "execution_count": 12, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "from android import Workload\n", |
| "\n", |
| "# Get the list of available workloads\n", |
| "wloads = Workload(te)\n", |
| "wloads.availables(target)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 16, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:55:58,229 INFO : Workload - Workloads available on target:\n", |
| "2016-09-23 18:55:58,230 INFO : Workload - ['YouTube', 'UiBench']\n", |
| "2016-09-23 18:56:00,090 INFO : Force manual orientation\n", |
| "2016-09-23 18:56:00,091 INFO : Set orientation: LANDSCAPE\n", |
| "2016-09-23 18:56:05,068 INFO : Play video for 16 [s]\n", |
| "2016-09-23 18:56:27,121 INFO : Set orientation: AUTO\n" |
| ] |
| } |
| ], |
| "source": [ |
| "yt = Workload.get(te, name='YouTube')\n", |
| "\n", |
| "# Playback big bug bunny for 15s starting from 1:20s\n", |
| "video_id = 'XSGBVzeBUbk'\n", |
| "video_url = \"https://youtu.be/{}?t={}s\".format(video_id, 80)\n", |
| "\n", |
| "# Play video and measure energy consumption\n", |
| "results = yt.run(te.res_dir,\n", |
| " video_url, video_duration_s=16,\n", |
| " collect='energy')" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 17, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/plain": [ |
| "('/home/derkling/Code/lisa/results/ReleaseNotes_v16.09/framestats.txt',\n", |
| " EnergyReport(channels={'Device1': 4363.82, 'Device0': 0.0}, report_file='/home/derkling/Code/lisa/results/ReleaseNotes_v16.09/energy.json'))" |
| ] |
| }, |
| "execution_count": 17, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "results" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 18, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Applications Graphics Acceleration Info:\r\n", |
| "Uptime: 135646296 Realtime: 248599085\r\n", |
| "\r\n", |
| "** Graphics info for pid 23414 [com.google.android.youtube] **\r\n", |
| "\r\n", |
| "Stats since: 135590611534135ns\r\n", |
| "Total frames rendered: 342\r\n", |
| "Janky frames: 59 (17.25%)\r\n", |
| "50th percentile: 8ms\r\n", |
| "90th percentile: 21ms\r\n", |
| "95th percentile: 40ms\r\n", |
| "99th percentile: 125ms\r\n", |
| "Number Missed Vsync: 21\r\n", |
| "Number High input latency: 0\r\n", |
| "Number Slow UI thread: 27\r\n", |
| "Number Slow bitmap uploads: 2\r\n", |
| "Number Slow issue draw commands: 28\r\n", |
| "HISTOGRAM: 5ms=70 6ms=32 7ms=35 8ms=38 9ms=31 10ms=21 11ms=21 12ms=14 13ms=10 14ms=7 15ms=3 16ms=4 17ms=3 18ms=3 19ms=7 20ms=4 21ms=7 22ms=0 23ms=3 24ms=1 25ms=2 26ms=2 27ms=1 28ms=0 29ms=2 30ms=0 31ms=0 32ms=0 34ms=2 36ms=0 38ms=0 40ms=2 42ms=0 44ms=0 46ms=2 48ms=1 53ms=2 57ms=2 61ms=0 65ms=1 69ms=0 73ms=0 77ms=2 81ms=0 85ms=2 89ms=0 93ms=0 97ms=0 101ms=0 105ms=0 109ms=0 113ms=0 117ms=0 121ms=1 125ms=1 129ms=0 133ms=0 150ms=2 200ms=0 250ms=1 300ms=0 350ms=0 400ms=0 450ms=0 500ms=0 550ms=0 600ms=0 650ms=0 700ms=0 750ms=0 800ms=0 850ms=0 900ms=0 950ms=0 1000ms=0 1050ms=0 1100ms=0 1150ms=0 1200ms=0 1250ms=0 1300ms=0 1350ms=0 1400ms=0 1450ms=0 1500ms=0 1550ms=0 1600ms=0 1650ms=0 1700ms=0 1750ms=0 1800ms=0 1850ms=0 1900ms=0 1950ms=0 2000ms=0 2050ms=0 2100ms=0 2150ms=0 2200ms=0 2250ms=0 2300ms=0 2350ms=0 2400ms=0 2450ms=0 2500ms=0 2550ms=0 2600ms=0 2650ms=0 2700ms=0 2750ms=0 2800ms=0 2850ms=0 2900ms=0 2950ms=0 3000ms=0 3050ms=0 3100ms=0 3150ms=0 3200ms=0 3250ms=0 3300ms=0 3350ms=0 3400ms=0 3450ms=0 3500ms=0 3550ms=0 3600ms=0 3650ms=0 3700ms=0 3750ms=0 3800ms=0 3850ms=0 3900ms=0 3950ms=0 4000ms=0 4050ms=0 4100ms=0 4150ms=0 4200ms=0 4250ms=0 4300ms=0 4350ms=0 4400ms=0 4450ms=0 4500ms=0 4550ms=0 4600ms=0 4650ms=0 4700ms=0 4750ms=0 4800ms=0 4850ms=0 4900ms=0 4950ms=0\r\n", |
| "\r\n", |
| "Caches:\r\n", |
| "Current memory usage / total memory usage (bytes):\r\n", |
| " TextureCache 3484348 / 58720256\r\n", |
| " LayerCache 0 / 33554432 (numLayers = 0)\r\n", |
| " Layers total 0 (numLayers = 0)\r\n", |
| " RenderBufferCache 0 / 8388608\r\n", |
| " GradientCache 16384 / 1048576\r\n", |
| " PathCache 0 / 16777216\r\n", |
| " TessellationCache 2232 / 1048576\r\n", |
| " TextDropShadowCache 0 / 6291456\r\n", |
| " PatchCache 64 / 131072\r\n", |
| " FontRenderer A8 1048576 / 1048576\r\n", |
| " FontRenderer RGBA 0 / 0\r\n", |
| " FontRenderer total 1048576 / 1048576\r\n", |
| "Other:\r\n", |
| " FboCache 0 / 0\r\n", |
| "Total memory usage:\r\n", |
| " 4551604 bytes, 4.34 MB\r\n", |
| "\r\n", |
| "\r\n", |
| "Pipeline=FrameBuilder\r\n", |
| "Profile data in ms:\r\n", |
| "\r\n", |
| "\tcom.google.android.youtube/com.google.android.apps.youtube.app.WatchWhileActivity/android.view.ViewRootImpl@74c8306 (visibility=0)\r\n", |
| "View hierarchy:\r\n", |
| "\r\n", |
| " com.google.android.youtube/com.google.android.apps.youtube.app.WatchWhileActivity/android.view.ViewRootImpl@74c8306\r\n", |
| " 275 views, 271.73 kB of display lists\r\n", |
| "\r\n", |
| "\r\n", |
| "Total ViewRootImpl: 1\r\n", |
| "Total Views: 275\r\n", |
| "Total DisplayList: 271.73 kB\r\n", |
