examples: restructure trace analysis notebooks
Add details and re-run the trace analysis notebooks.
Add a new plot for the idle states profiling where new
information was necessary (description of idle states).
Signed-off-by: Ionela Voinescu <ionela.voinescu@arm.com>
diff --git a/ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb b/ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb
index eb819d5..65ea258 100644
--- a/ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb
+++ b/ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb
@@ -4,27 +4,28 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<font size=\"8\">Trace Analysis Examples</font>\n",
- "<br>\n",
- "<font size=\"5\">Kernel Functions Profiling</font>\n",
- "<br>\n",
- "<hr>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Import Required Modules"
+ "# 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": true
+ "collapsed": false
},
- "outputs": [],
+ "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",
@@ -32,20 +33,19 @@
]
},
{
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Import required modules"
+ ]
+ },
+ {
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Populating the interactive namespace from numpy and matplotlib\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Generate plots inline\n",
"%matplotlib inline\n",
@@ -56,6 +56,7 @@
"# 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",
@@ -68,7 +69,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Target Configuration"
+ "## 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**."
]
},
{
@@ -86,6 +89,7 @@
" \"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",
@@ -127,38 +131,38 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "03:35:25 INFO : Target - Using base path: /home/derkling/Code/lisa\n",
- "03:35:25 INFO : Target - Loading custom (inline) target configuration\n",
- "03:35:25 INFO : Target - Devlib modules to load: ['bl', 'cpufreq']\n",
- "03:35:25 INFO : Target - Connecting linux target:\n",
- "03:35:25 INFO : Target - username : root\n",
- "03:35:25 INFO : Target - host : 192.168.0.1\n",
- "03:35:25 INFO : Target - password : \n",
- "03:35:25 INFO : Target - Connection settings:\n",
- "03:35:25 INFO : Target - {'username': 'root', 'host': '192.168.0.1', 'password': ''}\n",
- "03:35:29 INFO : Target - Initializing target workdir:\n",
- "03:35:29 INFO : Target - /root/devlib-target\n",
- "03:35:34 INFO : Target - Topology:\n",
- "03:35:34 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n",
- "03:35:35 INFO : Platform - Loading default EM:\n",
- "03:35:35 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/juno.json\n",
- "03:35:38 INFO : FTrace - Enabled tracepoints:\n",
- "03:35:38 INFO : FTrace - sched:*\n",
- "03:35:38 INFO : FTrace - Kernel functions profiled:\n",
- "03:35:38 INFO : FTrace - pick_next_task_fair\n",
- "03:35:38 INFO : FTrace - select_task_rq_fair\n",
- "03:35:38 INFO : FTrace - enqueue_task_fair\n",
- "03:35:38 INFO : FTrace - update_curr_fair\n",
- "03:35:38 INFO : FTrace - dequeue_task_fair\n",
- "03:35:38 WARNING : Target - Using configuration provided RTApp calibration\n",
- "03:35:38 INFO : Target - Using RT-App calibration values:\n",
- "03:35:38 INFO : Target - {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n",
- "03:35:38 INFO : EnergyMeter - HWMON module not enabled\n",
- "03:35:38 WARNING : EnergyMeter - Energy sampling disabled by configuration\n",
- "03:35:38 INFO : TestEnv - Set results folder to:\n",
- "03:35:38 INFO : TestEnv - /home/derkling/Code/lisa/results/TraceAnalysis_FunctionsProfiling\n",
- "03:35:38 INFO : TestEnv - Experiment results available also in:\n",
- "03:35:38 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n"
+ "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"
]
}
],
@@ -172,7 +176,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Workload Execution and Functions Profiling Data Collection"
+ "## Workload Execution and Functions Profiling Data Collection\n",
+ "\n",
+ "Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**."
