ramp_smoke_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "reload(logging)\n",
    "logging.basicConfig(\n",
    "    format='%(asctime)-9s %(levelname)-8s: %(message)s',\n",
    "    datefmt='%I:%M:%S')\n",
    "\n",
    "# Enable logging at INFO level\n",
    "logging.getLogger().setLevel(logging.INFO)\n",
    "# Uncomment the follwing line to enabled devlib debugging statements\n",
    "#logging.getLogger('ssh').setLevel(logging.DEBUG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# Generate plots inline\n",
    "%pylab inline\n",
    "\n",
    "import copy\n",
    "import json\n",
    "import os\n",
    "\n",
    "# Support to access the remote target\n",
    "import devlib\n",
    "from env import TestEnv\n",
    "\n",
    "# Support to configure and run RTApp based workloads\n",
    "from wlgen import RTA, Periodic\n",
    "\n",
    "# Support for performance analysis of RTApp workloads\n",
    "from perf_analysis import PerfAnalysis\n",
    "\n",
    "# Support for trace events analysis\n",
    "from trace import Trace\n",
    "from trace_analysis import TraceAnalysis\n",
    "\n",
    "# Suport for FTrace events parsing and visualization\n",
    "import trappy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test environment setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Setup a target configuration\n",
    "my_target_conf = {\n",
    "    \n",
    "    # Define the kind of target platform to use for the experiments\n",
    "    \"platform\"    : 'linux',  # Linux system, valid other options are:\n",
    "                              # android - access via ADB\n",
    "                              # linux   - access via SSH\n",
    "                              # host    - direct access\n",
    "    \n",
    "    # Preload settings for a specific target\n",
    "    \"board\"       : 'juno',   # load JUNO specific settings, e.g.\n",
    "                              # - HWMON based energy sampling\n",
    "                              # - Juno energy model\n",
    "                              # valid options are:\n",
    "                              # - juno  - JUNO Development Board\n",
    "                              # - tc2   - TC2 Development Board\n",
    "                              # - oak   - Mediatek MT63xx based target\n",
    "\n",
    "    # Account to access the remote target\n",
    "    \"host\"        : '10.1.208.145',\n",
    "    \"username\"    : 'root',\n",
    "    \"password\"    : '',\n",
    "\n",
    "    # Comment the following line to force rt-app calibration on your target\n",
    "    \"rtapp-calib\" : {\n",
    "        '0': 361, '1': 138, '2': 138, '3': 352, '4': 360, '5': 353\n",
    "    }\n",
    "\n",
    "}\n",
    "\n",
    "# Setup the required Test Environment supports\n",
    "my_tests_conf = {\n",
    " \n",
    "    # Binary tools required to run this experiment\n",
    "    # These tools must be present in the tools/ folder for the architecture\n",
    "    \"tools\"   : ['rt-app'],\n",
    "    \n",
    "    # FTrace events end buffer configuration\n",
    "    \"ftrace\"  : {\n",
    "         \"events\" : [\n",
    "            \"sched_switch\",\n",
    "            \"sched_contrib_scale_f\",\n",
    "            \"sched_load_avg_cpu\",\n",
    "            \"sched_load_avg_task\",\n",
    "            \"sched_tune_tasks_update\",\n",
    "            \"sched_boost_cpu\",\n",
    "            \"sched_boost_task\",\n",
    "            \"sched_energy_diff\",\n",
    "            \"cpu_frequency\",\n",
    "            \"cpu_capacity\"\n",
    "         ],\n",
    "         \"buffsize\" : 10240\n",
    "    },\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "02:14:01  INFO    :         Target - Using base path: /home/patbel01/Code/lisa\n",
