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android / platform / external / lisa / 58e57434aa253491f81868e2bb846245046836c4 / . / ipynb / examples / trace_analysis / TraceAnalysis_FunctionsProfiling.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Trace Analysis Examples\n",
"\n",
"## Kernel Functions Profiling\n",
"Details on functions profiling are given in **Plot Functions Profiling Data** below."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2016-12-12 12:54:48,228 INFO : root : Using LISA logging configuration:\n",
"2016-12-12 12:54:48,229 INFO : root : /home/vagrant/lisa/logging.conf\n"
]
}
],
"source": [
"import logging\n",
"from conf import LisaLogging\n",
"LisaLogging.setup()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Import required modules"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"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",
"from executor import Executor\n",
"\n",
"# RTApp configurator for generation of PERIODIC tasks\n",
"from wlgen import RTA, Ramp\n",
"\n",
"# Support for trace events analysis\n",
"from trace import Trace"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Target Configuration\n",
"The target configuration is used to describe and configure your test environment.\n",
"You can find more details in **examples/utils/testenv_example.ipynb**."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"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_FunctionsProfiling\",\n",
"\n",
" # Define devlib modules to load\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",
" \"functions\" : [\n",
" \"pick_next_task_fair\",\n",
" \"select_task_rq_fair\",\n",
" \"enqueue_task_fair\",\n",
" \"update_curr_fair\",\n",
" \"dequeue_task_fair\",\n",
" ],\n",
" \n",
" \"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",
"}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2016-12-07 13:11:43,327 INFO : TestEnv : Using base path: /home/vagrant/lisa\n",
"2016-12-07 13:11:43,328 INFO : TestEnv : Loading custom (inline) target configuration\n",
"2016-12-07 13:11:43,329 INFO : TestEnv : Devlib modules to load: ['bl', 'cpufreq']\n",
"2016-12-07 13:11:43,329 INFO : TestEnv : Connecting linux target:\n",
"2016-12-07 13:11:43,329 INFO : TestEnv : username : root\n",
"2016-12-07 13:11:43,330 INFO : TestEnv : host : 192.168.0.1\n",
"2016-12-07 13:11:43,330 INFO : TestEnv : password : juno\n",
"2016-12-07 13:11:43,331 INFO : TestEnv : Connection settings:\n",
"2016-12-07 13:11:43,331 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n",
"2016-12-07 13:11:50,441 INFO : TestEnv : Initializing target workdir:\n",
"2016-12-07 13:11:50,442 INFO : TestEnv : /root/devlib-target\n",
"2016-12-07 13:12:11,403 INFO : TestEnv : Topology:\n",
"2016-12-07 13:12:11,404 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n",
"2016-12-07 13:12:12,681 INFO : TestEnv : Loading default EM:\n",
"2016-12-07 13:12:12,682 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/juno.json\n",
"2016-12-07 13:12:18,266 INFO : TestEnv : Enabled tracepoints:\n",
"2016-12-07 13:12:18,267 INFO : TestEnv : sched:*\n",
"2016-12-07 13:12:18,267 INFO : TestEnv : Kernel functions profiled:\n",
"2016-12-07 13:12:18,267 INFO : TestEnv : pick_next_task_fair\n",
"2016-12-07 13:12:18,268 INFO : TestEnv : select_task_rq_fair\n",
"2016-12-07 13:12:18,268 INFO : TestEnv : enqueue_task_fair\n",
"2016-12-07 13:12:18,269 INFO : TestEnv : update_curr_fair\n",
"2016-12-07 13:12:18,269 INFO : TestEnv : dequeue_task_fair\n",
"2016-12-07 13:12:18,270 WARNING : TestEnv : Using configuration provided RTApp calibration\n",