| "\r\n" |
| ] |
| } |
| ], |
| "source": [ |
| "framestats = results[0]\n", |
| "!cat $framestats" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Benchmarks" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Android benchmarks can be integrated as standalone Notebook, like for example\n", |
| "what we provide for PCMark:\n", |
| " https://github.com/ARM-software/lisa/blob/master/ipynb/android/benchmarks/Android_PCMark.ipynb\n", |
| "\n", |
| "Alternatively we are adding other benchmarks as predefined Android workloads." |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "### UiBench support" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Here is an example of UiBench workload which can run a specified number\n", |
| "of tests:\n", |
| " " |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 19, |
| "metadata": { |
| "collapsed": false, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:56:28,488 INFO : Workload - Workloads available on target:\n", |
| "2016-09-23 18:56:28,489 INFO : Workload - ['YouTube', 'UiBench']\n", |
| "2016-09-23 18:56:30,765 WARNING : Failed to toggle airplane mode, permission denied.\n", |
| "2016-09-23 18:56:31,460 INFO : Force manual orientation\n", |
| "2016-09-23 18:56:31,461 INFO : Set orientation: PORTRAIT\n", |
| "2016-09-23 18:56:33,738 INFO : adb -s 00b43d0b08a8a4b8 logcat ActivityManager:* System.out:I *:S BENCH:*\n", |
| "2016-09-23 18:56:35,453 INFO : Benchmark [.GlTextureViewActivity] started, waiting 5 [s]\n", |
| "2016-09-23 18:56:46,364 INFO : Set orientation: AUTO\n" |
| ] |
| } |
| ], |
| "source": [ |
| "from android import Workload\n", |
| "\n", |
| "ui = Workload.get(te, name='UiBench')\n", |
| "\n", |
| "# Play video and measure energy consumption\n", |
| "results = ui.run(te.res_dir,\n", |
| " ui.test_GlTextureView,\n", |
| " duration_s=5,\n", |
| " collect='energy')" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 20, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/plain": [ |
| "('/home/derkling/Code/lisa/results/ReleaseNotes_v16.09/framestats.txt',\n", |
| " EnergyReport(channels={'Device1': 1479.17, 'Device0': 0.0}, report_file='/home/derkling/Code/lisa/results/ReleaseNotes_v16.09/energy.json'))" |
| ] |
| }, |
| "execution_count": 20, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "results" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 21, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Applications Graphics Acceleration Info:\r\n", |
| "Uptime: 135665445 Realtime: 248618234\r\n", |
| "\r\n", |
| "** Graphics info for pid 23836 [com.android.test.uibench] **\r\n", |
| "\r\n", |
| "Stats since: 135653517039151ns\r\n", |
| "Total frames rendered: 620\r\n", |
| "Janky frames: 580 (93.55%)\r\n", |
| "50th percentile: 19ms\r\n", |
| "90th percentile: 21ms\r\n", |
| "95th percentile: 22ms\r\n", |
| "99th percentile: 26ms\r\n", |
| "Number Missed Vsync: 2\r\n", |
| "Number High input latency: 0\r\n", |
| "Number Slow UI thread: 3\r\n", |
| "Number Slow bitmap uploads: 1\r\n", |
| "Number Slow issue draw commands: 574\r\n", |
| "HISTOGRAM: 5ms=11 6ms=2 7ms=3 8ms=1 9ms=4 10ms=2 11ms=1 12ms=1 13ms=3 14ms=3 15ms=5 16ms=8 17ms=45 18ms=109 19ms=192 20ms=147 21ms=49 22ms=14 23ms=8 24ms=3 25ms=1 26ms=3 27ms=0 28ms=1 29ms=2 30ms=1 31ms=0 32ms=0 34ms=0 36ms=0 38ms=0 40ms=0 42ms=0 44ms=0 46ms=0 48ms=1 53ms=0 57ms=0 61ms=0 65ms=0 69ms=0 73ms=0 77ms=0 81ms=0 85ms=0 89ms=0 93ms=0 97ms=0 101ms=0 105ms=0 109ms=0 113ms=0 117ms=0 121ms=0 125ms=0 129ms=0 133ms=0 150ms=0 200ms=0 250ms=0 300ms=0 350ms=0 400ms=0 450ms=0 500ms=0 550ms=0 600ms=0 650ms=0 700ms=0 750ms=0 800ms=0 850ms=0 900ms=0 950ms=0 1000ms=0 1050ms=0 1100ms=0 1150ms=0 1200ms=0 1250ms=0 1300ms=0 1350ms=0 1400ms=0 1450ms=0 1500ms=0 1550ms=0 1600ms=0 1650ms=0 1700ms=0 1750ms=0 1800ms=0 1850ms=0 1900ms=0 1950ms=0 2000ms=0 2050ms=0 2100ms=0 2150ms=0 2200ms=0 2250ms=0 2300ms=0 2350ms=0 2400ms=0 2450ms=0 2500ms=0 2550ms=0 2600ms=0 2650ms=0 2700ms=0 2750ms=0 2800ms=0 2850ms=0 2900ms=0 2950ms=0 3000ms=0 3050ms=0 3100ms=0 3150ms=0 3200ms=0 3250ms=0 3300ms=0 3350ms=0 3400ms=0 3450ms=0 3500ms=0 3550ms=0 3600ms=0 3650ms=0 3700ms=0 3750ms=0 3800ms=0 3850ms=0 3900ms=0 3950ms=0 4000ms=0 4050ms=0 4100ms=0 4150ms=0 4200ms=0 4250ms=0 4300ms=0 4350ms=0 4400ms=0 4450ms=0 4500ms=0 4550ms=0 4600ms=0 4650ms=0 4700ms=0 4750ms=0 4800ms=0 4850ms=0 4900ms=0 4950ms=0\r\n", |
| "\r\n", |
| "Caches:\r\n", |
| "Current memory usage / total memory usage (bytes):\r\n", |
| " TextureCache 0 / 58720256\r\n", |
| " LayerCache 0 / 33554432 (numLayers = 0)\r\n", |
| " Layer size 1080x1584; isTextureLayer()=1; texid=4 fbo=0; refs=1\r\n", |
| " Layers total 6842880 (numLayers = 1)\r\n", |
| " RenderBufferCache 0 / 8388608\r\n", |
| " GradientCache 0 / 1048576\r\n", |
| " PathCache 0 / 16777216\r\n", |
| " TessellationCache 0 / 1048576\r\n", |
| " TextDropShadowCache 0 / 6291456\r\n", |
| " PatchCache 0 / 131072\r\n", |
| " FontRenderer A8 1048576 / 1048576\r\n", |
