path: root/ipynb/examples/trace_analysis/TraceAnalysis_IdleStates.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Trace Analysis Examples\n",
    "\n",
    "## Idle States Residency Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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\n",
    " \n",
    "Details on idle states profiling ar given in **Per-CPU/Per-Cluster Idle State Residency Profiling** below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-01-20 11:54:37,100 INFO    : root         : Using LISA logging configuration:\n",
      "2017-01-20 11:54:37,100 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,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "\n",
    "# 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\n",
    "\n",
    "# DataFrame support\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "\n",
    "# Trappy (plots) support\n",
    "from trappy import ILinePlot\n",
    "from trappy.stats.grammar import Parser"
   ]
  },
  {
   "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**.\n",
    "\n",
    "Our target is a Juno R0 development board running Linux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# Setup a target configuration\n",
    "my_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",
    "    \"results_dir\" : \"IdleAnalysis\",\n",
    "    \n",
    "    # RTApp calibration values (comment to let LISA do a calibration run)\n",
    "    #\"rtapp-calib\" :  {\n",
    "    #    \"0\": 318, \"1\": 125, \"2\": 124, \"3\": 318, \"4\": 318, \"5\": 319\n",
    "    #},\n",
    "    \n",
    "    # Tools required by the experiments\n",
    "    \"tools\"   : ['rt-app', 'trace-cmd'],\n",
    "    \"modules\" : ['bl', 'cpufreq', 'cpuidle'],\n",
    "    \"exclude_modules\" : ['hwmon'],\n",
    "    \n",
    "    # FTrace events to collect for all the tests configuration which have\n",
    "    # the \"ftrace\" flag enabled\n",
    "    \"ftrace\"  : {\n",
    "         \"events\" : [\n",
    "            \"cpu_idle\",\n",
    "            \"sched_switch\"\n",
    "         ],\n",
    "         \"buffsize\" : 10 * 1024,\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-01-20 11:54:39,398 INFO    : TestEnv      : Using base path: /home/vagrant/lisa\n",
      "2017-01-20 11:54:39,399 INFO    : TestEnv      : Loading custom (inline) target configuration\n",
      "2017-01-20 11:54:39,399 INFO    : TestEnv      : Devlib modules to load: ['bl', 'cpuidle', 'cpufreq']\n",
      "2017-01-20 11:54:39,400 INFO    : TestEnv      : Connecting linux target:\n",
      "2017-01-20 11:54:39,400 INFO    : TestEnv      :   username : root\n",
      "2017-01-20 11:54:39,401 INFO    : TestEnv      :       host : 192.168.0.1\n",
      "2017-01-20 11:54:39,401 INFO    : TestEnv      :   password : juno\n",
      "2017-01-20 11:54:39,401 INFO    : TestEnv      : Connection settings:\n",
      "2017-01-20 11:54:39,402 INFO    : TestEnv      :    {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n",
      "2017-01-20 11:54:46,668 INFO    : TestEnv      : Initializing target workdir:\n",
      "2017-01-20 11:54:46,669 INFO    : TestEnv      :    /root/devlib-target\n",
      "2017-01-20 11:55:02,855 INFO    : TestEnv      : Topology:\n",
      "2017-01-20 11:55:02,856 INFO    : TestEnv      :    [[0, 3, 4, 5], [1, 2]]\n",
      "2017-01-20 11:55:04,096 INFO    : TestEnv      : Loading default EM:\n",
      "2017-01-20 11:55:04,096 INFO    : TestEnv      :    /home/vagrant/lisa/libs/utils/platforms/juno.json\n",
      "2017-01-20 11:55:07,198 INFO    : TestEnv      : Enabled tracepoints:\n",
      "2017-01-20 11:55:07,198 INFO    : TestEnv      :    cpu_idle\n",
      "2017-01-20 11:55:07,199 INFO    : TestEnv      :    sched_switch\n",
      "2017-01-20 11:55:07,199 WARNING : TestEnv      : Using configuration provided RTApp calibration\n",
      "2017-01-20 11:55:07,200 INFO    : TestEnv      : Using RT-App calibration values:\n",
      "2017-01-20 11:55:07,200 INFO    : TestEnv      :    {\"0\": 318, \"1\": 125, \"2\": 124, \"3\": 318, \"4\": 318, \"5\": 319}\n",
      "2017-01-20 11:55:07,201 INFO    : EnergyMeter  : HWMON module not enabled\n",
      "2017-01-20 11:55:07,201 WARNING : EnergyMeter  : Energy sampling disabled by configuration\n",
      "2017-01-20 11:55:07,201 INFO    : TestEnv      : Set results folder to:\n",
      "2017-01-20 11:55:07,202 INFO    : TestEnv      :    /home/vagrant/lisa/results/IdleAnalysis\n",
      "2017-01-20 11:55:07,202 INFO    : TestEnv      : Experiment results available also in:\n",
      "2017-01-20 11:55:07,203 INFO    : TestEnv      :    /home/vagrant/lisa/results_latest\n"
     ]
    }
   ],
   "source": [
    "# Initialize a test environment\n",
    "te = TestEnv(my_conf, wipe=False, force_new=True)\n",
    "target = te.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We're going to run quite a heavy workload to try and create short idle periods.\n",
    "# Let's set the CPU frequency to max to make sure those idle periods exist\n",
    "# (otherwise at a lower frequency the workload might overload the CPU\n",
    "#  so it never went idle at all)\n",