      "02:14:01  INFO    :         Target - Loading custom (inline) target configuration\n",
      "02:14:01  INFO    :         Target - Loading custom (inline) test configuration\n",
      "02:14:01  INFO    :         Target - Devlib modules to load: ['bl', 'cpufreq', 'hwmon']\n",
      "02:14:01  INFO    :         Target - Connecting linux target:\n",
      "02:14:01  INFO    :         Target -   username : root\n",
      "02:14:01  INFO    :         Target -       host : 10.1.208.145\n",
      "02:14:01  INFO    :         Target -   password : \n",
      "02:14:01  INFO    :         Target - Connection settings:\n",
      "02:14:01  INFO    :         Target -    {'username': 'root', 'host': '10.1.208.145', 'password': ''}\n",
      "02:14:11  INFO    :         Target - Initializing target workdir:\n",
      "02:14:11  INFO    :         Target -    /root/devlib-target\n",
      "02:14:17  INFO    :         Target - Topology:\n",
      "02:14:17  INFO    :         Target -    [[0, 3, 4, 5], [1, 2]]\n",
      "02:14:19  INFO    :       Platform - Loading default EM:\n",
      "02:14:19  INFO    :       Platform -    /home/patbel01/Code/lisa/libs/utils/platforms/juno.json\n",
      "02:14:23  WARNING : Event [sched_contrib_scale_f] not available for tracing\n",
      "02:14:23  WARNING : Event [sched_load_avg_cpu] not available for tracing\n",
      "02:14:23  WARNING : Event [sched_load_avg_task] not available for tracing\n",
      "02:14:23  WARNING : Event [sched_tune_tasks_update] not available for tracing\n",
      "02:14:23  WARNING : Event [sched_boost_cpu] not available for tracing\n",
      "02:14:23  WARNING : Event [sched_boost_task] not available for tracing\n",
      "02:14:23  WARNING : Event [sched_energy_diff] not available for tracing\n",
      "02:14:23  WARNING : Event [cpu_capacity] not available for tracing\n",
      "02:14:23  INFO    :         FTrace - Enabled tracepoints:\n",
      "02:14:23  INFO    :         FTrace -   sched_switch\n",
      "02:14:23  INFO    :         FTrace -   sched_contrib_scale_f\n",
      "02:14:23  INFO    :         FTrace -   sched_load_avg_cpu\n",
      "02:14:23  INFO    :         FTrace -   sched_load_avg_task\n",
      "02:14:23  INFO    :         FTrace -   sched_tune_tasks_update\n",
      "02:14:23  INFO    :         FTrace -   sched_boost_cpu\n",
      "02:14:23  INFO    :         FTrace -   sched_boost_task\n",
      "02:14:23  INFO    :         FTrace -   sched_energy_diff\n",
      "02:14:23  INFO    :         FTrace -   cpu_frequency\n",
      "02:14:23  INFO    :         FTrace -   cpu_capacity\n",
      "02:14:23  WARNING :         Target - Using configuration provided RTApp calibration\n",
      "02:14:23  INFO    :         Target - Using RT-App calibration values:\n",
      "02:14:23  INFO    :         Target -    {\"0\": 361, \"1\": 138, \"2\": 138, \"3\": 352, \"4\": 360, \"5\": 353}\n",
      "02:14:23  INFO    :    EnergyMeter - Scanning for HWMON channels, may take some time...\n",
      "02:14:23  INFO    :    EnergyMeter - Channels selected for energy sampling:\n",
      "02:14:23  INFO    :    EnergyMeter - Using channel A53 as little channel\n",
      "02:14:23  INFO    :    EnergyMeter - Using channel A57 as big channel\n",
      "02:14:23  INFO    :        TestEnv - Set results folder to:\n",
      "02:14:23  INFO    :        TestEnv -    /home/patbel01/Code/lisa/results/20160622_141423\n",
      "02:14:23  INFO    :        TestEnv - Experiment results available also in:\n",
      "02:14:23  INFO    :        TestEnv -    /home/patbel01/Code/lisa/results_latest\n"
     ]
    }
   ],
   "source": [
    "# Initialize a test environment using:\n",
    "# the provided target configuration (my_target_conf)\n",
    "# the provided test configuration (my_test_conf)\n",
    "te = TestEnv(target_conf=my_target_conf, test_conf=my_tests_conf)\n",