]
},
{
@@ -224,42 +230,42 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "03:35:38 INFO : WlGen - Setup new workload ramp\n",
- "03:35:38 INFO : RTApp - Workload duration defined by longest task\n",
- "03:35:38 INFO : RTApp - Default policy: SCHED_OTHER\n",
- "03:35:38 INFO : RTApp - ------------------------\n",
- "03:35:38 INFO : RTApp - task [ramp], sched: using default policy\n",
- "03:35:38 INFO : RTApp - | calibration CPU: 1\n",
- "03:35:38 INFO : RTApp - | loops count: 1\n",
- "03:35:38 INFO : RTApp - + phase_000001: duration 0.500000 [s] (5 loops)\n",
- "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 60 %\n",
- "03:35:38 INFO : RTApp - | run_time 60000 [us], sleep_time 40000 [us]\n",
- "03:35:38 INFO : RTApp - + phase_000002: duration 0.500000 [s] (5 loops)\n",
- "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 55 %\n",
- "03:35:38 INFO : RTApp - | run_time 55000 [us], sleep_time 45000 [us]\n",
- "03:35:38 INFO : RTApp - + phase_000003: duration 0.500000 [s] (5 loops)\n",
- "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 50 %\n",
- "03:35:38 INFO : RTApp - | run_time 50000 [us], sleep_time 50000 [us]\n",
- "03:35:38 INFO : RTApp - + phase_000004: duration 0.500000 [s] (5 loops)\n",
- "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n",
- "03:35:38 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n",
- "03:35:38 INFO : RTApp - + phase_000005: duration 0.500000 [s] (5 loops)\n",
- "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 40 %\n",
- "03:35:38 INFO : RTApp - | run_time 40000 [us], sleep_time 60000 [us]\n",
- "03:35:38 INFO : RTApp - + phase_000006: duration 0.500000 [s] (5 loops)\n",
- "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 35 %\n",
- "03:35:38 INFO : RTApp - | run_time 35000 [us], sleep_time 65000 [us]\n",
- "03:35:38 INFO : RTApp - + phase_000007: duration 0.500000 [s] (5 loops)\n",
- "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 30 %\n",
- "03:35:38 INFO : RTApp - | run_time 30000 [us], sleep_time 70000 [us]\n",
- "03:35:38 INFO : RTApp - + phase_000008: duration 0.500000 [s] (5 loops)\n",
- "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n",
- "03:35:38 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n",
- "03:35:38 INFO : RTApp - + phase_000009: duration 0.500000 [s] (5 loops)\n",
- "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n",
- "03:35:38 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n",
- "03:35:44 INFO : WlGen - Workload execution START:\n",
- "03:35:44 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n"
+ "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"
]
}
],
@@ -271,7 +277,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Parse Trace and Profiling Data"
+ "## Parse Trace and Profiling Data"
]
},
{
@@ -285,14 +291,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "03:35:53 INFO : Content of the output folder /home/derkling/Code/lisa/results/TraceAnalysis_FunctionsProfiling\n"
+ "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": [
- "\u001b[01;34m/home/derkling/Code/lisa/results/TraceAnalysis_FunctionsProfiling\u001b[00m\r\n",
+ "/home/vagrant/lisa/results/TraceAnalysis_FunctionsProfiling\r\n",
"├── output.log\r\n",
"├── platform.json\r\n",
"├── ramp_00.json\r\n",
@@ -324,14 +330,83 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "03:35:53 INFO : LITTLE cluster max capacity: 447\n"
+ "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",
+ "print json.dumps(platform, indent=4)\n",
"logging.info('LITTLE cluster max capacity: %d',\n",
" platform['nrg_model']['little']['cpu']['cap_max'])"
]
@@ -347,17 +422,19 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "03:21:10 INFO : Parsing FTrace format...\n",
- "03:21:10 INFO : Trace contains only functions stats\n",
- "03:21:10 INFO : Collected events spans a 0.000 [s] time interval\n",
- "03:21:10 INFO : Set plots time range to (0.000000, 0.000000)[s]\n",
- "03:21:10 INFO : Registering trace analysis modules:\n",
- "03:21:10 INFO : frequency\n",
- "03:21:10 INFO : functions\n",
- "03:21:10 INFO : tasks\n",
- "03:21:10 INFO : eas\n",
- "03:21:10 INFO : status\n",
- "03:21:10 INFO : cpus\n"
