20180531_UtilEst_RunningAvg

20180531_UtilEst_RunningAvg.ipynb

20180605_UtilEst_RunningAvg.ipynb

{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# RT-PELT Validation Mainline v4.17"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "USE_UTIL_EST = True\nKERNEL_DIR = '/data/Code/linux-kernel'",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Imports"
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "frozen": false,
          "read_only": false,
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import logging\nreload(logging)\nlogging.basicConfig(\n    format='%(asctime)-9s %(levelname)-8s: %(message)s',\n    datefmt='%I:%M:%S')\n\n# Enable logging at INFO level\nlogging.getLogger().setLevel(logging.INFO)\n# Comment the follwing line to disable devlib debugging statements\n#logging.getLogger('ssh').setLevel(logging.DEBUG)",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Generate plots inline\n%pylab inline",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Populating the interactive namespace from numpy and matplotlib\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "frozen": false,
          "read_only": false,
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import json\nimport os\nimport subprocess\nimport time\n\nimport devlib\nimport trappy\nimport pandas as pd\n\nfrom env import TestEnv\nfrom git import Git\nfrom trace import Trace\nfrom wlgen import RTA, Periodic, Ramp, PerfMessaging",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        }
      },
      "cell_type": "markdown",
      "source": "# Utility Functions"
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "frozen": false,
          "read_only": false,
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "def run(wload, collect='ftrace', governor='schedutil',\n        use_util_est=False, trace_name=None):\n    \n    # Run workload on target\n    if not trace_name:\n        logging.info(\"#### Set [%s] CPUFreq governor\", governor)\n        te.target.cpufreq.set_all_governors(governor)\n        if governor == 'schedutil':\n            for cpu_id in range(te.platform['cpus_count']):\n                if 'up_rate_limit_us' in te.target.cpufreq.get_governor_tunables(0):\n                    te.target.cpufreq.set_governor_tunables(\n                        cpu_id, 'schedutil', **{\n                            'up_rate_limit_us' : '10000',\n                            'down_rate_limit_us' : '10000'\n                        })\n                else:\n                    te.target.cpufreq.set_governor_tunables(\n                        cpu_id, 'schedutil', **{\n                            'rate_limit_us' : '5000',\n                        })      \n\n#         feats = te.target.execute('cat /sys/kernel/debug/sched_features')\n#         if 'UTIL_EST' in feats:\n#             logging.info(\"#### Enable Utilization estimation\")\n#             feature = 'UTIL_EST' if use_util_est else 'NO_UTIL_EST'\n#             te.target.execute(\"echo {} > /sys/kernel/debug/sched_features\"\\\n#                               .format(feature))\n#         else:\n#             logging.warning(\"### Kernel missing UtilEst support (i.e. sched_feat(UTIL_EST) no available)\")\n\n        if 'trace' in collect:\n            logging.info('#### Setup FTrace')\n            te.ftrace.start()\n        if 'energy' in collect:\n            logging.info('#### Start energy sampling')\n            te.emeter.reset()\n\n        logging.info('#### Start RTApp execution')\n        wload.run(out_dir=te.res_dir)\n\n        if 'energy' in collect:\n            logging.info('#### Read energy consumption: %s/energy.json', te.res_dir)\n            (nrg, nrg_file) = te.emeter.report(out_dir=te.res_dir)\n        if 'trace' in collect:\n            logging.info('#### Stop FTrace')\n            te.ftrace.stop()\n        \n        trace_name = 'trace_{}.dat'.format(wload.name)\n        trace_file = os.path.join(te.res_dir, trace_name)\n        logging.info('#### Save FTrace: %s', trace_file)\n        te.ftrace.get_trace(trace_file)\n        \n        # Reset governor to ondemand\n        te.target.cpufreq.set_all_governors('ondemand')\n\n        logging.info('#### Save platform description: %s/platform.json', te.res_dir)\n        (plt, plt_file) = te.platform_dump(te.res_dir)\n        \n        platform = te.platform\n        \n    # Load specified trace\n    else:\n        results_dir = os.path.join(\n            os.environ.get('LISA_HOME'), 'results',\n            my_conf['results_dir'])\n        trace_file = os.path.join(results_dir,\n                                  'trace_{}.dat'.format(trace_name))\n        logging.info('#### Using trace_file: %s', trace_file)\n        \n        platform_file = os.path.join(results_dir, 'platform.json')\n        with open(platform_file, 'r') as fh:\n            platform = json.load(fh)\n        \n    logging.info('#### Parse trace')\n    events_to_parse = my_conf['ftrace']['events']\n    trace = Trace(platform, trace_file, events_to_parse)\n\n    return trace_file, trace\n\ndef plot_signals(trace, tasks, cpus,\n         additional_signals=[], #'last', 'ewma',\n         x_min=0, x_max=None):\n    \n    trace.setXTimeRange(x_min, x_max)\n    \n    # Add additional signals\n    signals = ['residencies'] + additional_signals\n    \n    trace.analysis.tasks.plotTasks(tasks, signals)\n    trace.analysis.frequency.plotCPUFrequencies(cpus)\n    trace.analysis.frequency.plotCPUFrequencyResidency(cpus, pct=True)\n",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "run_control": {