    "target = te.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "02:14:52  INFO    : Target ABI: arm64, CPus: ['A53', 'A57', 'A57', 'A53', 'A53', 'A53']\n"
     ]
    }
   ],
   "source": [
    "logging.info(\"Target ABI: %s, CPus: %s\",\n",
    "             target.abi,\n",
    "             target.cpuinfo.cpu_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are a set of functions to run a workload with different CPUFreq configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def set_performance():\n",
    "    target.cpufreq.set_all_governors('performance')\n",
    "\n",
    "def set_powersave():\n",
    "    target.cpufreq.set_all_governors('powersave')\n",
    "\n",
    "def set_interactive():\n",
    "    target.cpufreq.set_all_governors('interactive')\n",
    "\n",
    "def set_sched():\n",
    "    target.cpufreq.set_all_governors('sched')\n",
    "\n",
    "def set_ondemand():\n",
    "    target.cpufreq.set_all_governors('ondemand')\n",
    "    \n",
    "    # Configure the MAX sampling rate supporte by the LITTLE cores\n",
    "    littles_tunables = target.cpufreq.get_governor_tunables(\n",
    "                            target.bl.littles[0])\n",
    "    target.cpufreq.set_governor_tunables(\n",
    "        target.bl.littles[0], 'ondemand',\n",
    "        **{'sampling_rate' : littles_tunables['sampling_rate_min']}\n",
    "    )\n",
    "    \n",
    "    # Configure the MAX sampling rate supporte by the big cores\n",
    "    bigs_tunables = target.cpufreq.get_governor_tunables(\n",
    "                            target.bl.bigs[0])\n",
    "    target.cpufreq.set_governor_tunables(\n",
    "        target.bl.bigs[0], 'ondemand',\n",
    "        **{'sampling_rate' : bigs_tunables['sampling_rate_min']}\n",
    "    )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CPUFreq configurations to test\n",
    "confs = {\n",
    "    'performance' : {\n",
    "        'label' : 'prf',\n",
    "        'set' :  set_performance,\n",
    "    },\n",
    "    'powersave' : {\n",
    "        'label' : 'pws',\n",
    "        'set' :  set_powersave,\n",
    "    },\n",
    "#     'interactive' : {\n",
    "#         'label' : 'int',\n",
    "#         'set' :  set_interactive,\n",
    "#     },\n",
    "    'sched' : {\n",
    "        'label' : 'sch',\n",
    "        'set' :  set_sched,\n",
    "    },\n",
    "    'ondemand' : {\n",
    "        'label' : 'odm',\n",
    "        'set' :  set_ondemand,\n",
    "    }\n",
    "}\n",
    "\n",
    "# The set of results forlder for each comparition test\n",
    "results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "code_folding": [],
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def experiment(governor, rtapp, exp_dir):\n",
    "    os.system('mkdir -p {}'.format(exp_dir));\n",
    "\n",
    "    logging.info('------------------------')\n",
    "    logging.info('Run workload using %s governor', governor)\n",
    "    confs[governor]['set']()\n",
    "\n",
    "    # Run workload while collecting a trace\n",
    "    te.ftrace.start()\n",
    "    rtapp.run(out_dir=exp_dir)\n",
    "    te.ftrace.stop()\n",
    "    \n",
    "    # Dump platform description\n",
    "    te.platform_dump(exp_dir)\n",
    "    \n",
    "    # Collect and keep track of the trace\n",
    "    trace_file = os.path.join(exp_dir, 'trace.dat')\n",
    "    te.ftrace.get_trace(trace_file)\n",
    "\n",
    "# Full analysis function\n",
    "def analysis(exp_dir, t_min=None, t_max=None):\n",
    "    platform_json = '{}/platform.json'.format(exp_dir)\n",
    "    trace_file = '{}/trace.dat'.format(exp_dir)\n",
    "    \n",
    "    # Load platform description data\n",
    "    with open(platform_json, 'r') as fh:\n",
    "        platform = json.load(fh)\n",
    "\n",
    "    # Load RTApp Performance data\n",