+ "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"
]
}
],
@@ -369,7 +446,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Report Functions Profiling Data"
+ "## Report Functions Profiling Data\n",
+ "\n"
]
},
{
@@ -398,114 +476,114 @@
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">0</th>\n",
" <th>dequeue_task_fair</th>\n",
- " <td>538</td>\n",
- " <td>3.372</td>\n",
- " <td>1814.54</td>\n",
- " <td>5.544</td>\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>571</td>\n",
- " <td>3.214</td>\n",
- " <td>1835.56</td>\n",
- " <td>2.027</td>\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>12</td>\n",
- " <td>3.501</td>\n",
- " <td>42.02</td>\n",
- " <td>1.593</td>\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>17</td>\n",
- " <td>3.076</td>\n",
- " <td>52.30</td>\n",
- " <td>0.593</td>\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>1160</td>\n",
- " <td>2.469</td>\n",
- " <td>2864.78</td>\n",
- " <td>2.218</td>\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>1164</td>\n",
- " <td>2.177</td>\n",
- " <td>2535.18</td>\n",
- " <td>0.999</td>\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>304</td>\n",
- " <td>2.362</td>\n",
- " <td>718.34</td>\n",
- " <td>3.015</td>\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>279</td>\n",
- " <td>2.538</td>\n",
- " <td>708.18</td>\n",
- " <td>1.082</td>\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>199</td>\n",
- " <td>2.646</td>\n",
- " <td>526.58</td>\n",
- " <td>2.827</td>\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>215</td>\n",
- " <td>2.571</td>\n",
- " <td>552.78</td>\n",
- " <td>0.903</td>\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>88</td>\n",
- " <td>2.407</td>\n",
- " <td>211.82</td>\n",
- " <td>2.773</td>\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>59</td>\n",
- " <td>2.774</td>\n",
- " <td>163.70</td>\n",
- " <td>1.491</td>\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 538 3.372 1814.54 5.544\n",
- " enqueue_task_fair 571 3.214 1835.56 2.027\n",
- "1 dequeue_task_fair 12 3.501 42.02 1.593\n",
- " enqueue_task_fair 17 3.076 52.30 0.593\n",
- "2 dequeue_task_fair 1160 2.469 2864.78 2.218\n",
- " enqueue_task_fair 1164 2.177 2535.18 0.999\n",
- "3 dequeue_task_fair 304 2.362 718.34 3.015\n",
- " enqueue_task_fair 279 2.538 708.18 1.082\n",
- "4 dequeue_task_fair 199 2.646 526.58 2.827\n",
- " enqueue_task_fair 215 2.571 552.78 0.903\n",
- "5 dequeue_task_fair 88 2.407 211.82 2.773\n",
- " enqueue_task_fair 59 2.774 163.70 1.491"
+ " 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": 9,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -518,7 +596,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {
"collapsed": false
},
@@ -542,66 +620,66 @@
" <tr>\n",
" <th>0</th>\n",
" <th>select_task_rq_fair</th>\n",
- " <td>783</td>\n",
- " <td>1.617</td>\n",
- " <td>1266.88</td>\n",
- " <td>0.896</td>\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>17</td>\n",
- " <td>0.782</td>\n",
- " <td>13.30</td>\n",
- " <td>0.031</td>\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>777</td>\n",
- " <td>1.042</td>\n",
- " <td>810.16</td>\n",
- " <td>3.248</td>\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>259</td>\n",
- " <td>1.575</td>\n",
- " <td>408.12</td>\n",
- " <td>8.924</td>\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>186</td>\n",
- " <td>1.837</td>\n",
- " <td>341.72</td>\n",
- " <td>4.420</td>\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>51</td>\n",
- " <td>2.557</td>\n",
- " <td>130.42</td>\n",
- " <td>13.227</td>\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 783 1.617 1266.88 0.896\n",
- "1 select_task_rq_fair 17 0.782 13.30 0.031\n",
- "2 select_task_rq_fair 777 1.042 810.16 3.248\n",
- "3 select_task_rq_fair 259 1.575 408.12 8.924\n",
- "4 select_task_rq_fair 186 1.837 341.72 4.420\n",
- "5 select_task_rq_fair 51 2.557 130.42 13.227"
+ " 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": 10,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -616,7 +694,10 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Plot Functions Profiling Data"
+ "## 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."