          "frozen": false,
          "read_only": false
        }
      },
      "cell_type": "markdown",
      "source": "# Target Connection"
    },
    {
      "metadata": {
        "collapsed": true,
        "run_control": {
          "marked": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Setup a target configuration\nmy_conf = {\n    \n    # Define the kind of target platform to use for the experiments\n    \"platform\"    : 'linux',  # Linux system, valid other options are:\n                              # android - access via ADB\n                              # linux   - access via SSH\n                              # host    - direct access\n    \n    # Preload settings for a specific target\n    \"board\"       : 'juno',   # load JUNO specific settings, e.g.\n                              # - HWMON based energy sampling\n                              # - Juno energy model\n                              # valid options are:\n                              # - juno  - JUNO Development Board\n                              # - tc2   - TC2 Development Board\n                              # - oak   - Mediatek MT63xx based target\n\n    # Define devlib module to load\n    \"exclude_modules\"     : [\n        'hwmon',        # do not load HWMON, we do not need to measure NRG\n    ],\n\n    # Account to access the remote target\n    \"host\"        : '192.168.90.1',\n    \"username\"    : 'root',\n    \"password\"    : '',\n\n    # Comment the following line to force rt-app calibration on your target\n    \"rtapp-calib\" : {\n        \"0\": 584, \"1\": 262, \"2\": 262, \"3\": 584, \"4\": 584, \"5\": 584        \n    },\n\n    # Binary tools required to run this experiment\n    # These tools must be present in the tools/ folder for the architecture\n    \"tools\"   : ['rt-app', 'taskset', 'trace-cmd'],\n    \n    # FTrace events end buffer configuration\n    \"ftrace\"  : {\n         \"events\" : [\n             \"cpu_idle\",\n             \"cpu_frequency\",\n             \"sched_load_se\",\n             \"sched_load_cfs_rq\",\n             \"sched_util_est_task\",\n             \"sched_util_est_cpu\",\n             \"sched_switch\",\n             \"sched_wakeup\",\n         ],\n         \"buffsize\" : 10240\n    },\n    \n    \"results_dir\" : time.strftime(\"%Y%m%d\") + \"_RT-PELT_JunoR2\",\n\n}",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": false,
        "run_control": {
          "frozen": false,
          "read_only": false
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "te = TestEnv(my_conf, force_new=True)\ntarget = te.target",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:12:37  INFO    : Using base path: /data/Code/lisa\n03:12:37  INFO    : Loading custom (inline) target configuration\n03:12:37  WARNING : Wipe previous contents of the results folder:\n03:12:37  WARNING :    /data/Code/lisa/results/20180605_RT-PELT_JunoR2\n03:12:37  INFO    : Devlib modules to load: ['bl', 'cpufreq']\n03:12:37  INFO    : Connecting linux target:\n03:12:37  INFO    :   username : root\n03:12:37  INFO    :       host : 192.168.90.1\n03:12:37  INFO    :   password : \n03:12:37  INFO    : Connection settings:\n03:12:37  INFO    :    {'username': 'root', 'host': '192.168.90.1', 'password': ''}\n03:12:54  INFO    : Initializing target workdir:\n03:12:54  INFO    :    /root/devlib-target\n03:12:57  INFO    : Topology:\n03:12:57  INFO    :    [[0, 3, 4, 5], [1, 2]]\n03:12:57  INFO    : Loading default EM:\n03:12:57  INFO    :    /data/Code/lisa/libs/utils/platforms/juno.json\n03:13:02  INFO    : Enabled tracepoints:\n03:13:02  INFO    :    cpu_frequency\n03:13:02  INFO    :    cpu_idle\n03:13:02  INFO    :    sched_load_cfs_rq\n03:13:02  INFO    :    sched_load_se\n03:13:02  INFO    :    sched_switch\n03:13:02  INFO    :    sched_util_est_cpu\n03:13:02  INFO    :    sched_util_est_task\n03:13:02  INFO    :    sched_wakeup\n03:13:02  INFO    : Using configuration provided RTApp calibration\n03:13:02  INFO    : Using RT-App calibration values:\n03:13:02  INFO    :    {\"0\": 584, \"1\": 262, \"2\": 262, \"3\": 584, \"4\": 584, \"5\": 584}\n03:13:02  INFO    : HWMON module not enabled\n03:13:02  WARNING : Energy sampling disabled by configuration\n03:13:02  INFO    : Set results folder to:\n03:13:02  INFO    :    /data/Code/lisa/results/20180605_RT-PELT_JunoR2\n03:13:02  INFO    : Experiment results available also in:\n03:13:02  INFO    :    /data/Code/lisa/results_latest\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "te.target.kernel_version",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "4.17.0-rc6-00226-g37fbd3297cea-dirty 173 SMP PREEMPT Tue Jun 5 15:07:16 BST 2018"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "big_cpu = target.core_cpus(target.big_core)[-1]",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# CFS Task Running Alone"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "rtapp = RTA(target, 'alone', calibration=te.calibration())\nrtapp.conf(\n    kind='profile',\n    params={\n        'task_p60': Periodic(\n            period_ms=100,          # period\n            duty_cycle_pct=60,     # duty cycle\n            duration_s=2,          # duration    \n            cpus=str(big_cpu)      # pinned on last big CPU\n        ).get(),\n#         'task_p20': Periodic(\n#             period_ms=10,          # period\n#             duty_cycle_pct=20,     # duty cycle\n#             duration_s=2,          # duration    \n#             delay_s=0.020,         # delay (20ms)\n#             cpus=str(big_cpu),     # pinned on last big CPU,\n#             sched={\n#                 'policy': 'FIFO',\n#                 'prio': 10,\n#             }\n#         ).get(),\n    },\n    run_dir=target.working_directory\n);",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:13:02  INFO    : Setup new workload alone\n03:13:02  INFO    : Workload duration defined by longest task\n03:13:02  INFO    : Default policy: SCHED_OTHER\n03:13:02  INFO    : ------------------------\n03:13:02  INFO    : task [task_p60], sched: using default policy\n03:13:02  INFO    :  | loops count: 1\n03:13:02  INFO    : + phase_000001: duration 2.000000 [s] (20 loops)\n03:13:02  INFO    : |  period   100000 [us], duty_cycle  60 %\n03:13:02  INFO    : |  run_time  60000 [us], sleep_time  40000 [us]\n03:13:02  INFO    : |  CPUs affinity: 2\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "tracefile, trace = run(rtapp, use_util_est=USE_UTIL_EST)\n# tracefile, trace = run(None, use_util_est=USE_UTIL_EST, trace_name=test_id)",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:13:03  INFO    : #### Set [schedutil] CPUFreq governor\n03:13:09  INFO    : #### Setup FTrace\n03:13:14  INFO    : #### Start RTApp execution\n03:13:14  INFO    : Workload execution START:\n03:13:14  INFO    :    /root/devlib-target/bin/rt-app /root/devlib-target/alone_00.json 2>&1\n03:13:17  INFO    : #### Stop FTrace\n03:13:17  INFO    : #### Save FTrace: /data/Code/lisa/results/20180605_RT-PELT_JunoR2/trace_alone.dat\n03:13:22  INFO    : #### Save platform description: /data/Code/lisa/results/20180605_RT-PELT_JunoR2/platform.json\n03:13:22  INFO    : #### Parse trace\n03:13:23  INFO    : Platform clusters verified to be Frequency coherent\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = trace.data_frame.trace_event('sched_util_est_task')\ndf[df.pid == trace.getTaskByName('task_p60')[0]][['util_avg', 'util_est_enqueued', 'util_est_ewma']].plot(figsize=(16,8), drawstyle='steps-post');\nlogging.info(\"Task Utilization\")",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:13:23  INFO    : Task Utilization\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EgN25adm63L7i0axcvylzDj+g3+FAlWwntIkWVNiFBbNn5J0Lju53GDt107J1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\nVnbFcWUn2pKgHpHk4R3eP5Kdr/iLO90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            "text/plain": "<matplotlib.figure.Figure at 0x7f76a343c950>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = trace.data_frame.trace_event('sched_load_se')\ndf[df.pid == trace.getTaskByName('task_p60')[0]][['util', 'running']][2.5:3].plot(figsize=(16,8), drawstyle='steps-post');\nlogging.info(\"Task Utilization\")",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:13:23  INFO    : Task Utilization\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7f76a34d3b10>"
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      ]
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        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#!kernelshark $trace.ftrace.trace_path",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# CFS Task Preempted by RT Task"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "rtapp = RTA(target, 'preempted', calibration=te.calibration())\nrtapp.conf(\n    kind='profile',\n    params={\n        'task_p60': Periodic(\n            period_ms=100,          # period\n            duty_cycle_pct=60,     # duty cycle\n            duration_s=2,          # duration    \n            cpus=str(big_cpu)      # pinned on last big CPU\n        ).get(),\n        'task_p20': Periodic(\n            period_ms=10,          # period\n            duty_cycle_pct=20,     # duty cycle\n            duration_s=2,          # duration    \n            delay_s=0.020,         # delay (20ms)\n            cpus=str(big_cpu),     # pinned on last big CPU,\n            sched={\n                'policy': 'FIFO',\n                'prio': 10,\n            }\n        ).get(),\n    },\n    run_dir=target.working_directory\n)",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:13:23  INFO    : Setup new workload preempted\n03:13:23  INFO    : Workload duration defined by longest task\n03:13:23  INFO    : Default policy: SCHED_OTHER\n03:13:23  INFO    : ------------------------\n03:13:23  INFO    : task [task_p20], sched: {'policy': 'FIFO', 'prio': 10}\n03:13:23  INFO    :  | start delay: 0.020000 [s]\n03:13:23  INFO    :  | loops count: 1\n03:13:23  INFO    : + phase_000001: duration 2.000000 [s] (200 loops)\n03:13:23  INFO    : |  period    10000 [us], duty_cycle  20 %\n03:13:23  INFO    : |  run_time   2000 [us], sleep_time   8000 [us]\n03:13:23  INFO    : |  CPUs affinity: 2\n03:13:23  INFO    : ------------------------\n03:13:23  INFO    : task [task_p60], sched: using default policy\n03:13:23  INFO    :  | loops count: 1\n03:13:23  INFO    : + phase_000001: duration 2.000000 [s] (20 loops)\n03:13:23  INFO    : |  period   100000 [us], duty_cycle  60 %\n03:13:23  INFO    : |  run_time  60000 [us], sleep_time  40000 [us]\n03:13:23  INFO    : |  CPUs affinity: 2\n",