"2016-12-07 13:12:18,270 INFO : TestEnv : Using RT-App calibration values:\n",
"2016-12-07 13:12:18,270 INFO : TestEnv : {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n",
"2016-12-07 13:12:18,272 INFO : EnergyMeter : HWMON module not enabled\n",
"2016-12-07 13:12:18,273 WARNING : EnergyMeter : Energy sampling disabled by configuration\n",
"2016-12-07 13:12:18,273 INFO : TestEnv : Set results folder to:\n",
"2016-12-07 13:12:18,274 INFO : TestEnv : /home/vagrant/lisa/results/TraceAnalysis_FunctionsProfiling\n",
"2016-12-07 13:12:18,274 INFO : TestEnv : Experiment results available also in:\n",
"2016-12-07 13:12:18,274 INFO : TestEnv : /home/vagrant/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": {},
"source": [
"## Workload Execution and Functions Profiling Data Collection\n",
"\n",
"Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"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)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2016-12-07 13:12:22,250 INFO : Workload : Setup new workload ramp\n",
"2016-12-07 13:12:22,254 INFO : Workload : Workload duration defined by longest task\n",
"2016-12-07 13:12:22,255 INFO : Workload : Default policy: SCHED_OTHER\n",
"2016-12-07 13:12:22,256 INFO : Workload : ------------------------\n",
"2016-12-07 13:12:22,256 INFO : Workload : task [ramp], sched: using default policy\n",
"2016-12-07 13:12:22,257 INFO : Workload : | calibration CPU: 1\n",
"2016-12-07 13:12:22,257 INFO : Workload : | loops count: 1\n",
"2016-12-07 13:12:22,258 INFO : Workload : + phase_000001: duration 0.500000 [s] (5 loops)\n",
"2016-12-07 13:12:22,258 INFO : Workload : | period 100000 [us], duty_cycle 60 %\n",
"2016-12-07 13:12:22,259 INFO : Workload : | run_time 60000 [us], sleep_time 40000 [us]\n",
"2016-12-07 13:12:22,259 INFO : Workload : + phase_000002: duration 0.500000 [s] (5 loops)\n",
"2016-12-07 13:12:22,260 INFO : Workload : | period 100000 [us], duty_cycle 55 %\n",
"2016-12-07 13:12:22,260 INFO : Workload : | run_time 55000 [us], sleep_time 45000 [us]\n",
"2016-12-07 13:12:22,261 INFO : Workload : + phase_000003: duration 0.500000 [s] (5 loops)\n",
"2016-12-07 13:12:22,262 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n",
"2016-12-07 13:12:22,263 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n",
"2016-12-07 13:12:22,263 INFO : Workload : + phase_000004: duration 0.500000 [s] (5 loops)\n",
"2016-12-07 13:12:22,264 INFO : Workload : | period 100000 [us], duty_cycle 45 %\n",
"2016-12-07 13:12:22,265 INFO : Workload : | run_time 45000 [us], sleep_time 55000 [us]\n",
"2016-12-07 13:12:22,265 INFO : Workload : + phase_000005: duration 0.500000 [s] (5 loops)\n",
"2016-12-07 13:12:22,266 INFO : Workload : | period 100000 [us], duty_cycle 40 %\n",
"2016-12-07 13:12:22,267 INFO : Workload : | run_time 40000 [us], sleep_time 60000 [us]\n",
"2016-12-07 13:12:22,267 INFO : Workload : + phase_000006: duration 0.500000 [s] (5 loops)\n",
"2016-12-07 13:12:22,268 INFO : Workload : | period 100000 [us], duty_cycle 35 %\n",
"2016-12-07 13:12:22,268 INFO : Workload : | run_time 35000 [us], sleep_time 65000 [us]\n",
"2016-12-07 13:12:22,269 INFO : Workload : + phase_000007: duration 0.500000 [s] (5 loops)\n",
"2016-12-07 13:12:22,269 INFO : Workload : | period 100000 [us], duty_cycle 30 %\n",
"2016-12-07 13:12:22,270 INFO : Workload : | run_time 30000 [us], sleep_time 70000 [us]\n",
"2016-12-07 13:12:22,270 INFO : Workload : + phase_000008: duration 0.500000 [s] (5 loops)\n",
"2016-12-07 13:12:22,271 INFO : Workload : | period 100000 [us], duty_cycle 25 %\n",