| " FontRenderer RGBA 0 / 0\r\n", |
| " FontRenderer total 1048576 / 1048576\r\n", |
| "Other:\r\n", |
| " FboCache 0 / 0\r\n", |
| "Total memory usage:\r\n", |
| " 7891456 bytes, 7.53 MB\r\n", |
| "\r\n", |
| "\r\n", |
| "Pipeline=FrameBuilder\r\n", |
| "Profile data in ms:\r\n", |
| "\r\n", |
| "\tcom.android.test.uibench/com.android.test.uibench.MainActivity/android.view.ViewRootImpl@abf726f (visibility=8)\r\n", |
| "\tcom.android.test.uibench/com.android.test.uibench.GlTextureViewActivity/android.view.ViewRootImpl@31bc075 (visibility=0)\r\n", |
| "View hierarchy:\r\n", |
| "\r\n", |
| " com.android.test.uibench/com.android.test.uibench.MainActivity/android.view.ViewRootImpl@abf726f\r\n", |
| " 24 views, 23.25 kB of display lists\r\n", |
| "\r\n", |
| " com.android.test.uibench/com.android.test.uibench.GlTextureViewActivity/android.view.ViewRootImpl@31bc075\r\n", |
| " 14 views, 17.86 kB of display lists\r\n", |
| "\r\n", |
| "\r\n", |
| "Total ViewRootImpl: 2\r\n", |
| "Total Views: 38\r\n", |
| "Total DisplayList: 41.11 kB\r\n", |
| "\r\n" |
| ] |
| } |
| ], |
| "source": [ |
| "framestats = results[0]\n", |
| "!cat $framestats" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# Improved Trace Analysis support" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "The Trace module is a wrapper around the TRAPpy library which has been\n", |
| "updated to:\n", |
| "\n", |
| "- support parsing of **systrace** file format<br>\n", |
| " requires catapult locally installed<br>\n", |
| " https://github.com/catapult-project/catapult\n", |
| "- parsing and DataFrame generation for **custom events**" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Create an example trace" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "**NOTE:** the cells in this sections are required just to create\n", |
| " a trace file to be used by the following sections" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 22, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "# The following exanples uses an HiKey board\n", |
| "ADB_DEVICE = '607A87C400055E6E'" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "# logging.getLogger().setLevel(logging.DEBUG)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 23, |
| "metadata": { |
| "collapsed": false, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "# Unified configuration dictionary\n", |
| "my_conf = {\n", |
| " \n", |
| " # Tools required\n", |
| " \"tools\" : ['rt-app', 'trace-cmd'],\n", |
| " \n", |
| " # RTApp calibration\n", |
| " #\"modules\" : ['cpufreq'],\n", |
| " \"rtapp-calib\" : {\n", |
| " \"0\": 254, \"1\": 252, \"2\": 252, \"3\": 251,\n", |
| " \"4\": 251, \"5\": 252, \"6\": 251, \"7\": 251\n", |
| " },\n", |
| " \n", |
| " # FTrace configuration\n", |
| " \"ftrace\" : {\n", |
| " \n", |
| " # Events to trace\n", |
| " \"events\" : [\n", |
| " \"sched_switch\",\n", |
| " \"sched_wakeup\",\n", |
| " \"sched_wakeup_new\",\n", |
| " \"sched_wakeup_tracking\", \n", |
| " \"sched_stat_wait\",\n", |
| " \"sched_overutilized\",\n", |
| " \"sched_contrib_scale_f\",\n", |
| " \"sched_load_avg_cpu\",\n", |
| " \"sched_load_avg_task\",\n", |
| " \"sched_tune_config\",\n", |
| " \"sched_tune_filter\",\n", |
| " \"sched_tune_tasks_update\",\n", |
| " \"sched_tune_boostgroup_update\",\n", |
| " \"sched_boost_cpu\",\n", |
| " \"sched_boost_task\",\n", |
| " \"sched_energy_diff\",\n", |
| " \"cpu_capacity\",\n", |
| " \"cpu_frequency\",\n", |
| " \"cpu_idle\",\n", |
| " \"walt_update_task_ravg\",\n", |
| " \"walt_update_history\",\n", |
| " \"walt_migration_update_sum\",\n", |
| " ],\n", |
| " \n", |
| "# # Kernel functions to profile\n", |
| "# \"functions\" : [\n", |
| "# \"pick_next_task_fair\",\n", |
| "# \"select_task_rq_fair\",\n", |
| "# \"enqueue_task_fair\",\n", |
| "# \"update_curr_fair\",\n", |
| "# \"dequeue_task_fair\",\n", |
| "# ],\n", |
| " \n", |
| " # Per-CPU buffer configuration\n", |
| " \"buffsize\" : 10 * 1024,\n", |
| " },\n", |
| " \n", |
| " # Target platform\n", |
| " \"platform\" : 'android',\n", |
| " \"board\" : 'hikey',\n", |
| " \"device\" : ADB_DEVICE,\n", |
| " \"results_dir\" : \"ReleaseNotes_v16.09\",\n", |
| " \"ANDROID_HOME\" : \"/opt/android-sdk-linux\",\n", |
| " \"CATAPULT_HOME\" : \"/home/derkling/Code/catapult\",\n", |
| "}" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 24, |
| "metadata": { |
| "collapsed": false, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:57:53,914 INFO : Target - Using base path: /home/derkling/Code/lisa\n", |
| "2016-09-23 18:57:53,914 INFO : Target - Loading custom (inline) target configuration\n", |
| "2016-09-23 18:57:53,914 INFO : Target - External tools using:\n", |
| "2016-09-23 18:57:53,915 INFO : Target - ANDROID_HOME: /opt/android-sdk-linux\n", |
| "2016-09-23 18:57:53,915 INFO : Target - CATAPULT_HOME: /home/derkling/Code/lisa/tools/catapult\n", |
| "2016-09-23 18:57:53,915 INFO : Platform - Loading board:\n", |