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "'preempted_00'"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "tracefile, trace = run(rtapp, use_util_est=USE_UTIL_EST)\n# tracefile, trace = run(None, use_util_est=USE_UTIL_EST, trace_name=test_id)",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:13:24  INFO    : #### Set [schedutil] CPUFreq governor\n03:13:30  INFO    : #### Setup FTrace\n03:13:35  INFO    : #### Start RTApp execution\n03:13:35  INFO    : Workload execution START:\n03:13:35  INFO    :    /root/devlib-target/bin/rt-app /root/devlib-target/preempted_00.json 2>&1\n03:13:38  INFO    : #### Stop FTrace\n03:13:39  INFO    : #### Save FTrace: /data/Code/lisa/results/20180605_RT-PELT_JunoR2/trace_preempted.dat\n03:13:52  INFO    : #### Save platform description: /data/Code/lisa/results/20180605_RT-PELT_JunoR2/platform.json\n03:13:52  INFO    : #### Parse trace\n03:13:53  INFO    : Platform clusters verified to be Frequency coherent\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = trace.data_frame.trace_event('sched_util_est_task')\ndf[df.pid == trace.getTaskByName('task_p60')[0]][['util_avg', 'util_est_enqueued', 'util_est_ewma']].plot(figsize=(16,8), drawstyle='steps-post');\nlogging.info(\"Task Utilization\")",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:13:53  INFO    : Task Utilization\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WjkNuUoTocPoxmG24/92D0Gs1+MPlUxCgk7PMguIHizJaDBYcqmrzejw2LAATUyJ9Fk99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\nkV+LJglujr7wMGio1/am/Dq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rm9d3lONZiVt97C1rxuEqOa8ZZpsdq5/4Gi2StqxqM1rx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\nKj3ZOFXFCeFxZg8CKjxX11inlbtXqqah9fOZ3X398UtVenH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VWr16tfLz87V6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\nrkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f76a37262d0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = trace.data_frame.trace_event('sched_load_se')\ndf[df.pid == trace.getTaskByName('task_p60')[0]][['util', 'running']][1.5:1.8].plot(figsize=(16,8), drawstyle='steps-post');\nlogging.info(\"Task Utilization\")",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:13:54  INFO    : Task Utilization\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m9x0XPJe0YSqZ2vXw24WGwyy2JikAAMCgCajjaMNUsueGYbcCAGDkzBw5tuAD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\nVCkAAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f76a36eddd0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Bigger  RT task preemption"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "rtapp = RTA(target, 'preempted_big', calibration=te.calibration())\nrtapp.conf(\n    kind='profile',\n    params={\n        'task_p60': Periodic(\n            period_ms=100,          # period\n            duty_cycle_pct=60,      # duty cycle\n            duration_s=2,          # duration    \n            cpus=str(big_cpu)      # pinned on last big CPU\n        ).get(),\n        'task_p20': Periodic(\n            period_ms=10,          # period\n            duty_cycle_pct=80,     # duty cycle\n            duration_s=2,          # duration    \n            delay_s=0.020,         # delay (20ms)\n            cpus=str(big_cpu),     # pinned on last big CPU,\n            sched={\n                'policy': 'FIFO',\n                'prio': 10,\n            }\n        ).get(),\n    },\n    run_dir=target.working_directory\n)",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:13:54  INFO    : Setup new workload preempted_big\n03:13:54  INFO    : Workload duration defined by longest task\n03:13:54  INFO    : Default policy: SCHED_OTHER\n03:13:54  INFO    : ------------------------\n03:13:54  INFO    : task [task_p20], sched: {'policy': 'FIFO', 'prio': 10}\n03:13:54  INFO    :  | start delay: 0.020000 [s]\n03:13:54  INFO    :  | loops count: 1\n03:13:54  INFO    : + phase_000001: duration 2.000000 [s] (200 loops)\n03:13:54  INFO    : |  period    10000 [us], duty_cycle  80 %\n03:13:54  INFO    : |  run_time   8000 [us], sleep_time   2000 [us]\n03:13:54  INFO    : |  CPUs affinity: 2\n03:13:54  INFO    : ------------------------\n03:13:54  INFO    : task [task_p60], sched: using default policy\n03:13:54  INFO    :  | loops count: 1\n03:13:54  INFO    : + phase_000001: duration 2.000000 [s] (20 loops)\n03:13:54  INFO    : |  period   100000 [us], duty_cycle  60 %\n03:13:54  INFO    : |  run_time  60000 [us], sleep_time  40000 [us]\n03:13:54  INFO    : |  CPUs affinity: 2\n",