]
},
{
@@ -628,9 +709,9 @@
"outputs": [
{
"data": {
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IiIiIiIjIjDDRJyIiIiIiIjIjTPSJiIiIiIiIzAgTfSKi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\nTBw8eBDz58+Xy1Z/YEdWVhZWrlyJU6dO1fNtkLnIzc3FlStXEBgYCG9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+ "image/png": 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AS0Pt88rbMSsri86doycaS5IkSZIkSaouBsM6EY2AjLDn7YHzgN3AdmBPVP/DwE4gv1aq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\nzAQaAHOApsBaghnE/6rVKiVJkiRJkiR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54IIL2mxfvHhxTjvttGy7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\nz8eoUaO49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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f5361b61150>"
+ "<matplotlib.figure.Figure at 0x7fc3bf62fc90>"
]
},
"metadata": {},
@@ -638,9 +719,9 @@
},
{
"data": {
- "image/png": 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/NAzlHzt2LJMnT244juacdNJJfOhDH6JLly507dp1q/psqvVLHJIkSZKkD5Q9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+ "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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\nQmCC\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f5361b61b10>"
+ "<matplotlib.figure.Figure at 0x7fc3bf636f50>"
]
},
"metadata": {},
@@ -657,7 +738,9 @@
" 'dequeue_task_fair'\n",
" ],\n",
" metrics = [\n",
+ " # Average completion time per CPU\n",
" 'avg',\n",
+ " # Total execution time per CPU\n",
" 'time',\n",
" ]\n",
")"
@@ -665,16 +748,16 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
- "image/png": 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uof0PZtDmwo1eBQAAAEzBnPZNhDnt0A1z2gEYL+a0QxfMaQcAAIBNkNAOAAAA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\neuMa2ry7qq6qqsur6sCR/dd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vG/18kydPTp8+fVJaWppu3bpl1KhRSerfCmL1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\nCKsq5jbxAAAAAElFTkSuQmCC\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f5361b34d50>"
+ "<matplotlib.figure.Figure at 0x7fc3bf73fcd0>"
]
},
"metadata": {},
@@ -687,15 +770,6 @@
" functions = 'update_curr_fair',\n",
")"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/ipynb/examples/trace_analysis/TraceAnalysis_IdleStates.ipynb b/ipynb/examples/trace_analysis/TraceAnalysis_IdleStates.ipynb
index f1aa6b0..eaf5af3 100644
--- a/ipynb/examples/trace_analysis/TraceAnalysis_IdleStates.ipynb
+++ b/ipynb/examples/trace_analysis/TraceAnalysis_IdleStates.ipynb
@@ -6,7 +6,9 @@
"collapsed": true
},
"source": [
- "# Idle States Residency Analysis"
+ "# Trace Analysis Examples\n",
+ "\n",
+ "## Idle States Residency Analysis"
]
},
{
@@ -16,7 +18,9 @@
"This notebook shows the features provided by the idle state analysis module. It will be necessary to collect the following events:\n",
"\n",
" - `cpu_idle`, to filter out intervals of time in which the CPU is idle\n",
- " - `sched_switch`, to recognise tasks on kernelshark"
+ " - `sched_switch`, to recognise tasks on kernelshark\n",
+ " \n",
+ "Details on idle states profiling ar given in **Per-CPU/Per-Cluster Idle State Residency Profiling** below."
]
},
{
@@ -28,7 +32,16 @@
"marked": false
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2016-12-07 18:19:51,925 INFO : root : Using LISA logging configuration:\n",
+ "2016-12-07 18:19:51,925 INFO : root : /home/vagrant/lisa/logging.conf\n"
+ ]
+ }
+ ],
"source": [
"import logging\n",
"from conf import LisaLogging\n",
@@ -53,25 +66,28 @@
"# Support to access the remote target\n",
"from env import TestEnv\n",
"\n",
+ "# Support to access cpuidle information from the target\n",
+ "from devlib import *\n",
+ "\n",
"# Support to configure and run RTApp based workloads\n",
"from wlgen import RTA, Ramp\n",
"\n",
"# Support for trace events analysis\n",
- "from trace import Trace"
+ "from trace import Trace\n",
+ "\n",
+ "# DataFrame support\n",
+ "from pandas import DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Target Configuration"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Our target is a Juno R2 development board running Linux."
+ "## Target Configuration\n",
+ "The target configuration is used to describe and configure your test environment.\n",
+ "You can find more details in **examples/utils/testenv_example.ipynb**.\n",
+ "\n",
+ "Our target is a Juno R0 development board running Linux."