| "2016-09-23 18:57:53,915 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/hikey.json\n", |
| "2016-09-23 18:57:53,916 INFO : Target - Devlib modules to load: [u'bl', u'cpufreq']\n", |
| "2016-09-23 18:57:53,916 INFO : Target - Connecting Android target [607A87C400055E6E]\n", |
| "2016-09-23 18:57:53,916 INFO : Target - Connection settings:\n", |
| "2016-09-23 18:57:53,917 INFO : Target - {'device': '607A87C400055E6E'}\n", |
| "2016-09-23 18:57:54,188 INFO : Target - Initializing target workdir:\n", |
| "2016-09-23 18:57:54,189 INFO : Target - /data/local/tmp/devlib-target\n", |
| "2016-09-23 18:57:56,401 INFO : Target - Topology:\n", |
| "2016-09-23 18:57:56,401 INFO : Target - [[0, 1, 2, 3], [4, 5, 6, 7]]\n", |
| "2016-09-23 18:57:56,601 INFO : Platform - Loading default EM:\n", |
| "2016-09-23 18:57:56,602 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/hikey.json\n", |
| "2016-09-23 18:57:56,985 WARNING : Event [sched_wakeup_tracking] not available for tracing\n", |
| "2016-09-23 18:57:56,993 INFO : FTrace - Enabled tracepoints:\n", |
| "2016-09-23 18:57:56,993 INFO : FTrace - sched_switch\n", |
| "2016-09-23 18:57:56,993 INFO : FTrace - sched_wakeup\n", |
| "2016-09-23 18:57:56,994 INFO : FTrace - sched_wakeup_new\n", |
| "2016-09-23 18:57:56,994 INFO : FTrace - sched_wakeup_tracking\n", |
| "2016-09-23 18:57:56,995 INFO : FTrace - sched_stat_wait\n", |
| "2016-09-23 18:57:56,995 INFO : FTrace - sched_overutilized\n", |
| "2016-09-23 18:57:56,995 INFO : FTrace - sched_contrib_scale_f\n", |
| "2016-09-23 18:57:56,996 INFO : FTrace - sched_load_avg_cpu\n", |
| "2016-09-23 18:57:56,996 INFO : FTrace - sched_load_avg_task\n", |
| "2016-09-23 18:57:56,997 INFO : FTrace - sched_tune_config\n", |
| "2016-09-23 18:57:56,997 INFO : FTrace - sched_tune_filter\n", |
| "2016-09-23 18:57:56,997 INFO : FTrace - sched_tune_tasks_update\n", |
| "2016-09-23 18:57:56,998 INFO : FTrace - sched_tune_boostgroup_update\n", |
| "2016-09-23 18:57:56,998 INFO : FTrace - sched_boost_cpu\n", |
| "2016-09-23 18:57:56,999 INFO : FTrace - sched_boost_task\n", |
| "2016-09-23 18:57:56,999 INFO : FTrace - sched_energy_diff\n", |
| "2016-09-23 18:57:56,999 INFO : FTrace - cpu_capacity\n", |
| "2016-09-23 18:57:57,000 INFO : FTrace - cpu_frequency\n", |
| "2016-09-23 18:57:57,000 INFO : FTrace - cpu_idle\n", |
| "2016-09-23 18:57:57,000 INFO : FTrace - walt_update_task_ravg\n", |
| "2016-09-23 18:57:57,001 INFO : FTrace - walt_update_history\n", |
| "2016-09-23 18:57:57,001 INFO : FTrace - walt_migration_update_sum\n", |
| "2016-09-23 18:57:57,002 WARNING : Target - Using configuration provided RTApp calibration\n", |
| "2016-09-23 18:57:57,002 INFO : Target - Using RT-App calibration values:\n", |
| "2016-09-23 18:57:57,002 INFO : Target - {\"0\": 254, \"1\": 252, \"2\": 252, \"3\": 251, \"4\": 251, \"5\": 252, \"6\": 251, \"7\": 251}\n", |
| "2016-09-23 18:57:57,003 WARNING : TestEnv - Wipe previous contents of the results folder:\n", |
| "2016-09-23 18:57:57,003 WARNING : TestEnv - /home/derkling/Code/lisa/results/ReleaseNotes_v16.09\n", |
| "2016-09-23 18:57:57,004 INFO : AEP - AEP configuration\n", |
| "2016-09-23 18:57:57,004 INFO : AEP - {'instrument': 'aep', 'channel_map': {'LITTLE': 'LITTLE'}, 'conf': {'resistor_values': [0.033], 'device_entry': '/dev/ttyACM0'}}\n", |
| "2016-09-23 18:57:57,011 INFO : AEP - Channels selected for energy sampling:\n", |
| "[CHAN(LITTLE_current), CHAN(LITTLE_power), CHAN(LITTLE_voltage)]\n", |
| "2016-09-23 18:57:57,012 INFO : TestEnv - Set results folder to:\n", |
| "2016-09-23 18:57:57,012 INFO : TestEnv - /home/derkling/Code/lisa/results/ReleaseNotes_v16.09\n", |
| "2016-09-23 18:57:57,013 INFO : TestEnv - Experiment results available also in:\n", |
| "2016-09-23 18:57:57,013 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n" |
| ] |
| } |
| ], |
| "source": [ |
| "from env import TestEnv\n", |
| "\n", |
| "te = TestEnv(my_conf, force_new=True)\n", |
| "target = te.target" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 25, |
| "metadata": { |
| "collapsed": false, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:57:58,066 INFO : WlGen - Setup new workload ramp\n", |
| "2016-09-23 18:57:58,067 INFO : RTApp - Workload duration defined by longest task\n", |
| "2016-09-23 18:57:58,067 INFO : RTApp - Default policy: SCHED_OTHER\n", |
| "2016-09-23 18:57:58,067 INFO : RTApp - ------------------------\n", |
| "2016-09-23 18:57:58,068 INFO : RTApp - task [ramp], sched: using default policy\n", |
| "2016-09-23 18:57:58,068 INFO : RTApp - | calibration CPU: 4\n", |
| "2016-09-23 18:57:58,068 INFO : RTApp - | loops count: 1\n", |
| "2016-09-23 18:57:58,068 INFO : RTApp - + phase_000001: sleep 1.000000 [s]\n", |
| "2016-09-23 18:57:58,068 INFO : RTApp - + phase_000002: duration 1.000000 [s] (10 loops)\n", |
| "2016-09-23 18:57:58,069 INFO : RTApp - | period 100000 [us], duty_cycle 10 %\n", |
| "2016-09-23 18:57:58,069 INFO : RTApp - | run_time 10000 [us], sleep_time 90000 [us]\n", |
