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android / platform / external / lisa / ac819aec7461dce770b1b93709efb89a53b80059 / . / ipynb / examples / trace_analysis / TraceAnalysis_TasksLatencies.ipynb
blob: 27023db7eb77a59ce02f32471058105035241177 [file] [log] [blame]
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"source": [
"# Trace Analysis Examples\n",
"\n",
"## Tasks Latencies\n",
"\n",
"This notebook shows the features provided for task latency profiling. It will be necessary to collect the following events:\n",
" \n",
"Details on idle states profiling ar given in **Latency DataFrames and Latency Plots ** below."
]
},
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"text": [
"2017-02-17 19:51:33,920 INFO : root : Using LISA logging configuration:\n",
"2017-02-17 19:51:33,922 INFO : root : /data/Code/lisa/logging.conf\n"
]
}
],
"source": [
"import logging\n",
"from conf import LisaLogging\n",
"LisaLogging.setup()"
]
},
{
"cell_type": "code",
"execution_count": 2,
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"source": [
"# Generate plots inline\n",
"%matplotlib inline\n",
"\n",
"import json\n",
"import os\n",
"\n",
"# Support to access the remote target\n",
"import devlib\n",
"from env import TestEnv\n",
"\n",
"# Support for workload generation\n",
"from wlgen import RTA, Ramp\n",
"\n",
"# Support for trace analysis\n",
"from trace import Trace\n",
"\n",
"# Support for plotting\n",
"import numpy\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import trappy"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Target Configuration\n",
"The target configuration is used to describe and configure your test environment.\n",
"You can find more details in **examples/utils/testenv_example.ipynb**."
]
},
{
"cell_type": "code",
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"source": [
"# Setup target configuration\n",
"my_conf = {\n",
"\n",
" # Target platform and board\n",
" \"platform\" : 'linux',\n",
" \"board\" : 'juno',\n",
" \"host\" : '192.168.0.1',\n",
" \"password\" : 'juno',\n",
"\n",
" # Folder where all the results will be collected\n",
" \"results_dir\" : \"TraceAnalysis_TaskLatencies\",\n",
"\n",
" # Define devlib modules to load\n",
" \"modules\" : ['cpufreq'],\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",
" \"sched_switch\",\n",
" \"sched_wakeup\",\n",
" \"sched_load_avg_cpu\",\n",
" \"sched_load_avg_task\",\n",
" ],\n",
" \n",
" \"buffsize\" : 100 * 1024,\n",
" },\n",
"\n",
" # Tools required by the experiments\n",
" \"tools\" : [ 'trace-cmd', 'rt-app' ],\n",
" \n",
" # Comment this line to calibrate RTApp in your own platform\n",
" # \"rtapp-calib\" : {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353},\n",
"}"
]
},
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"execution_count": 4,
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"text": [
"2017-02-17 19:51:34,465 INFO : TestEnv : Using base path: /data/Code/lisa\n",
"2017-02-17 19:51:34,466 INFO : TestEnv : Loading custom (inline) target configuration\n",
"2017-02-17 19:51:34,467 INFO : TestEnv : Devlib modules to load: ['bl', 'cpufreq']\n",
"2017-02-17 19:51:34,468 INFO : TestEnv : Connecting linux target:\n",
"2017-02-17 19:51:34,469 INFO : TestEnv : username : root\n",
"2017-02-17 19:51:34,470 INFO : TestEnv : host : 192.168.0.1\n",
"2017-02-17 19:51:34,471 INFO : TestEnv : password : juno\n",
"2017-02-17 19:51:34,472 INFO : TestEnv : Connection settings:\n",
"2017-02-17 19:51:34,473 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n",
"2017-02-17 19:51:38,957 INFO : TestEnv : Initializing target workdir:\n",
"2017-02-17 19:51:38,959 INFO : TestEnv : /root/devlib-target\n",
"2017-02-17 19:51:41,908 INFO : TestEnv : Topology:\n",
"2017-02-17 19:51:41,910 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n",
"2017-02-17 19:51:43,175 INFO : TestEnv : Loading default EM:\n",
"2017-02-17 19:51:43,177 INFO : TestEnv : /data/Code/lisa/libs/utils/platforms/juno.json\n",
"2017-02-17 19:51:44,416 WARNING : LinuxTarget : Event [sched_load_avg_cpu] not available for tracing\n",
"2017-02-17 19:51:44,419 WARNING : LinuxTarget : Event [sched_load_avg_task] not available for tracing\n",
"2017-02-17 19:51:44,420 INFO : TestEnv : Enabled tracepoints:\n",
"2017-02-17 19:51:44,422 INFO : TestEnv : sched_switch\n",
"2017-02-17 19:51:44,423 INFO : TestEnv : sched_wakeup\n",
"2017-02-17 19:51:44,425 INFO : TestEnv : sched_load_avg_cpu\n",
"2017-02-17 19:51:44,426 INFO : TestEnv : sched_load_avg_task\n",
"2017-02-17 19:51:44,427 WARNING : TestEnv : Using configuration provided RTApp calibration\n",