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/plain": "'preempted_big_00'"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "tracefile, trace = run(rtapp, use_util_est=USE_UTIL_EST)\n# tracefile, trace = run(None, use_util_est=USE_UTIL_EST, trace_name=test_id)",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:13:54  INFO    : #### Set [schedutil] CPUFreq governor\n03:14:01  INFO    : #### Setup FTrace\n03:14:05  INFO    : #### Start RTApp execution\n03:14:05  INFO    : Workload execution START:\n03:14:05  INFO    :    /root/devlib-target/bin/rt-app /root/devlib-target/preempted_big_00.json 2>&1\n03:14:10  INFO    : #### Stop FTrace\n03:14:11  INFO    : #### Save FTrace: /data/Code/lisa/results/20180605_RT-PELT_JunoR2/trace_preempted_big.dat\n03:14:14  INFO    : #### Save platform description: /data/Code/lisa/results/20180605_RT-PELT_JunoR2/platform.json\n03:14:14  INFO    : #### Parse trace\n03:14:16  INFO    : Platform clusters verified to be Frequency coherent\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = trace.data_frame.trace_event('sched_util_est_task')\ndf[df.pid == trace.getTaskByName('task_p60')[0]][['util_avg', 'util_est_enqueued', 'util_est_ewma']].plot(figsize=(16,8), drawstyle='steps-post');\nlogging.info(\"Task Utilization\")",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:14:16  INFO    : Task Utilization\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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llbMhq4Q6l4eI0MAK6bqGAnrZzROIj/L9DJ5Ny+LZtOyAsr6Z+d7PzvNbfP97dSavbzl+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7miJb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\nSY9vAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f76a356acd0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = trace.data_frame.trace_event('sched_load_se')\ndf[df.pid == trace.getTaskByName('task_p60')[0]][['util', 'running']][1.5:1.8].plot(figsize=(16,8), drawstyle='steps-post');\nlogging.info(\"Task Utilization\")",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:14:16  INFO    : Task Utilization\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/++T0888//8m4fP7zn589e/bkwgsvfMpzTzjhhFx66aVJkvPOOy933HFHkuTOO+/M+973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\nXfj/6woQLdOEeqEAAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f76a369d9d0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# One small task"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "rtapp = RTA(target, 'one_small', calibration=te.calibration())\nrtapp.conf(\n    kind='profile',\n    params={\n        'task_p40_1': Periodic(\n            period_ms=10,          # period\n            duty_cycle_pct=10,     # duty cycle\n            duration_s=2,          # duration    \n            cpus=str(big_cpu)      # pinned on last big CPU\n        ).get(),\n    },\n    run_dir=target.working_directory\n)",
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:20:13  INFO    : Setup new workload one_small\n03:20:13  INFO    : Workload duration defined by longest task\n03:20:13  INFO    : Default policy: SCHED_OTHER\n03:20:13  INFO    : ------------------------\n03:20:13  INFO    : task [task_p40_1], sched: using default policy\n03:20:13  INFO    :  | loops count: 1\n03:20:13  INFO    : + phase_000001: duration 2.000000 [s] (200 loops)\n03:20:13  INFO    : |  period    10000 [us], duty_cycle  10 %\n03:20:13  INFO    : |  run_time   1000 [us], sleep_time   9000 [us]\n03:20:13  INFO    : |  CPUs affinity: 2\n",
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "execution_count": 56,
          "data": {
            "text/plain": "'one_small_00'"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "tracefile, trace = run(rtapp, use_util_est=USE_UTIL_EST)\n# tracefile, trace = run(None, use_util_est=USE_UTIL_EST, trace_name=test_id)",
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:20:13  INFO    : #### Set [schedutil] CPUFreq governor\n03:20:20  INFO    : #### Setup FTrace\n03:20:24  INFO    : #### Start RTApp execution\n03:20:24  INFO    : Workload execution START:\n03:20:24  INFO    :    /root/devlib-target/bin/rt-app /root/devlib-target/one_small_00.json 2>&1\n03:20:27  INFO    : #### Stop FTrace\n03:20:27  INFO    : #### Save FTrace: /data/Code/lisa/results/20180605_RT-PELT_JunoR2/trace_one_small.dat\n03:20:32  INFO    : #### Save platform description: /data/Code/lisa/results/20180605_RT-PELT_JunoR2/platform.json\n03:20:32  INFO    : #### Parse trace\n03:20:33  INFO    : Platform clusters verified to be Frequency coherent\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = trace.data_frame.trace_event('sched_util_est_task')\ndf[df.pid == trace.getTaskByName('task_p40_1')[0]][['util_avg', 'util_est_enqueued', 'util_est_ewma']].plot(figsize=(16,8), drawstyle='steps-post');\nlogging.info(\"Task Utilization\")",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:20:33  INFO    : Task Utilization\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V6DgVFRVcc801TJ06lVmzZvHNb36Tm266iTZt2lBaWsq2bdt47bXXKCws5LXXXmP58uW0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\nrEvA5w4AAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f76a35f2450>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df[df.pid == trace.getTaskByName('task_p40_1')[0]][1:].describe().T",