| "2016-09-23 18:57:58,069 INFO : RTApp - + phase_000003: duration 1.000000 [s] (10 loops)\n", |
| "2016-09-23 18:57:58,069 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n", |
| "2016-09-23 18:57:58,070 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n", |
| "2016-09-23 18:57:58,070 INFO : RTApp - + phase_000004: duration 1.000000 [s] (10 loops)\n", |
| "2016-09-23 18:57:58,070 INFO : RTApp - | period 100000 [us], duty_cycle 30 %\n", |
| "2016-09-23 18:57:58,070 INFO : RTApp - | run_time 30000 [us], sleep_time 70000 [us]\n", |
| "2016-09-23 18:57:58,070 INFO : RTApp - + phase_000005: duration 1.000000 [s] (10 loops)\n", |
| "2016-09-23 18:57:58,071 INFO : RTApp - | period 100000 [us], duty_cycle 40 %\n", |
| "2016-09-23 18:57:58,071 INFO : RTApp - | run_time 40000 [us], sleep_time 60000 [us]\n", |
| "2016-09-23 18:57:58,071 INFO : RTApp - + phase_000006: duration 1.000000 [s] (10 loops)\n", |
| "2016-09-23 18:57:58,071 INFO : RTApp - | period 100000 [us], duty_cycle 50 %\n", |
| "2016-09-23 18:57:58,072 INFO : RTApp - | run_time 50000 [us], sleep_time 50000 [us]\n", |
| "2016-09-23 18:57:58,072 INFO : RTApp - + phase_000007: duration 1.000000 [s] (10 loops)\n", |
| "2016-09-23 18:57:58,072 INFO : RTApp - | period 100000 [us], duty_cycle 60 %\n", |
| "2016-09-23 18:57:58,072 INFO : RTApp - | run_time 60000 [us], sleep_time 40000 [us]\n", |
| "2016-09-23 18:57:58,072 INFO : RTApp - + phase_000008: duration 1.000000 [s] (10 loops)\n", |
| "2016-09-23 18:57:58,073 INFO : RTApp - | period 100000 [us], duty_cycle 70 %\n", |
| "2016-09-23 18:57:58,073 INFO : RTApp - | run_time 70000 [us], sleep_time 30000 [us]\n", |
| "2016-09-23 18:57:58,073 INFO : RTApp - + phase_000009: duration 1.000000 [s] (10 loops)\n", |
| "2016-09-23 18:57:58,073 INFO : RTApp - | period 100000 [us], duty_cycle 80 %\n", |
| "2016-09-23 18:57:58,073 INFO : RTApp - | run_time 80000 [us], sleep_time 20000 [us]\n", |
| "2016-09-23 18:57:58,074 INFO : RTApp - + phase_000010: duration 1.000000 [s] (10 loops)\n", |
| "2016-09-23 18:57:58,074 INFO : RTApp - | period 100000 [us], duty_cycle 90 %\n", |
| "2016-09-23 18:57:58,074 INFO : RTApp - | run_time 90000 [us], sleep_time 10000 [us]\n", |
| "2016-09-23 18:57:58,074 INFO : RTApp - + phase_000011: batch 1.000000 [s]\n" |
| ] |
| } |
| ], |
| "source": [ |
| "from wlgen import RTA,Ramp\n", |
| "\n", |
| "# Let's run a simple RAMP task\n", |
| "rta = RTA(target, 'ramp')\n", |
| "rta.conf(\n", |
| " kind='profile',\n", |
| " params = {\n", |
| " 'ramp' : Ramp().get()\n", |
| " }\n", |
| ");" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 26, |
| "metadata": { |
| "collapsed": false, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:58:00,939 INFO : WlGen - Workload execution START:\n", |
| "2016-09-23 18:58:00,939 INFO : WlGen - /data/local/tmp/bin/rt-app /data/local/tmp/devlib-target/ramp_00.json 2>&1\n" |
| ] |
| } |
| ], |
| "source": [ |
| "te.ftrace.start()\n", |
| "\n", |
| "target.execute(\"echo 'my_marker: label=START' > /sys/kernel/debug/tracing/trace_marker\",\n", |
| " as_root=True)\n", |
| "rta.run(out_dir=te.res_dir)\n", |
| "target.execute(\"echo 'my_marker: label=STOP' > /sys/kernel/debug/tracing/trace_marker\",\n", |
| " as_root=True)\n", |
| "\n", |
| "te.ftrace.stop()\n", |
| "\n", |
| "trace_file = os.path.join(te.res_dir, 'trace.dat')\n", |
| "te.ftrace.get_trace(trace_file)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## DataFrame namespace" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 27, |
| "metadata": { |
| "collapsed": false, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:58:17,976 INFO : Parsing FTrace format...\n", |
| "2016-09-23 18:58:20,633 WARNING : Cluster Frequency is not coherent! Failure in [cpu_frequency] events at:\n", |
| "2016-09-23 18:58:20,634 WARNING : __comm __cpu __pid cpu frequency\n", |
| "Time \n", |
| "10.619162 shutils 1 10380 4 1200000\n", |
| "10.619241 shutils 1 10380 5 1200000\n", |
| "10.619319 shutils 1 10380 6 1200000\n", |
| "10.619397 shutils 1 10380 7 960000\n", |
| "2016-09-23 18:58:20,638 INFO : Collected events spans a 10.725 [s] time interval\n", |
| "2016-09-23 18:58:20,639 INFO : Overutilized time: 5.322431 [s] (49.626% of trace time)\n", |
| "2016-09-23 18:58:20,639 INFO : Set plots time range to (0.000000, 10.725080)[s]\n", |
| "2016-09-23 18:58:20,639 INFO : Registering trace analysis modules:\n", |
| "2016-09-23 18:58:20,643 INFO : perf\n", |
| "2016-09-23 18:58:20,644 INFO : latency\n", |
| "2016-09-23 18:58:20,644 INFO : eas\n", |
| "2016-09-23 18:58:20,645 INFO : tasks\n", |
| "2016-09-23 18:58:20,645 INFO : cpus\n", |
| "2016-09-23 18:58:20,646 INFO : functions\n", |
| "2016-09-23 18:58:20,646 INFO : status\n", |
| "2016-09-23 18:58:20,646 INFO : stune\n", |
| "2016-09-23 18:58:20,647 INFO : frequency\n" |
| ] |
| }, |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Maximum estimated system energy: 5584\n" |