"2017-02-17 19:51:44,429 INFO : TestEnv : Using RT-App calibration values:\n",
"2017-02-17 19:51:44,430 INFO : TestEnv : {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n",
"2017-02-17 19:51:44,432 INFO : EnergyMeter : HWMON module not enabled\n",
"2017-02-17 19:51:44,434 WARNING : EnergyMeter : Energy sampling disabled by configuration\n",
"2017-02-17 19:51:44,435 INFO : TestEnv : Set results folder to:\n",
"2017-02-17 19:51:44,436 INFO : TestEnv : /data/Code/lisa/results/TraceAnalysis_TaskLatencies\n",
"2017-02-17 19:51:44,438 INFO : TestEnv : Experiment results available also in:\n",
"2017-02-17 19:51:44,439 INFO : TestEnv : /data/Code/lisa/results_latest\n"
]
}
],
"source": [
"# Initialize a test environment using:\n",
"te = TestEnv(my_conf, wipe=False, force_new=True)\n",
"target = te.target"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Workload Configuration and Execution\n",
"\n",
"Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
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"source": [
"def experiment(te):\n",
"\n",
" # Create and 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 = 60,\n",
" end_pct = 20,\n",
" delta_pct = 5,\n",
" time_s = 0.5).get()\n",
" })\n",
"\n",
" # FTrace the execution of this workload\n",
" te.ftrace.start()\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",
" # Collect and keep track of the Kernel Functions performance data\n",
" stats_file = os.path.join(te.res_dir, 'trace.stats')\n",
" te.ftrace.get_stats(stats_file)\n",
"\n",
" # Dump platform descriptor\n",
" te.platform_dump(te.res_dir)"
]
},
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"name": "stderr",
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"text": [
"2017-02-17 19:51:44,484 INFO : Workload : Setup new workload ramp\n",
"2017-02-17 19:51:44,798 INFO : Workload : Workload duration defined by longest task\n",
"2017-02-17 19:51:44,800 INFO : Workload : Default policy: SCHED_OTHER\n",
"2017-02-17 19:51:44,801 INFO : Workload : ------------------------\n",
"2017-02-17 19:51:44,803 INFO : Workload : task [ramp], sched: using default policy\n",
"2017-02-17 19:51:44,804 INFO : Workload : | calibration CPU: 1\n",
"2017-02-17 19:51:44,806 INFO : Workload : | loops count: 1\n",
"2017-02-17 19:51:44,808 INFO : Workload : + phase_000001: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,809 INFO : Workload : | period 100000 [us], duty_cycle 60 %\n",
"2017-02-17 19:51:44,811 INFO : Workload : | run_time 60000 [us], sleep_time 40000 [us]\n",
"2017-02-17 19:51:44,812 INFO : Workload : + phase_000002: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,813 INFO : Workload : | period 100000 [us], duty_cycle 55 %\n",
"2017-02-17 19:51:44,815 INFO : Workload : | run_time 55000 [us], sleep_time 45000 [us]\n",
"2017-02-17 19:51:44,816 INFO : Workload : + phase_000003: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,817 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n",
"2017-02-17 19:51:44,818 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n",
"2017-02-17 19:51:44,820 INFO : Workload : + phase_000004: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,821 INFO : Workload : | period 100000 [us], duty_cycle 45 %\n",
"2017-02-17 19:51:44,822 INFO : Workload : | run_time 45000 [us], sleep_time 55000 [us]\n",
"2017-02-17 19:51:44,823 INFO : Workload : + phase_000005: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,824 INFO : Workload : | period 100000 [us], duty_cycle 40 %\n",
"2017-02-17 19:51:44,826 INFO : Workload : | run_time 40000 [us], sleep_time 60000 [us]\n",
"2017-02-17 19:51:44,827 INFO : Workload : + phase_000006: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,828 INFO : Workload : | period 100000 [us], duty_cycle 35 %\n",
"2017-02-17 19:51:44,829 INFO : Workload : | run_time 35000 [us], sleep_time 65000 [us]\n",
"2017-02-17 19:51:44,830 INFO : Workload : + phase_000007: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,831 INFO : Workload : | period 100000 [us], duty_cycle 30 %\n",
"2017-02-17 19:51:44,832 INFO : Workload : | run_time 30000 [us], sleep_time 70000 [us]\n",
"2017-02-17 19:51:44,833 INFO : Workload : + phase_000008: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,834 INFO : Workload : | period 100000 [us], duty_cycle 25 %\n",
"2017-02-17 19:51:44,836 INFO : Workload : | run_time 25000 [us], sleep_time 75000 [us]\n",
"2017-02-17 19:51:44,837 INFO : Workload : + phase_000009: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,838 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n",