    "    pa = PerfAnalysis(exp_dir)\n",
    "    logging.info(\"Loaded performance data for tasks: %s\", pa.tasks())\n",
    "    \n",
    "    # Load Trace data\n",
    "    events = [\n",
    "        \"sched_switch\",\n",
    "        \"sched_contrib_scale_f\",\n",
    "        \"sched_load_avg_cpu\",\n",
    "        \"sched_load_avg_task\",\n",
    "        \"sched_tune_tasks_update\",\n",
    "        \"sched_boost_cpu\",\n",
    "        \"sched_boost_task\",\n",
    "        \"sched_energy_diff\",\n",
    "        \"cpu_frequency\",\n",
    "        \"cpu_capacity\"\n",
    "    ]\n",
    "    trace = Trace(platform, exp_dir, events, tasks=pa.tasks())\n",
    "    ta = TraceAnalysis(trace, tasks=pa.tasks())\n",
    "    \n",
    "    # Define time ranges for all the temporal plots\n",
    "    ta.setXTimeRange(t_min, t_max)\n",
    "    \n",
    "    # Tasks plots\n",
    "    ta.plotTasks()\n",
    "    for task in pa.tasks():\n",
    "        pa.plotPerf(task)\n",
    "\n",
    "    # Cluster and CPUs plots\n",
    "    ta.plotClusterFrequencies()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test: Ramp Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "02:16:18  INFO    :          WlGen - Setup new workload smoke\n",
      "02:16:18  INFO    :          RTApp - Workload duration defined by longest task\n",
      "02:16:18  INFO    :          RTApp - Default policy: SCHED_OTHER\n",
      "02:16:18  INFO    :          RTApp - ------------------------\n",
      "02:16:18  INFO    :          RTApp - task [r5_10-60], sched: using default policy\n",
      "02:16:18  INFO    :          RTApp -  | calibration CPU: 1\n",
      "02:16:18  INFO    :          RTApp -  | loops count: 1\n",
      "02:16:18  INFO    :          RTApp - + phase_000001: duration 1.000000 [s] (166 loops)\n",
      "02:16:18  INFO    :          RTApp - |  period     6000 [us], duty_cycle   5 %\n",
      "02:16:18  INFO    :          RTApp - |  run_time    300 [us], sleep_time   5700 [us]\n",
      "02:16:18  INFO    :          RTApp - + phase_000002: duration 1.000000 [s] (166 loops)\n",
      "02:16:18  INFO    :          RTApp - |  period     6000 [us], duty_cycle  10 %\n",
      "02:16:18  INFO    :          RTApp - |  run_time    600 [us], sleep_time   5400 [us]\n",
      "02:16:18  INFO    :          RTApp - + phase_000003: duration 1.000000 [s] (166 loops)\n",
      "02:16:18  INFO    :          RTApp - |  period     6000 [us], duty_cycle  15 %\n",
      "02:16:18  INFO    :          RTApp - |  run_time    900 [us], sleep_time   5100 [us]\n",
      "02:16:18  INFO    :          RTApp - + phase_000004: duration 1.000000 [s] (166 loops)\n",
      "02:16:18  INFO    :          RTApp - |  period     6000 [us], duty_cycle  20 %\n",
      "02:16:18  INFO    :          RTApp - |  run_time   1200 [us], sleep_time   4800 [us]\n",
      "02:16:18  INFO    :          RTApp - + phase_000005: duration 1.000000 [s] (166 loops)\n",
      "02:16:18  INFO    :          RTApp - |  period     6000 [us], duty_cycle  25 %\n",
      "02:16:18  INFO    :          RTApp - |  run_time   1500 [us], sleep_time   4500 [us]\n",
      "02:16:18  INFO    :          RTApp - + phase_000006: duration 1.000000 [s] (166 loops)\n",
      "02:16:18  INFO    :          RTApp - |  period     6000 [us], duty_cycle  30 %\n",
      "02:16:18  INFO    :          RTApp - |  run_time   1800 [us], sleep_time   4200 [us]\n",
      "02:16:18  INFO    :          RTApp - + phase_000007: duration 1.000000 [s] (166 loops)\n",
      "02:16:18  INFO    :          RTApp - |  period     6000 [us], duty_cycle  35 %\n",
      "02:16:18  INFO    :          RTApp - |  run_time   2100 [us], sleep_time   3900 [us]\n",
      "02:16:18  INFO    :          RTApp - + phase_000008: duration 1.000000 [s] (166 loops)\n",