| ] |
| } |
| ], |
| "source": [ |
| "from trace import Trace\n", |
| "\n", |
| "events_to_parse = my_conf['ftrace']['events']\n", |
| "events_to_parse += ['my_marker'] \n", |
| "\n", |
| "trace = Trace(te.platform, trace_file, events=events_to_parse)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 28, |
| "metadata": { |
| "collapsed": false, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/plain": [ |
| "['sched_load_avg_task',\n", |
| " 'cpu_capacity',\n", |
| " 'cpu_idle',\n", |
| " 'sched_wakeup_new',\n", |
| " 'sched_contrib_scale_f',\n", |
| " 'cpu_frequency',\n", |
| " 'sched_boost_cpu',\n", |
| " 'sched_boost_task',\n", |
| " 'sched_switch',\n", |
| " 'sched_energy_diff',\n", |
| " 'sched_stat_wait',\n", |
| " 'sched_load_avg_cpu',\n", |
| " 'my_marker',\n", |
| " 'sched_overutilized',\n", |
| " 'sched_wakeup']" |
| ] |
| }, |
| "execution_count": 28, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "trace.available_events" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [], |
| "source": [ |
| "# Use TAB to complete\n", |
| "trace.data_frame." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 30, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/html": [ |
| "<div>\n", |
| "<table border=\"1\" class=\"dataframe\">\n", |
| " <thead>\n", |
| " <tr style=\"text-align: right;\">\n", |
| " <th></th>\n", |
| " <th>prio</th>\n", |
| " <th>comm</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>pid</th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " </tr>\n", |
| " </thead>\n", |
| " <tbody>\n", |
| " <tr>\n", |
| " <th>10</th>\n", |
| " <td>0</td>\n", |
| " <td>migration/0</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>11</th>\n", |
| " <td>0</td>\n", |
| " <td>watchdog/0</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>12</th>\n", |
| " <td>0</td>\n", |
| " <td>watchdog/1</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>13</th>\n", |
| " <td>0</td>\n", |
| " <td>migration/1</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>17</th>\n", |
| " <td>0</td>\n", |
| " <td>watchdog/2</td>\n", |
| " </tr>\n", |
| " </tbody>\n", |
| "</table>\n", |
| "</div>" |
| ], |
| "text/plain": [ |
| " prio comm\n", |
| "pid \n", |
| "10 0 migration/0\n", |
| "11 0 watchdog/0\n", |
| "12 0 watchdog/1\n", |
| "13 0 migration/1\n", |
| "17 0 watchdog/2" |
| ] |
| }, |
| "execution_count": 30, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "rt_tasks = trace.data_frame.rt_tasks()\n", |
| "rt_tasks.head()" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 31, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/html": [ |
| "<div>\n", |
| "<table border=\"1\" class=\"dataframe\">\n", |
| " <thead>\n", |
| " <tr style=\"text-align: right;\">\n", |
| " <th></th>\n", |
| " <th>target_cpu</th>\n", |
| " <th>__cpu</th>\n", |
| " <th>curr_state</th>\n", |
| " <th>next_state</th>\n", |
| " <th>t_start</th>\n", |
| " <th>t_delta</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>Time</th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " </tr>\n", |
| " </thead>\n", |
| " <tbody>\n", |
| " <tr>\n", |
| " <th>0.326839</th>\n", |
| " <td>NaN</td>\n", |
| " <td>1</td>\n", |
| " <td>A</td>\n", |
| " <td>S</td>\n", |
| " <td>0.326839</td>\n", |
| " <td>0.000204</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>0.327043</th>\n", |
| " <td>NaN</td>\n", |
| " <td>1</td>\n", |
| " <td>S</td>\n", |
| " <td>W</td>\n", |
| " <td>0.327043</td>\n", |
| " <td>1.000443</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>1.327486</th>\n", |
| " <td>1</td>\n", |
| " <td>NaN</td>\n", |
| " <td>W</td>\n", |
| " <td>A</td>\n", |
| " <td>1.327486</td>\n", |
| " <td>0.000068</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>1.327554</th>\n", |
| " <td>NaN</td>\n", |
| " <td>1</td>\n", |
| " <td>A</td>\n", |
| " <td>R</td>\n", |
| " <td>1.327554</td>\n", |
| " <td>0.000776</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>1.328330</th>\n", |
| " <td>NaN</td>\n", |
| " <td>1</td>\n", |
| " <td>R</td>\n", |
| " <td>A</td>\n", |
| " <td>1.328330</td>\n", |
| " <td>0.000154</td>\n", |
| " </tr>\n", |
| " </tbody>\n", |
| "</table>\n", |
| "</div>" |
| ], |
| "text/plain": [ |
| " target_cpu __cpu curr_state next_state t_start t_delta\n", |
| "Time \n", |
| "0.326839 NaN 1 A S 0.326839 0.000204\n", |
| "0.327043 NaN 1 S W 0.327043 1.000443\n", |
| "1.327486 1 NaN W A 1.327486 0.000068\n", |
| "1.327554 NaN 1 A R 1.327554 0.000776\n", |
| "1.328330 NaN 1 R A 1.328330 0.000154" |
| ] |
| }, |
| "execution_count": 31, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "lat_df = trace.data_frame.latency_df('ramp')\n", |
| "lat_df.head()" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 32, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/html": [ |
| "<div>\n", |
| "<table border=\"1\" class=\"dataframe\">\n", |
| " <thead>\n", |
| " <tr style=\"text-align: right;\">\n", |
| " <th></th>\n", |
| " <th>__comm</th>\n", |