"2017-02-17 19:51:44,839 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n",
"2017-02-17 19:51:50,397 INFO : Workload : Workload execution START:\n",
"2017-02-17 19:51:50,399 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n"
]
}
],
"source": [
"experiment(te)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
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},
"source": [
"## Parse Trace and Profiling Data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:51:59,998 INFO : root : Content of the output folder /data/Code/lisa/results/TraceAnalysis_TaskLatencies\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[01;34m/data/Code/lisa/results/TraceAnalysis_TaskLatencies\u001b[00m\r\n",
"├── output.log\r\n",
"├── platform.json\r\n",
"├── ramp_00.json\r\n",
"├── rt-app-ramp-0.log\r\n",
"├── \u001b[01;35mtask_activations_5019_5019__ramp,_rt-app.png\u001b[00m\r\n",
"├── \u001b[01;35mtask_activations_5083_5083__ramp,_rt-app.png\u001b[00m\r\n",
"├── \u001b[01;35mtask_latencies_5019_5019__ramp,_rt-app.png\u001b[00m\r\n",
"├── \u001b[01;35mtask_latencies_5083_5083__ramp,_rt-app.png\u001b[00m\r\n",
"├── \u001b[01;35mtask_runtimes_5019_5019__ramp,_rt-app.png\u001b[00m\r\n",
"├── \u001b[01;35mtask_runtimes_5083_5083__ramp,_rt-app.png\u001b[00m\r\n",
"├── trace.dat\r\n",
"├── trace.raw.txt\r\n",
"└── trace.txt\r\n",
"\r\n",
"0 directories, 13 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": 8,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"marked": false,
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:52:00,120 INFO : root : LITTLE cluster max capacity: 447\n"
]
}
],
"source": [
"with open(os.path.join(res_dir, 'platform.json'), 'r') as fh:\n",
" platform = json.load(fh)\n",
"logging.info('LITTLE cluster max capacity: %d',\n",
" platform['nrg_model']['little']['cpu']['cap_max'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"marked": false,
"read_only": false
}
},
"outputs": [],
"source": [
"trace_file = os.path.join(res_dir, 'trace.dat')\n",
"trace = Trace(platform, trace_file, events=my_conf['ftrace']['events'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Trace visualization"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"marked": false,
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"outputs": [
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" * http://www.apache.org/licenses/LICENSE-2.0\n",
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" * distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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" }); /* TRAPPY_PUBLISH_REMOVE_LINE */\n",
" </script>\n",
" </div>"
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"<IPython.core.display.HTML object>"
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},
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],
"source": [
"trappy.plotter.plot_trace(trace.ftrace)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# Latency Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Latency DataFrames"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" DataFrame of task's wakeup/suspend events\n",
"\n",
" The returned DataFrame has these columns\n",
" - Time: the time an event related to this task happened\n",
" - target_cpu: the CPU where the task has been scheduled\n",
" reported only for wakeup events\n",
" - curr_state: the current task state:\n",
" A letter which corresponds to the standard events reported by the\n",
" prev_state field of a sched_switch event.\n",
" Only exception is 'A', which is used to represent active tasks,\n",
" i.e. tasks RUNNING on a CPU\n",
" - next_state: the next status for the task\n",
" - t_start: the time when the current status started, it matches Time\n",
" - t_delta: the interval of time after witch the task will switch to the\n",
" next_state\n",
"\n",
" :param task: the task to report wakeup latencies for\n",
" :type task: int or str\n",
" \n"
]
}
],
"source": [
"print trace.data_frame.latency_df.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
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" <th>__cpu</th>\n",
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" <th>next_state</th>\n",
" <th>t_start</th>\n",
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" <tr>\n",
" <th>Time</th>\n",
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" </tr>\n",
" <tr>\n",
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" <tr>\n",
" <th>2.494329</th>\n",
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"text/plain": [
" target_cpu __cpu curr_state next_state t_start t_delta\n",
"Time \n",
"1.778588 NaN 1.0 A R 1.778588 0.000433\n",