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 59,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\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>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>__cpu</th>\n      <td>504.0</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>__line</th>\n      <td>504.0</td>\n      <td>8302.365079</td>\n      <td>1042.566510</td>\n      <td>6520.0</td>\n      <td>7380.5</td>\n      <td>8313.5</td>\n      <td>9181.5</td>\n      <td>10191.0</td>\n    </tr>\n    <tr>\n      <th>__pid</th>\n      <td>504.0</td>\n      <td>1022.809524</td>\n      <td>1327.365898</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2742.0</td>\n      <td>2742.0</td>\n    </tr>\n    <tr>\n      <th>cpu</th>\n      <td>504.0</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>pid</th>\n      <td>504.0</td>\n      <td>2742.000000</td>\n      <td>0.000000</td>\n      <td>2742.0</td>\n      <td>2742.0</td>\n      <td>2742.0</td>\n      <td>2742.0</td>\n      <td>2742.0</td>\n    </tr>\n    <tr>\n      <th>running_avg</th>\n      <td>504.0</td>\n      <td>113.478175</td>\n      <td>8.405215</td>\n      <td>100.0</td>\n      <td>103.0</td>\n      <td>119.0</td>\n      <td>120.0</td>\n      <td>123.0</td>\n    </tr>\n    <tr>\n      <th>util_avg</th>\n      <td>504.0</td>\n      <td>109.263889</td>\n      <td>8.261134</td>\n      <td>102.0</td>\n      <td>103.0</td>\n      <td>104.0</td>\n      <td>121.0</td>\n      <td>124.0</td>\n    </tr>\n    <tr>\n      <th>util_est_enqueued</th>\n      <td>504.0</td>\n      <td>118.049603</td>\n      <td>0.308140</td>\n      <td>118.0</td>\n      <td>118.0</td>\n      <td>118.0</td>\n      <td>118.0</td>\n      <td>120.0</td>\n    </tr>\n    <tr>\n      <th>util_est_ewma</th>\n      <td>504.0</td>\n      <td>126.071429</td>\n      <td>0.457820</td>\n      <td>126.0</td>\n      <td>126.0</td>\n      <td>126.0</td>\n      <td>126.0</td>\n      <td>129.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                   count         mean          std     min     25%     50%  \\\n__cpu              504.0     2.000000     0.000000     2.0     2.0     2.0   \n__line             504.0  8302.365079  1042.566510  6520.0  7380.5  8313.5   \n__pid              504.0  1022.809524  1327.365898     0.0     0.0     0.0   \ncpu                504.0     2.000000     0.000000     2.0     2.0     2.0   \npid                504.0  2742.000000     0.000000  2742.0  2742.0  2742.0   \nrunning_avg        504.0   113.478175     8.405215   100.0   103.0   119.0   \nutil_avg           504.0   109.263889     8.261134   102.0   103.0   104.0   \nutil_est_enqueued  504.0   118.049603     0.308140   118.0   118.0   118.0   \nutil_est_ewma      504.0   126.071429     0.457820   126.0   126.0   126.0   \n\n                      75%      max  \n__cpu                 2.0      2.0  \n__line             9181.5  10191.0  \n__pid              2742.0   2742.0  \ncpu                   2.0      2.0  \npid                2742.0   2742.0  \nrunning_avg         120.0    123.0  \nutil_avg            121.0    124.0  \nutil_est_enqueued   118.0    120.0  \nutil_est_ewma       126.0    129.0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Two small tasks synched"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "rtapp = RTA(target, 'two_synched', calibration=te.calibration())\nrtapp.conf(\n    kind='profile',\n    params={\n        'task_p40_1': Periodic(\n            period_ms=10,          # period\n            duty_cycle_pct=40,     # duty cycle\n            duration_s=2,          # duration    \n            cpus=str(big_cpu)      # pinned on last big CPU\n        ).get(),\n        'task_p40_2': Periodic(\n            period_ms=10,          # period\n            duty_cycle_pct=10,     # duty cycle\n            duration_s=2,          # duration    \n            cpus=str(big_cpu)      # pinned on last big CPU\n        ).get(),\n    },\n    run_dir=target.working_directory\n)",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:18:58  INFO    : Setup new workload two_synched\n03:18:59  INFO    : Workload duration defined by longest task\n03:18:59  INFO    : Default policy: SCHED_OTHER\n03:18:59  INFO    : ------------------------\n03:18:59  INFO    : task [task_p40_1], sched: using default policy\n03:18:59  INFO    :  | loops count: 1\n03:18:59  INFO    : + phase_000001: duration 2.000000 [s] (200 loops)\n03:18:59  INFO    : |  period    10000 [us], duty_cycle  40 %\n03:18:59  INFO    : |  run_time   4000 [us], sleep_time   6000 [us]\n03:18:59  INFO    : |  CPUs affinity: 2\n03:18:59  INFO    : ------------------------\n03:18:59  INFO    : task [task_p40_2], sched: using default policy\n03:18:59  INFO    :  | loops count: 1\n03:18:59  INFO    : + phase_000001: duration 2.000000 [s] (200 loops)\n03:18:59  INFO    : |  period    10000 [us], duty_cycle  10 %\n03:18:59  INFO    : |  run_time   1000 [us], sleep_time   9000 [us]\n03:18:59  INFO    : |  CPUs affinity: 2\n",
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "execution_count": 49,
          "data": {