| " <th>__cpu</th>\n", |
| " <th>__pid</th>\n", |
| " <th>label</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>Time</th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " </tr>\n", |
| " </thead>\n", |
| " <tbody>\n", |
| " <tr>\n", |
| " <th>0.155194</th>\n", |
| " <td>sh</td>\n", |
| " <td>2</td>\n", |
| " <td>10362</td>\n", |
| " <td>START</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>10.546818</th>\n", |
| " <td>sh</td>\n", |
| " <td>1</td>\n", |
| " <td>10375</td>\n", |
| " <td>STOP</td>\n", |
| " </tr>\n", |
| " </tbody>\n", |
| "</table>\n", |
| "</div>" |
| ], |
| "text/plain": [ |
| " __comm __cpu __pid label\n", |
| "Time \n", |
| "0.155194 sh 2 10362 START\n", |
| "10.546818 sh 1 10375 STOP" |
| ] |
| }, |
| "execution_count": 32, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "custom_df = trace.data_frame.trace_event('my_marker')\n", |
| "custom_df" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 33, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/html": [ |
| "<div>\n", |
| "<table border=\"1\" class=\"dataframe\">\n", |
| " <thead>\n", |
| " <tr style=\"text-align: right;\">\n", |
| " <th></th>\n", |
| " <th>__comm</th>\n", |
| " <th>__cpu</th>\n", |
| " <th>__pid</th>\n", |
| " <th>next_comm</th>\n", |
| " <th>next_pid</th>\n", |
| " <th>next_prio</th>\n", |
| " <th>prev_comm</th>\n", |
| " <th>prev_pid</th>\n", |
| " <th>prev_prio</th>\n", |
| " <th>prev_state</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>Time</th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " <th></th>\n", |
| " </tr>\n", |
| " </thead>\n", |
| " <tbody>\n", |
| " <tr>\n", |
| " <th>0.000045</th>\n", |
| " <td><idle></td>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>shell srvc 1034</td>\n", |
| " <td>10345</td>\n", |
| " <td>120</td>\n", |
| " <td>swapper/1</td>\n", |
| " <td>0</td>\n", |
| " <td>120</td>\n", |
| " <td>0</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>0.000166</th>\n", |
| " <td><idle></td>\n", |
| " <td>2</td>\n", |
| " <td>0</td>\n", |
| " <td>adbd</td>\n", |
| " <td>1843</td>\n", |
| " <td>120</td>\n", |
| " <td>swapper/2</td>\n", |
| " <td>0</td>\n", |
| " <td>120</td>\n", |
| " <td>0</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>0.000200</th>\n", |
| " <td>shell</td>\n", |
| " <td>1</td>\n", |
| " <td>10345</td>\n", |
| " <td>swapper/1</td>\n", |
| " <td>0</td>\n", |
| " <td>120</td>\n", |
| " <td>shell srvc 1034</td>\n", |
| " <td>10345</td>\n", |
| " <td>120</td>\n", |
| " <td>1</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>0.000256</th>\n", |
| " <td><idle></td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>rcu_preempt</td>\n", |
| " <td>7</td>\n", |
| " <td>120</td>\n", |
| " <td>swapper/0</td>\n", |
| " <td>0</td>\n", |
| " <td>120</td>\n", |
| " <td>0</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>0.000267</th>\n", |
| " <td><idle></td>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>rcu_sched</td>\n", |
| " <td>8</td>\n", |
| " <td>120</td>\n", |
| " <td>swapper/1</td>\n", |
| " <td>0</td>\n", |
| " <td>120</td>\n", |
| " <td>0</td>\n", |
| " </tr>\n", |
| " </tbody>\n", |
| "</table>\n", |
| "</div>" |
| ], |
| "text/plain": [ |
| " __comm __cpu __pid next_comm next_pid next_prio \\\n", |
| "Time \n", |
| "0.000045 <idle> 1 0 shell srvc 1034 10345 120 \n", |
| "0.000166 <idle> 2 0 adbd 1843 120 \n", |
| "0.000200 shell 1 10345 swapper/1 0 120 \n", |
| "0.000256 <idle> 0 0 rcu_preempt 7 120 \n", |
| "0.000267 <idle> 1 0 rcu_sched 8 120 \n", |
| "\n", |
| " prev_comm prev_pid prev_prio prev_state \n", |
| "Time \n", |
| "0.000045 swapper/1 0 120 0 \n", |
| "0.000166 swapper/2 0 120 0 \n", |
| "0.000200 shell srvc 1034 10345 120 1 \n", |
| "0.000256 swapper/0 0 120 0 \n", |
| "0.000267 swapper/1 0 120 0 " |
| ] |
| }, |
| "execution_count": 33, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "ctxsw_df = trace.data_frame.trace_event('sched_switch')\n", |
| "ctxsw_df.head()" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Analysis namespace" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "# Use TAB to complete\n", |
| "trace.analysis." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 34, |
| "metadata": { |
| "collapsed": false, |
| "run_control": { |
| "marked": false |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:58:32,103 INFO : Plotting 10366: ramp, rt-app...\n" |
| ] |
| }, |
| { |
| "data": { |
| "image/png": 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2v7chxR4SVEnHmDrnLvHZdJJP+RuBGxM8vxg4oMu1EwmAlj7zIkGjtilBpLUi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\na13ix+9eFA6P9sgMtC381jdt0ROZyH2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rIJKQ2qb0pL0mNfHWsxvZ/uE6vnhJDZu2tP+I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\nB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m9kCmOgVSvg9JObd2BKREQUDhd9tSRyJ7e1luo99FDg88WLnSkHOcNNdVNK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|