]
},
{
@@ -97,7 +113,7 @@
" \n",
" # Login credentials\n",
" \"username\" : 'root',\n",
- " \"password\" : '',\n",
+ " \"password\" : 'juno',\n",
" \n",
" \"results_dir\" : \"IdleAnalysis\",\n",
" \n",
@@ -108,7 +124,7 @@
" \n",
" # Tools required by the experiments\n",
" \"tools\" : ['rt-app', 'trace-cmd'],\n",
- " \"modules\" : ['bl', 'cpufreq'],\n",
+ " \"modules\" : ['bl', 'cpufreq', 'cpuidle'],\n",
" \"exclude_modules\" : ['hwmon'],\n",
" \n",
" # FTrace events to collect for all the tests configuration which have\n",
@@ -124,13 +140,6 @@
]
},
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Tests execution"
- ]
- },
- {
"cell_type": "code",
"execution_count": 4,
"metadata": {
@@ -145,33 +154,33 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2016-09-02 09:25:41,477 INFO : Target - Using base path: /data/lisa\n",
- "2016-09-02 09:25:41,478 INFO : Target - Loading custom (inline) target configuration\n",
- "2016-09-02 09:25:41,479 INFO : Target - Devlib modules to load: ['bl', 'cpufreq']\n",
- "2016-09-02 09:25:41,480 INFO : Target - Connecting linux target:\n",
- "2016-09-02 09:25:41,481 INFO : Target - username : root\n",
- "2016-09-02 09:25:41,482 INFO : Target - host : 192.168.0.1\n",
- "2016-09-02 09:25:41,482 INFO : Target - password : \n",
- "2016-09-02 09:25:41,483 INFO : Target - Connection settings:\n",
- "2016-09-02 09:25:41,484 INFO : Target - {'username': 'root', 'host': '192.168.0.1', 'password': ''}\n",
- "2016-09-02 09:25:45,648 INFO : Target - Initializing target workdir:\n",
- "2016-09-02 09:25:45,650 INFO : Target - /root/devlib-target\n",
- "2016-09-02 09:25:51,095 INFO : Target - Topology:\n",
- "2016-09-02 09:25:51,097 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n",
- "2016-09-02 09:25:52,729 INFO : Platform - Loading default EM:\n",
- "2016-09-02 09:25:52,730 INFO : Platform - /data/lisa/libs/utils/platforms/juno.json\n",
- "2016-09-02 09:25:58,291 INFO : FTrace - Enabled tracepoints:\n",
- "2016-09-02 09:25:58,293 INFO : FTrace - cpu_idle\n",
- "2016-09-02 09:25:58,294 INFO : FTrace - sched_switch\n",
- "2016-09-02 09:25:58,295 WARNING : Target - Using configuration provided RTApp calibration\n",
- "2016-09-02 09:25:58,296 INFO : Target - Using RT-App calibration values:\n",
- "2016-09-02 09:25:58,298 INFO : Target - {\"0\": 318, \"1\": 125, \"2\": 124, \"3\": 318, \"4\": 318, \"5\": 319}\n",
- "2016-09-02 09:25:58,299 INFO : EnergyMeter - HWMON module not enabled\n",
- "2016-09-02 09:25:58,300 WARNING : EnergyMeter - Energy sampling disabled by configuration\n",
- "2016-09-02 09:25:58,301 INFO : TestEnv - Set results folder to:\n",
- "2016-09-02 09:25:58,302 INFO : TestEnv - /data/lisa/results/IdleAnalysis\n",
- "2016-09-02 09:25:58,303 INFO : TestEnv - Experiment results available also in:\n",
- "2016-09-02 09:25:58,304 INFO : TestEnv - /data/lisa/results_latest\n"
+ "2016-12-07 18:19:55,077 INFO : TestEnv : Using base path: /home/vagrant/lisa\n",
+ "2016-12-07 18:19:55,078 INFO : TestEnv : Loading custom (inline) target configuration\n",
+ "2016-12-07 18:19:55,078 INFO : TestEnv : Devlib modules to load: ['bl', 'cpuidle', 'cpufreq']\n",
+ "2016-12-07 18:19:55,079 INFO : TestEnv : Connecting linux target:\n",
+ "2016-12-07 18:19:55,079 INFO : TestEnv : username : root\n",
+ "2016-12-07 18:19:55,080 INFO : TestEnv : host : 192.168.0.1\n",
+ "2016-12-07 18:19:55,080 INFO : TestEnv : password : juno\n",
+ "2016-12-07 18:19:55,081 INFO : TestEnv : Connection settings:\n",
+ "2016-12-07 18:19:55,081 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n",
+ "2016-12-07 18:20:04,495 INFO : TestEnv : Initializing target workdir:\n",
+ "2016-12-07 18:20:04,497 INFO : TestEnv : /root/devlib-target\n",