"2016-12-07 13:12:22,271 INFO : Workload : | run_time 25000 [us], sleep_time 75000 [us]\n",
"2016-12-07 13:12:22,271 INFO : Workload : + phase_000009: duration 0.500000 [s] (5 loops)\n",
"2016-12-07 13:12:22,272 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n",
"2016-12-07 13:12:22,272 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n",
"2016-12-07 13:12:35,923 INFO : Workload : Workload execution START:\n",
"2016-12-07 13:12:35,924 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n"
]
}
],
"source": [
"experiment(te)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parse Trace and Profiling Data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2016-12-07 13:13:03,632 INFO : root : Content of the output folder /home/vagrant/lisa/results/TraceAnalysis_FunctionsProfiling\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/vagrant/lisa/results/TraceAnalysis_FunctionsProfiling\r\n",
"├── output.log\r\n",
"├── platform.json\r\n",
"├── ramp_00.json\r\n",
"├── rt-app-ramp-0.log\r\n",
"├── trace.dat\r\n",
"├── trace.raw.txt\r\n",
"├── trace.stats\r\n",
"└── trace.txt\r\n",
"\r\n",
"0 directories, 8 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
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2016-12-07 13:13:07,030 INFO : root : LITTLE cluster max capacity: 447\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"nrg_model\": {\n",
" \"big\": {\n",
" \"cluster\": {\n",
" \"nrg_max\": 64\n",
" }, \n",
" \"cpu\": {\n",
" \"cap_max\": 1024, \n",
" \"nrg_max\": 616\n",
" }\n",
" }, \n",
" \"little\": {\n",
" \"cluster\": {\n",
" \"nrg_max\": 57\n",
" }, \n",
" \"cpu\": {\n",
" \"cap_max\": 447, \n",
" \"nrg_max\": 93\n",
" }\n",
" }\n",
" }, \n",
" \"clusters\": {\n",
" \"big\": [\n",
" 1, \n",
" 2\n",
" ], \n",
" \"little\": [\n",
" 0, \n",
" 3, \n",
" 4, \n",
" 5\n",
" ]\n",
" }, \n",
" \"cpus_count\": 6, \n",
" \"freqs\": {\n",
" \"big\": [\n",
" 450000, \n",
" 625000, \n",
" 800000, \n",
" 950000, \n",
" 1100000\n",
" ], \n",
" \"little\": [\n",
" 450000, \n",
" 575000, \n",
" 700000, \n",
" 775000, \n",
" 850000\n",
" ]\n",
" }, \n",
" \"topology\": [\n",
" [\n",
" 0, \n",
" 3, \n",
" 4, \n",
" 5\n",
" ], \n",
" [\n",
" 1, \n",
" 2\n",
" ]\n",
" ]\n",
"}\n"
]
}
],
"source": [
"with open(os.path.join(res_dir, 'platform.json'), 'r') as fh:\n",
" platform = json.load(fh)\n",
"print json.dumps(platform, indent=4)\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
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2016-12-07 13:13:08,084 INFO : Trace : Parsing FTrace format...\n",
"2016-12-07 13:13:08,456 INFO : Trace : Trace contains only functions stats\n",
"2016-12-07 13:13:08,457 INFO : Trace : Collected events spans a 0.000 [s] time interval\n",
"2016-12-07 13:13:08,457 INFO : Trace : Set plots time range to (0.000000, 0.000000)[s]\n",
"2016-12-07 13:13:08,461 INFO : Analysis : Registering trace analysis modules:\n",
"2016-12-07 13:13:08,465 INFO : Analysis : tasks\n",
"2016-12-07 13:13:08,468 INFO : Analysis : status\n",
"2016-12-07 13:13:08,471 INFO : Analysis : frequency\n",
"2016-12-07 13:13:08,473 INFO : Analysis : cpus\n",
"2016-12-07 13:13:08,476 INFO : Analysis : latency\n",
"2016-12-07 13:13:08,479 INFO : Analysis : idle\n",
"2016-12-07 13:13:08,481 INFO : Analysis : functions\n",
"2016-12-07 13:13:08,482 INFO : Analysis : eas\n"
]
}
],
"source": [
"trace = Trace(platform, res_dir, events=[])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Report Functions Profiling Data\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>hits</th>\n",
" <th>avg</th>\n",