| "text/plain": [ |
| "<matplotlib.figure.Figure at 0x7f6199757490>" |
| ] |
| }, |
| "metadata": {}, |
| "output_type": "display_data" |
| } |
| ], |
| "source": [ |
| "trace.analysis.tasks.plotTasks(tasks='ramp',\n", |
| " signals=['util_avg', 'boosted_util',\n", |
| " 'sched_overutilized', 'residencies'])" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 35, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:58:32,990 INFO : Found: 80 WAKEUP latencies\n", |
| "2016-09-23 18:58:33,019 INFO : Found: 371 PREEMPT latencies\n", |
| "2016-09-23 18:58:33,021 INFO : Total: 451 latency events\n" |
| ] |
| }, |
| { |
| "data": { |
| "image/png": 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NzJmjzOTJcUcFAJHRyi4AAAAAIBYkpAAAAGlGLSEAKUZCCgAAkFIdPT0kpABSjYQUAAAg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\nf6h/DM0+DilpbNC3R8QqAElnAb+LiE81NiwzM7Ohk5sMqZmZNaee3nS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|
| "text/plain": [ |
| "<matplotlib.figure.Figure at 0x7f6193aa9c90>" |
| ] |
| }, |
| "metadata": {}, |
| "output_type": "display_data" |
| } |
| ], |
| "source": [ |
| "lat_data = trace.analysis.latency.plotLatency('ramp')" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 36, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/html": [ |
| "<div>\n", |
| "<table border=\"1\" class=\"dataframe\">\n", |
| " <thead>\n", |
| " <tr style=\"text-align: right;\">\n", |
| " <th></th>\n", |
| " <th>count</th>\n", |
| " <th>mean</th>\n", |
| " <th>std</th>\n", |
| " <th>min</th>\n", |
| " <th>50%</th>\n", |
| " <th>95%</th>\n", |
| " <th>99%</th>\n", |
| " <th>max</th>\n", |
| " </tr>\n", |
| " </thead>\n", |
| " <tbody>\n", |
| " <tr>\n", |
| " <th>latency</th>\n", |
| " <td>451</td>\n", |
| " <td>0.00012</td>\n", |
| " <td>0.000347</td>\n", |
| " <td>0.000019</td>\n", |
| " <td>0.000031</td>\n", |
| " <td>0.000539</td>\n", |
| " <td>0.002132</td>\n", |
| " <td>0.003023</td>\n", |
| " </tr>\n", |
| " </tbody>\n", |
| "</table>\n", |
| "</div>" |
| ], |
| "text/plain": [ |
| " count mean std min 50% 95% 99% \\\n", |
| "latency 451 0.00012 0.000347 0.000019 0.000031 0.000539 0.002132 \n", |
| "\n", |
| " max \n", |
| "latency 0.003023 " |
| ] |
| }, |
| "execution_count": 36, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "lat_data.T" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 37, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:58:34,935 INFO : LITTLE cluster average frequency: 0.916 GHz\n", |
| "2016-09-23 18:58:34,935 INFO : big cluster average frequency: 0.915 GHz\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/plain": [ |
| "(0.91643081506625346, 0.91532341938266315)" |
| ] |
| }, |
| "execution_count": 37, |
| "metadata": {}, |
| "output_type": "execute_result" |
| }, |
| { |
| "data": { |
| "image/png": 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hP31tX/m8qI+NzY6LJVh+vV6nVqu1j7t27btpt9D+9Ll9ffv2ou/zfU9qs/wZ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\ngMUpi28//q67n+Pu/87d3x399w13f8ndr3H3vLtf6+7Hap5zr7tf7O7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|
| "text/plain": [ |
| "<matplotlib.figure.Figure at 0x7f6193ec8dd0>" |
| ] |
| }, |
| "metadata": {}, |
| "output_type": "display_data" |
| } |
| ], |
| "source": [ |
| "trace.analysis.frequency.plotClusterFrequencies()" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 38, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:58:35,119 WARNING : Cluster frequency is not coherent, plot DISABLED!\n" |
| ] |
| } |
| ], |
| "source": [ |
| "trace.analysis.frequency.plotClusterFrequencyResidency(pct=True, active=True)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 39, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2016-09-23 18:58:35,289 WARNING : Cluster frequency is not coherent, plot DISABLED!\n" |
| ] |
| } |
| ], |
| "source": [ |
| "trace.analysis.frequency.plotClusterFrequencyResidency(pct=True, active=False)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# Notebooks" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## New collection of examples" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Each new API introduced in LISA has an associated notebook which shows a\n", |
| "complete example of its usage.<br>\n", |
| "Examples are usually defined to:\n", |
| "\n", |
| " - setup the connection to a target (usually a JUNO board)\n", |
| " - configure a workload (usually using RTApp)\n", |
| " - run workload and collect required measures\n", |
| " - show the most common functions exposed by the new API" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "- Energy meters APIs:<br>\n", |
| " https://github.com/ARM-software/lisa/tree/master/ipynb/examples/energy_meter" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "- Trace analysis APIs:<br>\n", |
| " https://github.com/ARM-software/lisa/tree/master/ipynb/examples/trace_analysis" |
| ] |
| } |
| ], |
| "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 |
| } |