    "te.target.cpufreq.set_all_governors('performance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Workload configuration and execution\n",
    "\n",
    "Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This experiment:\n",
    "- Runs a periodic RT-App workload, pinned to CPU 1, that ramps down from 80% to 10% over 7.5 seconds\n",
    "- Uses `perturb_cpus` to ensure 'cpu_idle' events are present in the trace for all CPUs\n",
    "- Triggers and collects ftrace output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cpu = 1\n",
    "def experiment(te):\n",
    "\n",
    "    # Create RTApp RAMP task\n",
    "    rtapp = RTA(te.target, 'ramp', calibration=te.calibration())\n",
    "    rtapp.conf(kind='profile',\n",
    "               params={\n",
    "                    'ramp' : Ramp(\n",
    "                        start_pct =  80,\n",
    "                        end_pct   =  10,\n",
    "                        delta_pct =  5,\n",
    "                        time_s    =  0.5,\n",
    "                        period_ms =  5,\n",
    "                        cpus =       [cpu]).get()\n",
    "              })\n",
    "\n",
    "    # FTrace the execution of this workload\n",
    "    te.ftrace.start()\n",
    "    # Momentarily wake all CPUs to ensure cpu_idle trace events are present from the beginning\n",
    "    te.target.cpuidle.perturb_cpus()\n",
    "    rtapp.run(out_dir=te.res_dir)\n",
    "    te.ftrace.stop()\n",
    "\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",
    "    # Dump platform descriptor\n",
    "    te.platform_dump(te.res_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-01-20 11:55:44,654 INFO    : Workload     : Setup new workload ramp\n",
      "2017-01-20 11:55:44,655 INFO    : Workload     : Workload duration defined by longest task\n",
      "2017-01-20 11:55:44,655 INFO    : Workload     : Default policy: SCHED_OTHER\n",
      "2017-01-20 11:55:44,656 INFO    : Workload     : ------------------------\n",
      "2017-01-20 11:55:44,656 INFO    : Workload     : task [ramp], sched: using default policy\n",
      "2017-01-20 11:55:44,656 INFO    : Workload     :  | calibration CPU: 1\n",
      "2017-01-20 11:55:44,657 INFO    : Workload     :  | loops count: 1\n",
      "2017-01-20 11:55:44,657 INFO    : Workload     :  | CPUs affinity: [1]\n",
      "2017-01-20 11:55:44,658 INFO    : Workload     : + phase_000001: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,658 INFO    : Workload     : |  period     5000 [us], duty_cycle  80 %\n",
      "2017-01-20 11:55:44,658 INFO    : Workload     : |  run_time   4000 [us], sleep_time   1000 [us]\n",
      "2017-01-20 11:55:44,659 INFO    : Workload     : + phase_000002: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,659 INFO    : Workload     : |  period     5000 [us], duty_cycle  75 %\n",
      "2017-01-20 11:55:44,659 INFO    : Workload     : |  run_time   3750 [us], sleep_time   1250 [us]\n",
      "2017-01-20 11:55:44,660 INFO    : Workload     : + phase_000003: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,660 INFO    : Workload     : |  period     5000 [us], duty_cycle  70 %\n",
      "2017-01-20 11:55:44,660 INFO    : Workload     : |  run_time   3500 [us], sleep_time   1500 [us]\n",
      "2017-01-20 11:55:44,661 INFO    : Workload     : + phase_000004: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,661 INFO    : Workload     : |  period     5000 [us], duty_cycle  65 %\n",
      "2017-01-20 11:55:44,662 INFO    : Workload     : |  run_time   3250 [us], sleep_time   1750 [us]\n",
      "2017-01-20 11:55:44,662 INFO    : Workload     : + phase_000005: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,662 INFO    : Workload     : |  period     5000 [us], duty_cycle  60 %\n",
      "2017-01-20 11:55:44,663 INFO    : Workload     : |  run_time   3000 [us], sleep_time   2000 [us]\n",
      "2017-01-20 11:55:44,663 INFO    : Workload     : + phase_000006: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,664 INFO    : Workload     : |  period     5000 [us], duty_cycle  55 %\n",
      "2017-01-20 11:55:44,665 INFO    : Workload     : |  run_time   2750 [us], sleep_time   2250 [us]\n",
      "2017-01-20 11:55:44,665 INFO    : Workload     : + phase_000007: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,666 INFO    : Workload     : |  period     5000 [us], duty_cycle  50 %\n",
      "2017-01-20 11:55:44,666 INFO    : Workload     : |  run_time   2500 [us], sleep_time   2500 [us]\n",
      "2017-01-20 11:55:44,667 INFO    : Workload     : + phase_000008: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,667 INFO    : Workload     : |  period     5000 [us], duty_cycle  45 %\n",
      "2017-01-20 11:55:44,668 INFO    : Workload     : |  run_time   2250 [us], sleep_time   2750 [us]\n",
      "2017-01-20 11:55:44,669 INFO    : Workload     : + phase_000009: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,669 INFO    : Workload     : |  period     5000 [us], duty_cycle  40 %\n",
      "2017-01-20 11:55:44,670 INFO    : Workload     : |  run_time   2000 [us], sleep_time   3000 [us]\n",