            "text/plain": "'two_synched_00'"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "tracefile, trace = run(rtapp, use_util_est=USE_UTIL_EST)\n# tracefile, trace = run(None, use_util_est=USE_UTIL_EST, trace_name=test_id)",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:19:07  INFO    : #### Set [schedutil] CPUFreq governor\n03:19:13  INFO    : #### Setup FTrace\n03:19:17  INFO    : #### Start RTApp execution\n03:19:17  INFO    : Workload execution START:\n03:19:17  INFO    :    /root/devlib-target/bin/rt-app /root/devlib-target/two_synched_00.json 2>&1\n03:19:20  INFO    : #### Stop FTrace\n03:19:21  INFO    : #### Save FTrace: /data/Code/lisa/results/20180605_RT-PELT_JunoR2/trace_two_synched.dat\n03:19:25  INFO    : #### Save platform description: /data/Code/lisa/results/20180605_RT-PELT_JunoR2/platform.json\n03:19:25  INFO    : #### Parse trace\n03:19:27  INFO    : Platform clusters verified to be Frequency coherent\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = trace.data_frame.trace_event('sched_util_est_task')\ndf[df.pid == trace.getTaskByName('task_p40_1')[0]][['util_avg', 'util_est_enqueued', 'util_est_ewma']].plot(figsize=(16,8), drawstyle='steps-post');\nlogging.info(\"Task Utilization\")",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:19:27  INFO    : Task Utilization\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Nmj9/Pjt27Gh1g9RWcnIya9asYffu3eh0Ou6//35WrVpFZGQkGRkZ1oZwUVERXl5evPPO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\n5+fT6szXYYHufLQrxVrfdfd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id6ndPWFO7kwZFUPsju0ABLs5c+v4CdY883LQ8eeJU4jd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MCStW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\ncO8PosIAAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f76a358b210>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df[df.pid == trace.getTaskByName('task_p40_1')[0]][1:].describe().T",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 55,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\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>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>__cpu</th>\n      <td>786.0</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>__line</th>\n      <td>786.0</td>\n      <td>10459.643766</td>\n      <td>2240.352728</td>\n      <td>6516.0</td>\n      <td>8531.5</td>\n      <td>10460.0</td>\n      <td>12380.0</td>\n      <td>14464.0</td>\n    </tr>\n    <tr>\n      <th>__pid</th>\n      <td>786.0</td>\n      <td>1606.734097</td>\n      <td>1304.393416</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2664.0</td>\n      <td>2664.0</td>\n      <td>2665.0</td>\n    </tr>\n    <tr>\n      <th>cpu</th>\n      <td>786.0</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>pid</th>\n      <td>786.0</td>\n      <td>2664.000000</td>\n      <td>0.000000</td>\n      <td>2664.0</td>\n      <td>2664.0</td>\n      <td>2664.0</td>\n      <td>2664.0</td>\n      <td>2664.0</td>\n    </tr>\n    <tr>\n      <th>running_avg</th>\n      <td>786.0</td>\n      <td>437.450382</td>\n      <td>19.456323</td>\n      <td>410.0</td>\n      <td>418.0</td>\n      <td>439.0</td>\n      <td>456.0</td>\n      <td>470.0</td>\n    </tr>\n    <tr>\n      <th>util_avg</th>\n      <td>786.0</td>\n      <td>427.944020</td>\n      <td>15.240139</td>\n      <td>409.0</td>\n      <td>421.0</td>\n      <td>421.0</td>\n      <td>441.0</td>\n      <td>456.0</td>\n    </tr>\n    <tr>\n      <th>util_est_enqueued</th>\n      <td>786.0</td>\n      <td>458.053435</td>\n      <td>8.044126</td>\n      <td>411.0</td>\n      <td>452.0</td>\n      <td>452.0</td>\n      <td>468.0</td>\n      <td>470.0</td>\n    </tr>\n    <tr>\n      <th>util_est_ewma</th>\n      <td>786.0</td>\n      <td>460.348601</td>\n      <td>0.780726</td>\n      <td>448.0</td>\n      <td>460.0</td>\n      <td>460.0</td>\n      <td>461.0</td>\n      <td>462.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                   count          mean          std     min     25%      50%  \\\n__cpu              786.0      2.000000     0.000000     2.0     2.0      2.0   \n__line             786.0  10459.643766  2240.352728  6516.0  8531.5  10460.0   \n__pid              786.0   1606.734097  1304.393416     0.0     0.0   2664.0   \ncpu                786.0      2.000000     0.000000     2.0     2.0      2.0   \npid                786.0   2664.000000     0.000000  2664.0  2664.0   2664.0   \nrunning_avg        786.0    437.450382    19.456323   410.0   418.0    439.0   \nutil_avg           786.0    427.944020    15.240139   409.0   421.0    421.0   \nutil_est_enqueued  786.0    458.053435     8.044126   411.0   452.0    452.0   \nutil_est_ewma      786.0    460.348601     0.780726   448.0   460.0    460.0   \n\n                       75%      max  \n__cpu                  2.0      2.0  \n__line             12380.0  14464.0  \n__pid               2664.0   2665.0  \ncpu                    2.0      2.0  \npid                 2664.0   2664.0  \nrunning_avg          456.0    470.0  \nutil_avg             441.0    456.0  \nutil_est_enqueued    468.0    470.0  \nutil_est_ewma        461.0    462.0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df[df.pid == trace.getTaskByName('task_p40_2')[0]][['util_avg', 'util_est_enqueued', 'util_est_ewma']].plot(figsize=(16,8), drawstyle='steps-post');\nlogging.info(\"Task Utilization\")",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": "03:19:27  INFO    : Task