"1.779021 NaN 1.0 R A 1.779021 0.000135\n",
"1.779156 NaN 1.0 A R 1.779156 0.715133\n",
"2.494289 NaN 1.0 R A 2.494289 0.000040\n",
"2.494329 NaN 1.0 A R 2.494329 0.001559"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Report full set of task status informations available from the trace\n",
"trace.data_frame.latency_df('ramp').head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
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" <th>next_prio</th>\n",
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" <td>kworker/u12:1</td>\n",
" <td>4563</td>\n",
" <td>120</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" __comm __cpu __pid next_comm next_pid next_prio \\\n",
"Time \n",
"0.000013 trace-cmd 1 5137 sudo 5136 120 \n",
"0.001866 sudo 1 5136 swapper/1 0 120 \n",
"0.001998 <idle> 0 0 sh 5096 120 \n",
"0.002457 sh 0 5096 kworker/u12:1 4563 120 \n",
"0.002526 kworker/u12:1 0 4563 sshd 4428 120 \n",
"\n",
" prev_comm prev_pid prev_prio prev_state \n",
"Time \n",
"0.000013 trace-cmd 5137 120 64 \n",
"0.001866 sudo 5136 120 64 \n",
"0.001998 swapper/0 0 120 0 \n",
"0.002457 sh 5096 120 4096 \n",
"0.002526 kworker/u12:1 4563 120 1 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Report information on sched_switch events\n",
"df = trace.data_frame.trace_event('sched_switch')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" DataFrame of task's wakeup latencies\n",
"\n",
" The returned DataFrame has these columns:\n",
" - Time: the time the task wakeups\n",
" - wakeup_latency: the time the task waited before getting a CPU\n",
"\n",
" :param task: the task to report wakeup latencies for\n",
" :type task: int or str\n",
" \n"
]
}
],
"source": [
"print trace.data_frame.latency_wakeup_df.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": 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>wakeup_latency</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2.578911</th>\n",
" <td>0.000244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.678908</th>\n",
" <td>0.000022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.778907</th>\n",
" <td>0.000020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.878907</th>\n",
" <td>0.000021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.978903</th>\n",
" <td>0.000021</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" wakeup_latency\n",
"Time \n",
"2.578911 0.000244\n",
"2.678908 0.000022\n",
"2.778907 0.000020\n",
"2.878907 0.000021\n",
"2.978903 0.000021"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Report WAKEUP events and their duration\n",
"trace.data_frame.latency_wakeup_df('ramp').head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" DataFrame of task's preemption latencies\n",
"\n",
" The returned DataFrame has these columns:\n",
" - Time: the time the has been preempted\n",
" - preemption_latency: the time the task waited before getting again a CPU\n",
"\n",
" :param task: the task to report wakeup latencies for\n",
" :type task: int or str\n",
" \n"
]
}
],
"source": [
"print trace.data_frame.latency_preemption_df.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": 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>preempt_latency</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.779021</th>\n",
" <td>0.000135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.494289</th>\n",
" <td>0.000040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.495888</th>\n",
" <td>0.000014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.496282</th>\n",
" <td>0.000009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.506273</th>\n",
" <td>0.000014</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" preempt_latency\n",
"Time \n",
"1.779021 0.000135\n",
"2.494289 0.000040\n",
"2.495888 0.000014\n",
"2.496282 0.000009\n",
"2.506273 0.000014"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Report PREEMPTION events and their duration\n",
"trace.data_frame.latency_preemption_df('ramp').head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Latency Plots"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Generate a set of plots to report the WAKEUP and PREEMPT latencies the\n",
" specified task has been subject to. A WAKEUP latencies is the time from\n",
" when a task becomes RUNNABLE till the first time it gets a CPU.\n",
" A PREEMPT latencies is the time from when a RUNNABLE task is suspended\n",
" because of the CPU is assigned to another task till when the task\n",
" enters the CPU again.\n",
"\n",
" :param task: the task to report latencies for\n",
" :type task: int or list(str)\n",