      "2017-01-20 11:55:44,670 INFO    : Workload     : + phase_000010: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,671 INFO    : Workload     : |  period     5000 [us], duty_cycle  35 %\n",
      "2017-01-20 11:55:44,671 INFO    : Workload     : |  run_time   1750 [us], sleep_time   3250 [us]\n",
      "2017-01-20 11:55:44,672 INFO    : Workload     : + phase_000011: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,672 INFO    : Workload     : |  period     5000 [us], duty_cycle  30 %\n",
      "2017-01-20 11:55:44,673 INFO    : Workload     : |  run_time   1500 [us], sleep_time   3500 [us]\n",
      "2017-01-20 11:55:44,673 INFO    : Workload     : + phase_000012: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,674 INFO    : Workload     : |  period     5000 [us], duty_cycle  25 %\n",
      "2017-01-20 11:55:44,674 INFO    : Workload     : |  run_time   1250 [us], sleep_time   3750 [us]\n",
      "2017-01-20 11:55:44,675 INFO    : Workload     : + phase_000013: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,675 INFO    : Workload     : |  period     5000 [us], duty_cycle  20 %\n",
      "2017-01-20 11:55:44,676 INFO    : Workload     : |  run_time   1000 [us], sleep_time   4000 [us]\n",
      "2017-01-20 11:55:44,676 INFO    : Workload     : + phase_000014: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,677 INFO    : Workload     : |  period     5000 [us], duty_cycle  15 %\n",
      "2017-01-20 11:55:44,677 INFO    : Workload     : |  run_time    750 [us], sleep_time   4250 [us]\n",
      "2017-01-20 11:55:44,678 INFO    : Workload     : + phase_000015: duration 0.500000 [s] (100 loops)\n",
      "2017-01-20 11:55:44,678 INFO    : Workload     : |  period     5000 [us], duty_cycle  10 %\n",
      "2017-01-20 11:55:44,679 INFO    : Workload     : |  run_time    500 [us], sleep_time   4500 [us]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-01-20 11:55:52,296 INFO    : Workload     : Workload execution START:\n",
      "2017-01-20 11:55:52,297 INFO    : Workload     :    /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "experiment(te)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse trace and analyse data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-01-20 11:56:11,164 INFO    : root         : Content of the output folder /home/vagrant/lisa/results/IdleAnalysis\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/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",
      "├── rt-app-ramp-0.log\r\n",
      "├── trace.dat\r\n",
      "├── trace.raw.txt\r\n",
      "└── trace.txt\r\n",
      "\r\n",
      "0 directories, 9 files\r\n"
     ]
    }
   ],
   "source": [
    "# Base folder where tests folder are located\n",
    "res_dir = te.res_dir\n",
    "logging.info('Content of the output folder %s', res_dir)\n",
    "!tree {res_dir}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "trace = Trace(te.platform, res_dir, events=my_conf['ftrace']['events'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Per-CPU Idle State Residency Profiling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to get the residency in each idle state of a CPU or a cluster with the following commands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>idle_state</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.042726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.178961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.367166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 time\n",
       "idle_state           \n",
       "0            0.042726\n",
       "1            6.178961\n",
       "2           10.367166"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Idle state residency for CPU 3\n",
    "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": 11,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": 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>name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CpuidleState(WFI, ARM WFI)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CpuidleState(cpu-sleep-0, cpu-sleep-0)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CpuidleState(cluster-sleep-0, cluster-sleep-0)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             name  value\n",
       "0                      CpuidleState(WFI, ARM WFI)      0\n",
       "1          CpuidleState(cpu-sleep-0, cpu-sleep-0)      1\n",
       "2  CpuidleState(cluster-sleep-0, cluster-sleep-0)      2"
      ]
     },
     "execution_count": 11,
     "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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ia = trace.analysis.idle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9576bca10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Actual time spent in each idle state\n",
    "ia.plotCPUIdleStateResidency([1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd956afebd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Percentage of time spent in each idle state\n",
    "ia.plotCPUIdleStateResidency([1,2], pct=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CPU idle state over time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the target's idle states:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[CpuidleState(WFI, ARM WFI),\n",