      "02:16:18  INFO    :          RTApp - |  period     6000 [us], duty_cycle  40 %\n",
      "02:16:18  INFO    :          RTApp - |  run_time   2400 [us], sleep_time   3600 [us]\n",
      "02:16:18  INFO    :          RTApp - + phase_000009: duration 1.000000 [s] (166 loops)\n",
      "02:16:18  INFO    :          RTApp - |  period     6000 [us], duty_cycle  45 %\n",
      "02:16:18  INFO    :          RTApp - |  run_time   2700 [us], sleep_time   3300 [us]\n",
      "02:16:19  INFO    :          RTApp - + phase_000010: duration 1.000000 [s] (166 loops)\n",
      "02:16:19  INFO    :          RTApp - |  period     6000 [us], duty_cycle  50 %\n",
      "02:16:19  INFO    :          RTApp - |  run_time   3000 [us], sleep_time   3000 [us]\n",
      "02:16:19  INFO    :          RTApp - + phase_000011: duration 1.000000 [s] (166 loops)\n",
      "02:16:19  INFO    :          RTApp - |  period     6000 [us], duty_cycle  55 %\n",
      "02:16:19  INFO    :          RTApp - |  run_time   3300 [us], sleep_time   2700 [us]\n",
      "02:16:19  INFO    :          RTApp - + phase_000012: duration 1.000000 [s] (166 loops)\n",
      "02:16:19  INFO    :          RTApp - |  period     6000 [us], duty_cycle  60 %\n",
      "02:16:19  INFO    :          RTApp - |  run_time   3600 [us], sleep_time   2400 [us]\n"
     ]
    }
   ],
   "source": [
    "from wlgen import Ramp\n",
    "\n",
    "rtapp = RTA(target, 'smoke', calibration=te.calibration())\n",
    "rtapp.conf(\n",
    "    kind='profile',\n",
    "    params={\n",
    "        'r5_10-60' : Ramp(\n",
    "            period_ms  =  6,\n",
    "            start_pct  =  5,\n",
    "            end_pct    = 60,\n",
    "            delta_pct  =  5,\n",
    "            time_s     =  1\n",
    "        ).get()\n",
    "    },\n",
    "    run_dir=target.working_directory\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "02:20:34  INFO    : ------------------------\n",
      "02:20:34  INFO    : Run workload using performance governor\n",
      "02:20:38  INFO    :          WlGen - Workload execution START:\n",
      "02:20:38  INFO    :          WlGen -    /root/devlib-target/bin/rt-app /root/devlib-target/smoke_00.json\n"
     ]
    }
   ],
   "source": [
    "experiment('performance', rtapp, te.res_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "02:21:30  INFO    : Loaded performance data for tasks: ['r5_10-60']\n",
      "02:21:30  INFO    : Parsing FTrace format...\n",
      "02:21:30  INFO    :   task         r5_10-60 found, pid: [1237]\n",
      "02:21:30  INFO    : Collected events spans a 13.650 [s] time interval\n",
      "02:21:30  INFO    : Set plots time range to (0.000000, 13.650085)[s]\n",
      "02:21:30  INFO    : Set plots time range to (0.000000, 13.650085)[s]\n",
      "02:21:30  WARNING : Events [sched_load_avg_task] not found, plot DISABLED!\n",
      "02:21:30  INFO    : PerfIndex, Task [r5_10-60] avg: 0.86, std: 0.03\n",
      "02:21:31  WARNING : Events [sched_overutilized] not found, plot DISABLED!\n",
      "02:21:31  WARNING : Events [sched_overutilized] not found, plot DISABLED!\n",
      "02:21:31  INFO    : LITTLE cluster average frequency: 0.850 GHz\n",
      "02:21:31  INFO    : big    cluster average frequency: 1.100 GHz\n"
     ]
    },
    {
     "data": {
      "image/png": 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Yl6If5KUp/sFb/N/Exvh//VZFdR2Jn9AnmS6vG6P4HTATP6tfzDX4Lqn5+LEi/3DOvZWo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\nGP8fx5asmRNz5nQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ebfbf3d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SpA6rqh2uxL/kRW2SVcDPAj81cHgrcOTA+Ah6Z2i30t+i3B4fvnn99y3WRa80WdasWcOa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ebf307490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis(te.res_dir)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}