"\n",
" :param kind: the kind of latencies to report (WAKEUP and/or PREEMPT\")\n",
" :type kind: str\n",
"\n",
" :param tag: a string to add to the plot title\n",
" :type tag: str\n",
"\n",
" :param threshold_ms: the minimum acceptable [ms] value to report\n",
" graphically in the generated plots\n",
" :type threshold_ms: int or float\n",
" \n"
]
}
],
"source": [
"print trace.analysis.latency.plotLatency.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:52:01,228 INFO : Analysis : Found: 38 WAKEUP latencies\n",
"2017-02-17 19:52:01,265 INFO : Analysis : Found: 14 PREEMPT latencies\n",
"2017-02-17 19:52:01,267 INFO : Analysis : Total: 52 latency events\n",
"2017-02-17 19:52:01,269 INFO : Analysis : 100.0 % samples below 1 [ms] threshold\n",
"2017-02-17 19:52:01,399 WARNING : Analysis : Event [sched_overutilized] not found, plot DISABLED!\n"
]
},
{
"data": {
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v4SEAAAAA0Et4CAAAAAD0Eh4CAAAAAL2EhwAAAABAL+EhAAAAANBLeAgAAAAA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\nF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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0b10f87ad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot latency events for a specified task\n",
"latency_stats_df = trace.analysis.latency.plotLatency('ramp')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>50%</th>\n",
" <th>95%</th>\n",
" <th>99%</th>\n",
" <th>max</th>\n",
" <th>100.0%</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>latency</th>\n",
" <td>52.0</td>\n",
" <td>0.000027</td>\n",
" <td>0.000035</td>\n",
" <td>0.000009</td>\n",
" <td>0.00002</td>\n",
" <td>0.000036</td>\n",
" <td>0.000188</td>\n",
" <td>0.000244</td>\n",
" <td>0.001</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 50% 95% 99% \\\n",
"latency 52.0 0.000027 0.000035 0.000009 0.00002 0.000036 0.000188 \n",
"\n",
" max 100.0% \n",
"latency 0.000244 0.001 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plot statistics on task latencies\n",
"latency_stats_df.T"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Draw a plot that shows intervals of time when the execution of a\n",
" RUNNABLE task has been delayed. The plot reports:\n",
" WAKEUP lantecies as RED colored bands\n",
" PREEMPTION lantecies as BLUE colored bands\n",
"\n",
" The optional axes parameter allows to plot the signal on an existing\n",
" graph.\n",
"\n",
" :param task: the task to report latencies for\n",
" :type task: str\n",
"\n",
" :param axes: axes on which to plot the signal\n",
" :type axes: :mod:`matplotlib.axes.Axes`\n",
" \n"
]
}
],
"source": [
"print trace.analysis.latency.plotLatencyBands.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0ad5e955d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot latency events for a specified task\n",
"trace.analysis.latency.plotLatencyBands('ramp')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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SJEmSJEnSgCYnJxkbGwMYy8x5+9bAnomSJEmSJEmSajGZKEmSJEmSJKkWk4mS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\nSGpW53PE/8vM1dUXBksoSYo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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0b20272e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Zoom into a spefific time frame\n",
"trace.setXTimeRange(4.28,4.29)\n",
"trace.analysis.latency.plotLatencyBands('ramp')"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# Activations Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Activations DataFrames"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" DataFrame of task's wakeup intrvals\n",
"\n",
" The returned DataFrame has these columns:\n",
" - Time: the wakeup time for the task\n",
" - activation_interval: the time since the previous wakeup events\n",
"\n",
" :param task: the task to report runtimes for\n",
" :type task: int or str\n",
" \n"
]
}
],
"source": [
"print trace.data_frame.activations_df.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": 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>activation_interval</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2.578911</th>\n",
" <td>0.099997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.678908</th>\n",
" <td>0.099999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.778907</th>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.878907</th>\n",
" <td>0.099996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.978903</th>\n",
" <td>0.100001</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" activation_interval\n",
"Time \n",
"2.578911 0.099997\n",