       " CpuidleState(cpu-sleep-0, cpu-sleep-0),\n",
       " CpuidleState(cluster-sleep-0, cluster-sleep-0)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te.target.cpuidle.get_states()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use trappy to plot the idle state of a single CPU over time. Higher is deeper: the plot is at -1 when the CPU is active, 0 for WFI, 1 for CPU sleep, etc.\n",
    "\n",
    "We should see that as the workload ramps down and the idle periods become longer, the idle states used become deeper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border-style: hidden;\">\n",
       "<tr>\n",
       "<td style=\"border-style: hidden;\"><div class=\"ilineplot\" id=\"fig_efaaafbc817f4fe7bf53a26046d33295\"></div></td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style=\"border-style: hidden;\"><div style=\"text-align:center\" id=\"fig_efaaafbc817f4fe7bf53a26046d33295_legend\"></div></td>\n",
       "</tr>\n",
       "</table>\n",
       "<script>\n",
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = Parser(trace.ftrace, filters = {'cpu_id': cpu})\n",
    "idle_df = p.solve('cpu_idle:state')\n",
    "ILinePlot(idle_df, column=cpu, drawstyle='steps-post').view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine idle period lengths\n",
    "Let's get a DataFrame showing the length of each idle period on the CPU and the index of the cpuidle state that was entered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def get_idle_periods(df):\n",
    "    series = df[cpu]\n",
    "    series = series[series.shift() != series].dropna()\n",
    "    if series.iloc[0] == -1:\n",
    "        series = series.iloc[1:]\n",
    "\n",
    "    idles = series.iloc[0::2] \n",
    "    wakeups = series.iloc[1::2]\n",
    "    if len(idles) > len(wakeups):\n",
    "        idles = idles.iloc[:-1]\n",
    "    else:\n",
    "        wakeups = wakeups.iloc[:-1]\n",
    "\n",
    "    lengths = pd.Series((wakeups.index - idles.index), index=idles.index)\n",
    "    return pd.DataFrame({\"length\": lengths, \"state\": idles})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a scatter plot of the length of idle periods against the state that was entered. We should see that for long idle periods, deeper states were entered (i.e. we should see a positive corellation between the X and Y axes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7fd95675a550>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == 'face':\n"
     ]
    },
    {
     "data": {
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Ilwd2AYcPYJ1aHQ+D7Yv51OaYOJ14xeRLxPP4rmFgn6nWxkUx/TCf2hsTpwD/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\nkiRJkiRJkiRJkiRJkjRc/j8gnhxAS231CwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd95675a590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lengths = get_idle_periods(idle_df)\n",
    "lengths.plot(kind='scatter', x='length', y='state')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw a histogram of the length of idle periods shorter than 100ms in which the CPU entered cpuidle state 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes.AxesSubplot object at 0x7fd95656f710>]], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3EfBi4N4K11GswcHBpTfqI/aUK1IL2FOySC0Qrye3Ko8uuY308MgrgCc4+zyL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\nSZIkSZIkSZIkSdJ57P8D5iPynI5rOBwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd95c146910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = lengths[(lengths['state'] == 2) & (lengths['length'] < 0.010)]\n",
    "df.hist(column='length', bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Per-cluster Idle State Residency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>idle_state</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.527443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.709098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.609212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                time\n",
       "idle_state          \n",
       "0           1.527443\n",
       "1           2.709098\n",
       "2           8.609212"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Idle state residency for CPUs in the big cluster\n",
    "trace.data_frame.cluster_idle_state_residency('big')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd956221b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Actual time spent in each idle state for CPUs in the big and LITTLE clusters\n",
    "ia.plotClusterIdleStateResidency(['big', 'LITTLE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HUHeKi68A/fr1o6mpieXLl+/19fWe39K+zPyWVC4LsJIk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\nJEmSJEmSJEmSJHXh/wGRWeYLPMWu3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9564d26d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Percentage of time spent in each idle state for CPUs in the big and LITTLE clusters\n",
    "ia.plotClusterIdleStateResidency(['big', 'LITTLE'], pct=True)"
   ]
  }
 ],
 "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": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}