+ "2016-12-07 18:20:28,575 INFO : TestEnv : Topology:\n",
+ "2016-12-07 18:20:28,576 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n",
+ "2016-12-07 18:20:30,241 INFO : TestEnv : Loading default EM:\n",
+ "2016-12-07 18:20:30,243 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/juno.json\n",
+ "2016-12-07 18:20:34,565 INFO : TestEnv : Enabled tracepoints:\n",
+ "2016-12-07 18:20:34,565 INFO : TestEnv : cpu_idle\n",
+ "2016-12-07 18:20:34,566 INFO : TestEnv : sched_switch\n",
+ "2016-12-07 18:20:34,566 WARNING : TestEnv : Using configuration provided RTApp calibration\n",
+ "2016-12-07 18:20:34,566 INFO : TestEnv : Using RT-App calibration values:\n",
+ "2016-12-07 18:20:34,567 INFO : TestEnv : {\"0\": 318, \"1\": 125, \"2\": 124, \"3\": 318, \"4\": 318, \"5\": 319}\n",
+ "2016-12-07 18:20:34,568 INFO : EnergyMeter : HWMON module not enabled\n",
+ "2016-12-07 18:20:34,570 WARNING : EnergyMeter : Energy sampling disabled by configuration\n",
+ "2016-12-07 18:20:34,571 INFO : TestEnv : Set results folder to:\n",
+ "2016-12-07 18:20:34,572 INFO : TestEnv : /home/vagrant/lisa/results/IdleAnalysis\n",
+ "2016-12-07 18:20:34,574 INFO : TestEnv : Experiment results available also in:\n",
+ "2016-12-07 18:20:34,576 INFO : TestEnv : /home/vagrant/lisa/results_latest\n"
]
}
],
@@ -185,7 +194,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Workload configuration and execution"
+ "## Workload configuration and execution\n",
+ "\n",
+ "Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**."
]
},
{
@@ -238,42 +249,49 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2016-09-02 09:25:58,330 INFO : WlGen - Setup new workload ramp\n",
- "2016-09-02 09:25:58,331 INFO : RTApp - Workload duration defined by longest task\n",
- "2016-09-02 09:25:58,332 INFO : RTApp - Default policy: SCHED_OTHER\n",
- "2016-09-02 09:25:58,333 INFO : RTApp - ------------------------\n",
- "2016-09-02 09:25:58,334 INFO : RTApp - task [ramp], sched: using default policy\n",
- "2016-09-02 09:25:58,335 INFO : RTApp - | calibration CPU: 1\n",
- "2016-09-02 09:25:58,336 INFO : RTApp - | loops count: 1\n",
- "2016-09-02 09:25:58,336 INFO : RTApp - + phase_000001: duration 0.500000 [s] (5 loops)\n",
- "2016-09-02 09:25:58,337 INFO : RTApp - | period 100000 [us], duty_cycle 60 %\n",
- "2016-09-02 09:25:58,338 INFO : RTApp - | run_time 60000 [us], sleep_time 40000 [us]\n",
- "2016-09-02 09:25:58,339 INFO : RTApp - + phase_000002: duration 0.500000 [s] (5 loops)\n",
- "2016-09-02 09:25:58,339 INFO : RTApp - | period 100000 [us], duty_cycle 55 %\n",
- "2016-09-02 09:25:58,340 INFO : RTApp - | run_time 55000 [us], sleep_time 45000 [us]\n",
- "2016-09-02 09:25:58,341 INFO : RTApp - + phase_000003: duration 0.500000 [s] (5 loops)\n",
- "2016-09-02 09:25:58,342 INFO : RTApp - | period 100000 [us], duty_cycle 50 %\n",
- "2016-09-02 09:25:58,343 INFO : RTApp - | run_time 50000 [us], sleep_time 50000 [us]\n",
- "2016-09-02 09:25:58,344 INFO : RTApp - + phase_000004: duration 0.500000 [s] (5 loops)\n",
- "2016-09-02 09:25:58,345 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n",
- "2016-09-02 09:25:58,346 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n",
- "2016-09-02 09:25:58,347 INFO : RTApp - + phase_000005: duration 0.500000 [s] (5 loops)\n",
- "2016-09-02 09:25:58,347 INFO : RTApp - | period 100000 [us], duty_cycle 40 %\n",
- "2016-09-02 09:25:58,348 INFO : RTApp - | run_time 40000 [us], sleep_time 60000 [us]\n",
- "2016-09-02 09:25:58,349 INFO : RTApp - + phase_000006: duration 0.500000 [s] (5 loops)\n",
- "2016-09-02 09:25:58,350 INFO : RTApp - | period 100000 [us], duty_cycle 35 %\n",
- "2016-09-02 09:25:58,351 INFO : RTApp - | run_time 35000 [us], sleep_time 65000 [us]\n",