" <th>time</th>\n",
" <th>s_2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">0</th>\n",
" <th>dequeue_task_fair</th>\n",
" <td>2064</td>\n",
" <td>9.994</td>\n",
" <td>20629.08</td>\n",
" <td>37.589</td>\n",
" </tr>\n",
" <tr>\n",
" <th>enqueue_task_fair</th>\n",
" <td>701</td>\n",
" <td>10.302</td>\n",
" <td>7221.72</td>\n",
" <td>21.210</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">1</th>\n",
" <th>dequeue_task_fair</th>\n",
" <td>570</td>\n",
" <td>3.857</td>\n",
" <td>2198.90</td>\n",
" <td>9.192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>enqueue_task_fair</th>\n",
" <td>208</td>\n",
" <td>6.415</td>\n",
" <td>1334.52</td>\n",
" <td>15.595</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2</th>\n",
" <th>dequeue_task_fair</th>\n",
" <td>148</td>\n",
" <td>8.643</td>\n",
" <td>1279.18</td>\n",
" <td>13.554</td>\n",
" </tr>\n",
" <tr>\n",
" <th>enqueue_task_fair</th>\n",
" <td>433</td>\n",
" <td>3.091</td>\n",
" <td>1338.60</td>\n",
" <td>2.320</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">3</th>\n",
" <th>dequeue_task_fair</th>\n",
" <td>171</td>\n",
" <td>12.253</td>\n",
" <td>2095.40</td>\n",
" <td>33.150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>enqueue_task_fair</th>\n",
" <td>45</td>\n",
" <td>8.536</td>\n",
" <td>384.14</td>\n",
" <td>16.124</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">4</th>\n",
" <th>dequeue_task_fair</th>\n",
" <td>536</td>\n",
" <td>6.805</td>\n",
" <td>3647.66</td>\n",
" <td>28.950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>enqueue_task_fair</th>\n",
" <td>88</td>\n",
" <td>4.474</td>\n",
" <td>393.74</td>\n",
" <td>8.697</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">5</th>\n",
" <th>dequeue_task_fair</th>\n",
" <td>139</td>\n",
" <td>6.097</td>\n",
" <td>847.56</td>\n",
" <td>25.569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>enqueue_task_fair</th>\n",
" <td>22</td>\n",
" <td>6.029</td>\n",
" <td>132.64</td>\n",
" <td>15.115</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" hits avg time s_2\n",
"0 dequeue_task_fair 2064 9.994 20629.08 37.589\n",
" enqueue_task_fair 701 10.302 7221.72 21.210\n",
"1 dequeue_task_fair 570 3.857 2198.90 9.192\n",
" enqueue_task_fair 208 6.415 1334.52 15.595\n",
"2 dequeue_task_fair 148 8.643 1279.18 13.554\n",
" enqueue_task_fair 433 3.091 1338.60 2.320\n",
"3 dequeue_task_fair 171 12.253 2095.40 33.150\n",
" enqueue_task_fair 45 8.536 384.14 16.124\n",
"4 dequeue_task_fair 536 6.805 3647.66 28.950\n",
" enqueue_task_fair 88 4.474 393.74 8.697\n",
"5 dequeue_task_fair 139 6.097 847.56 25.569\n",
" enqueue_task_fair 22 6.029 132.64 15.115"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the DataFrame for the specified list of kernel functions\n",
"df = trace.data_frame.functions_stats(['enqueue_task_fair', 'dequeue_task_fair'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>hits</th>\n",
" <th>avg</th>\n",
" <th>time</th>\n",
" <th>s_2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <th>select_task_rq_fair</th>\n",
" <td>714</td>\n",
" <td>4.641</td>\n",
" <td>3314.34</td>\n",
" <td>75.975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <th>select_task_rq_fair</th>\n",
" <td>270</td>\n",
" <td>11.346</td>\n",
" <td>3063.56</td>\n",
" <td>100.978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <th>select_task_rq_fair</th>\n",
" <td>456</td>\n",
" <td>4.223</td>\n",
" <td>1925.96</td>\n",
" <td>25.138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>select_task_rq_fair</th>\n",
" <td>49</td>\n",
" <td>13.006</td>\n",
" <td>637.32</td>\n",