"2.678908 0.099999\n",
"2.778907 0.100000\n",
"2.878907 0.099996\n",
"2.978903 0.100001"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Report the sequence of activations intervals:\n",
"# Time: wakeup time\n",
"# activation_internal: time interval wrt previous wakeup\n",
"trace.data_frame.activations_df('ramp').head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Activations Plots"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Plots \"activation intervals\" for the specified task\n",
"\n",
" An \"activation interval\" is time incurring between two consecutive\n",
" wakeups of a task. A set of plots is generated to report:\n",
" - Activations interval at wakeup time: every time a task wakeups a\n",
" point is plotted to represent the time interval since the previous\n",
" wakeup.\n",
" - Activations interval cumulative function: reports the cumulative\n",
" function of the activation intervals.\n",
" - Activations interval histogram: reports a 64 bins histogram of\n",
" the activation iternals.\n",
"\n",
" All plots are parameterized based on the value of threshold_ms, which\n",
" can be used to filter activations intervals bigger than 2 times this\n",
" value.\n",
" Such a threshold is useful to filter out from the plots outliers thus\n",
" focusing the analysis in the most critical periodicity under analysis.\n",
" The number and percentage of discarded samples is reported in output.\n",
" A default threshold of 16 [ms] is used, which is useful for example\n",
" to analyze a 60Hz rendering pipelines.\n",
"\n",
" A PNG of the generated plots is generated and saved in the same folder\n",
" where the trace is.\n",
"\n",
" :param task: the task to report latencies for\n",
" :type task: int or list(str)\n",
"\n",
" :param tag: a string to add to the plot title\n",
" :type tag: str\n",
"\n",
" :param threshold_ms: the minimum acceptable [ms] value to report\n",
" graphically in the generated plots\n",
" :type threshold_ms: int or float\n",
"\n",
" :returns: a DataFrame with statistics on ploted activation intervals\n",
" \n"
]
}
],
"source": [
"print trace.analysis.latency.plotActivations.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:52:03,201 INFO : Analysis : Found: 38 activations for [5144: ramp, rt-app]\n",
"2017-02-17 19:52:03,203 WARNING : Analysis : Discarding 1 activation intervals (above 2 x threshold_ms, 2.6% of the overall activations)\n",
"2017-02-17 19:52:03,205 INFO : Analysis : 100.0 % samples below 120 [ms] threshold\n",
"2017-02-17 19:52:03,258 WARNING : Analysis : Event [sched_overutilized] not found, plot DISABLED!\n"
]
},
{
"data": {
"image/png": 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PWm38O8q8HBjzEkcyP7GIY2vzPJDsjjIvn8+8xJHMTyzz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\nafcKgHEUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0b118ef590>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot activation internvals for a specified task\n",
"activations_df = trace.analysis.latency.plotActivations('ramp', threshold_ms=120)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>50%</th>\n",
" <th>95%</th>\n",
" <th>99%</th>\n",
" <th>max</th>\n",
" <th>100.0%</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>activation_interval</th>\n",
" <td>37.0</td>\n",
" <td>0.100002</td>\n",
" <td>0.000012</td>\n",
" <td>0.099966</td>\n",
" <td>0.1</td>\n",
" <td>0.100028</td>\n",
" <td>0.100039</td>\n",
" <td>0.10004</td>\n",
" <td>0.12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 50% 95% \\\n",
"activation_interval 37.0 0.100002 0.000012 0.099966 0.1 0.100028 \n",
"\n",
" 99% max 100.0% \n",
"activation_interval 0.100039 0.10004 0.12 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plot statistics on task activation intervals\n",
"activations_df.T"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# Runtimes Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Runtimes DataFrames"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" DataFrame of task's runtime each time the task blocks\n",
"\n",
" The returned DataFrame has these columns:\n",
" - Time: the time the task completed an activation (i.e. sleep or exit)\n",
" - running_time: the time the task spent RUNNING since its last wakeup\n",
"\n",
" :param task: the task to report runtimes for\n",
" :type task: int or str\n",
" \n"
]
}
],
"source": [
"print trace.data_frame.runtimes_df.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": 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>running_time</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2.506287</th>\n",