Utilization\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9xv3331+n/vr06cPMmTM/NW/06NGJUdOzzz6bO++8kyAIOOOMMxLLZGZmAhCJRBKPP25X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\nE1edxAcbd/HvU2rvYDHzRyezfls5E+5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i266qb2XCz14DYNkPZxIZ6mL4HXayvf0X52KM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            "text/plain": "<matplotlib.figure.Figure at 0x7f76a2d07550>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df[df.pid == trace.getTaskByName('task_p40_2')[0]][1:].describe().T",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 54,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\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>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>__cpu</th>\n      <td>487.0</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>__line</th>\n      <td>487.0</td>\n      <td>10484.607803</td>\n      <td>2255.762730</td>\n      <td>6509.0</td>\n      <td>8550.5</td>\n      <td>10479.0</td>\n      <td>12438.0</td>\n      <td>14396.0</td>\n    </tr>\n    <tr>\n      <th>__pid</th>\n      <td>487.0</td>\n      <td>935.759754</td>\n      <td>1273.374635</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2665.0</td>\n      <td>2665.0</td>\n    </tr>\n    <tr>\n      <th>cpu</th>\n      <td>487.0</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>pid</th>\n      <td>487.0</td>\n      <td>2665.000000</td>\n      <td>0.000000</td>\n      <td>2665.0</td>\n      <td>2665.0</td>\n      <td>2665.0</td>\n      <td>2665.0</td>\n      <td>2665.0</td>\n    </tr>\n    <tr>\n      <th>running_avg</th>\n      <td>487.0</td>\n      <td>109.121150</td>\n      <td>8.733815</td>\n      <td>96.0</td>\n      <td>98.0</td>\n      <td>115.0</td>\n      <td>116.0</td>\n      <td>120.0</td>\n    </tr>\n    <tr>\n      <th>util_avg</th>\n      <td>487.0</td>\n      <td>104.188912</td>\n      <td>8.342047</td>\n      <td>98.0</td>\n      <td>98.0</td>\n      <td>98.0</td>\n      <td>117.0</td>\n      <td>119.0</td>\n    </tr>\n    <tr>\n      <th>util_est_enqueued</th>\n      <td>487.0</td>\n      <td>114.000000</td>\n      <td>0.000000</td>\n      <td>114.0</td>\n      <td>114.0</td>\n      <td>114.0</td>\n      <td>114.0</td>\n      <td>114.0</td>\n    </tr>\n    <tr>\n      <th>util_est_ewma</th>\n      <td>487.0</td>\n      <td>122.000000</td>\n      <td>0.000000</td>\n      <td>122.0</td>\n      <td>122.0</td>\n      <td>122.0</td>\n      <td>122.0</td>\n      <td>122.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                   count          mean          std     min     25%      50%  \\\n__cpu              487.0      2.000000     0.000000     2.0     2.0      2.0   \n__line             487.0  10484.607803  2255.762730  6509.0  8550.5  10479.0   \n__pid              487.0    935.759754  1273.374635     0.0     0.0      0.0   \ncpu                487.0      2.000000     0.000000     2.0     2.0      2.0   \npid                487.0   2665.000000     0.000000  2665.0  2665.0   2665.0   \nrunning_avg        487.0    109.121150     8.733815    96.0    98.0    115.0   \nutil_avg           487.0    104.188912     8.342047    98.0    98.0     98.0   \nutil_est_enqueued  487.0    114.000000     0.000000   114.0   114.0    114.0   \nutil_est_ewma      487.0    122.000000     0.000000   122.0   122.0    122.0   \n\n                       75%      max  \n__cpu                  2.0      2.0  \n__line             12438.0  14396.0  \n__pid               2665.0   2665.0  \ncpu                    2.0      2.0  \npid                 2665.0   2665.0  \nrunning_avg          116.0    120.0  \nutil_avg             117.0    119.0  \nutil_est_enqueued    114.0    114.0  \nutil_est_ewma        122.0    122.0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Trace inspection"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "os.popen(\"kernelshark {}\".format(trace.ftrace.trace_path))",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 30,
          "data": {
            "text/plain": "<open file 'kernelshark /data/Code/lisa/results/20180605_RT-PELT_JunoR2/trace_two_synched.dat', mode 'r' at 0x7f0ae8871f60>"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2",
      "language": "python"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "name": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.12",
      "file_extension": ".py",
      "codemirror_mode": {
        "version": 2,
        "name": "ipython"
      }
    },
    "gist": {
      "id": "fe01e5ddf639e18b5d85a3b1d2e84b7d",
      "data": {
        "description": "20180531_UtilEst_RunningAvg",
        "public": false
      }
    },
    "toc": {
      "threshold": 4,
      "number_sections": true,
      "toc_cell": false,
      "toc_window_display": false,
      "toc_section_display": "block",
      "sideBar": true,
      "navigate_menu": true,
      "moveMenuLeft": true,
      "colors": {
        "hover_highlight": "#DAA520",
        "selected_highlight": "#FFD700",
        "running_highlight": "#FF0000"
      },
      "nav_menu": {
        "width": "252px",
        "height": "367px"
      }
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/fe01e5ddf639e18b5d85a3b1d2e84b7d"
    },
    "hide_input": false
  },
  "nbformat": 4,
  "nbformat_minor": 1
}