" <td>89.897</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <th>select_task_rq_fair</th>\n",
" <td>96</td>\n",
" <td>7.731</td>\n",
" <td>742.18</td>\n",
" <td>83.133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <th>select_task_rq_fair</th>\n",
" <td>25</td>\n",
" <td>11.571</td>\n",
" <td>289.28</td>\n",
" <td>172.983</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" hits avg time s_2\n",
"0 select_task_rq_fair 714 4.641 3314.34 75.975\n",
"1 select_task_rq_fair 270 11.346 3063.56 100.978\n",
"2 select_task_rq_fair 456 4.223 1925.96 25.138\n",
"3 select_task_rq_fair 49 13.006 637.32 89.897\n",
"4 select_task_rq_fair 96 7.731 742.18 83.133\n",
"5 select_task_rq_fair 25 11.571 289.28 172.983"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the DataFrame for the single specified kernel function\n",
"df = trace.data_frame.functions_stats('select_task_rq_fair')\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot Functions Profiling Data\n",
"\n",
"The only method of the FunctionsAnalysis class that is used for functions profiling is **plotProfilingStats**. This method is used to plot functions profiling metrics for the specified kernel functions. For each speficied metric a barplot is generated which reports the value of the metric when the kernel function has been executed on each CPU.\n",
"The default metric is **avg** if not otherwise specified. A list of kernel functions to plot can also be passed to plotProfilingStats. Otherwise, by default, all the kernel functions are plotted."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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AS0Pt88rbMSsri86doycaS5IkSZIkSaouBsM6EY2AjLDn7YHzgN3AdmBPVP/DwE4gv1aq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\nzAQaAHOApsBaghnE/6rVKiVJkiRJkiR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54IIL2mxfvHhxTjvttGy7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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc3bf62fc90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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MS5IkSZIkSVKcsZWEJEmSJEmSToj8/Hz27NkT6zKkU0pqaioZGRnHPY/BsCRJkiRJkqpd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\nCDOG60dTOg7qh8VN6AAAAACAHiQYrh+3J5nUrm1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xbmYvA7Bx1MsnVXxKBQDobYJhAOrQ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\nC63dd999Oeuss/LII4/k9ttvz5tvvpnx48dn6dKlq/tcdNFFmTJlSi6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YsGGDXD1CQ0Px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\n0dHRGDNmjODAOHNzc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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc3bf636f50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot Average and Total execution time for the specified\n",
"# list of kernel functions\n",
"trace.analysis.functions.plotProfilingStats(\n",
" functions = [\n",
" 'select_task_rq_fair',\n",
" 'enqueue_task_fair',\n",
" 'dequeue_task_fair'\n",
" ],\n",
" metrics = [\n",
" # Average completion time per CPU\n",
" 'avg',\n",
" # Total execution time per CPU\n",
" 'time',\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc3bf73fcd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot Average execution time for the single specified kernel function\n",
"trace.analysis.functions.plotProfilingStats(\n",
" functions = 'update_curr_fair',\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
},
"toc": {
"toc_cell": false,
"toc_number_sections": true,
"toc_threshold": 6,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 0
}