" <td>0.790959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.579155</th>\n",
" <td>0.059508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.678930</th>\n",
" <td>0.059534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.778927</th>\n",
" <td>0.054048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.898286</th>\n",
" <td>0.054230</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" running_time\n",
"Time \n",
"2.506287 0.790959\n",
"2.579155 0.059508\n",
"2.678930 0.059534\n",
"2.778927 0.054048\n",
"2.898286 0.054230"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Report the sequence of running times:\n",
"# Time: task block time (i.e. sleep or exit)\n",
"# running_time: cumulative ruinning times since last wakeup event\n",
"trace.data_frame.runtimes_df('ramp').head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Runtimes Plots"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Plots \"running times\" for the specified task\n",
"\n",
" A \"running time\" is the sum of all the time intervals a task executed\n",
" in between a wakeup and the next sleep (or exit).\n",
" A set of plots is generated to report:\n",
" - Running times at block time: every time a task blocks a\n",
" point is plotted to represent the cumulative time the task has be\n",
" running since its last wakeup\n",
" - Running time cumulative function: reports the cumulative\n",
" function of the running times.\n",
" - Running times histogram: reports a 64 bins histogram of\n",
" the running times.\n",
"\n",
" All plots are parameterized based on the value of threshold_ms, which\n",
" can be used to filter running times bigger than 2 times this value.\n",
" Such a threshold is useful to filter out from the plots outliers thus\n",
" focusing the analysis in the most critical periodicity under analysis.\n",
" The number and percentage of discarded samples is reported in output.\n",
" A default threshold of 16 [ms] is used, which is useful for example to\n",
" analyze a 60Hz rendering pipelines.\n",
"\n",
" A PNG of the generated plots is generated and saved in the same folder\n",
" where the trace is.\n",
"\n",
" :param task: the task to report latencies for\n",
" :type task: int or list(str)\n",
"\n",
" :param tag: a string to add to the plot title\n",
" :type tag: str\n",
"\n",
" :param threshold_ms: the minimum acceptable [ms] value to report\n",
" graphically in the generated plots\n",
" :type threshold_ms: int or float\n",
"\n",
" :returns: a DataFrame with statistics on ploted running times\n",
" \n"
]
}
],
"source": [
"print trace.analysis.latency.plotRuntimes.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:52:04,119 INFO : Analysis : Found: 39 activations for [5144: ramp, rt-app]\n",
"2017-02-17 19:52:04,121 WARNING : Analysis : Discarding 1 running times (above 2 x threshold_ms, 2.6% of the overall activations)\n",
"2017-02-17 19:52:04,123 INFO : Analysis : 100.0 % samples below 120 [ms] threshold\n",
"2017-02-17 19:52:04,172 WARNING : Analysis : Event [sched_overutilized] not found, plot DISABLED!\n"
]
},
{
"data": {
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rLb9WOfXSfb0e8lEPeYD6KC/q5VyYdXY9ap0BMzMzMzPr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NUPA0RJytlDtxzN//7U77J4xQ7e/oWrWbyiq+iSJKmevBk4bYL2q4GfAwaIkiRJ05TbQ1Qi4oMR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\nJEmSJEmSNCkDREmSJEmSJEmT+j8ikTTvVpkXhgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0b10df61d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot activation internvals for a specified task\n",
"runtimes_df = trace.analysis.latency.plotRuntimes('ramp', threshold_ms=120)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>50%</th>\n",
" <th>95%</th>\n",
" <th>99%</th>\n",
" <th>max</th>\n",
" <th>100.0%</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>running_time</th>\n",
" <td>38.0</td>\n",
" <td>0.036271</td>\n",
" <td>0.012981</td>\n",
" <td>0.000277</td>\n",
" <td>0.0326</td>\n",
" <td>0.055088</td>\n",
" <td>0.059524</td>\n",
" <td>0.059534</td>\n",
" <td>0.12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 50% 95% 99% \\\n",
"running_time 38.0 0.036271 0.012981 0.000277 0.0326 0.055088 0.059524 \n",
"\n",
" max 100.0% \n",
"running_time 0.059534 0.12 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plot statistics on task running times\n",
"runtimes_df.T"
]
}
],
"metadata": {
"_draft": {
"nbviewer_url": "https://gist.github.com/ec38b4edb2da1ef21e2aa9b1d6c64f65"
},
"gist": {
"data": {
"description": "TraceAnalysis_TasksLatencies.ipynb",
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