- "2016-09-02 09:25:58,352 INFO : RTApp - + phase_000007: duration 0.500000 [s] (5 loops)\n",
- "2016-09-02 09:25:58,352 INFO : RTApp - | period 100000 [us], duty_cycle 30 %\n",
- "2016-09-02 09:25:58,353 INFO : RTApp - | run_time 30000 [us], sleep_time 70000 [us]\n",
- "2016-09-02 09:25:58,353 INFO : RTApp - + phase_000008: duration 0.500000 [s] (5 loops)\n",
- "2016-09-02 09:25:58,354 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n",
- "2016-09-02 09:25:58,355 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n",
- "2016-09-02 09:25:58,356 INFO : RTApp - + phase_000009: duration 0.500000 [s] (5 loops)\n",
- "2016-09-02 09:25:58,356 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n",
- "2016-09-02 09:25:58,357 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n",
- "2016-09-02 09:26:03,072 INFO : WlGen - Workload execution START:\n",
- "2016-09-02 09:26:03,074 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n"
+ "2016-12-07 18:20:39,906 INFO : Workload : Setup new workload ramp\n",
+ "2016-12-07 18:20:39,907 INFO : Workload : Workload duration defined by longest task\n",
+ "2016-12-07 18:20:39,908 INFO : Workload : Default policy: SCHED_OTHER\n",
+ "2016-12-07 18:20:39,908 INFO : Workload : ------------------------\n",
+ "2016-12-07 18:20:39,908 INFO : Workload : task [ramp], sched: using default policy\n",
+ "2016-12-07 18:20:39,909 INFO : Workload : | calibration CPU: 1\n",
+ "2016-12-07 18:20:39,909 INFO : Workload : | loops count: 1\n",
+ "2016-12-07 18:20:39,909 INFO : Workload : + phase_000001: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-07 18:20:39,910 INFO : Workload : | period 100000 [us], duty_cycle 60 %\n",
+ "2016-12-07 18:20:39,910 INFO : Workload : | run_time 60000 [us], sleep_time 40000 [us]\n",
+ "2016-12-07 18:20:39,910 INFO : Workload : + phase_000002: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-07 18:20:39,911 INFO : Workload : | period 100000 [us], duty_cycle 55 %\n",
+ "2016-12-07 18:20:39,911 INFO : Workload : | run_time 55000 [us], sleep_time 45000 [us]\n",
+ "2016-12-07 18:20:39,912 INFO : Workload : + phase_000003: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-07 18:20:39,912 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n",
+ "2016-12-07 18:20:39,913 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n",
+ "2016-12-07 18:20:39,913 INFO : Workload : + phase_000004: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-07 18:20:39,914 INFO : Workload : | period 100000 [us], duty_cycle 45 %\n",
+ "2016-12-07 18:20:39,914 INFO : Workload : | run_time 45000 [us], sleep_time 55000 [us]\n",
+ "2016-12-07 18:20:39,916 INFO : Workload : + phase_000005: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-07 18:20:39,916 INFO : Workload : | period 100000 [us], duty_cycle 40 %\n",
+ "2016-12-07 18:20:39,917 INFO : Workload : | run_time 40000 [us], sleep_time 60000 [us]\n",
+ "2016-12-07 18:20:39,918 INFO : Workload : + phase_000006: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-07 18:20:39,919 INFO : Workload : | period 100000 [us], duty_cycle 35 %\n",
+ "2016-12-07 18:20:39,919 INFO : Workload : | run_time 35000 [us], sleep_time 65000 [us]\n",
+ "2016-12-07 18:20:39,920 INFO : Workload : + phase_000007: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-07 18:20:39,920 INFO : Workload : | period 100000 [us], duty_cycle 30 %\n",
+ "2016-12-07 18:20:39,921 INFO : Workload : | run_time 30000 [us], sleep_time 70000 [us]\n",
+ "2016-12-07 18:20:39,921 INFO : Workload : + phase_000008: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-07 18:20:39,922 INFO : Workload : | period 100000 [us], duty_cycle 25 %\n",
+ "2016-12-07 18:20:39,923 INFO : Workload : | run_time 25000 [us], sleep_time 75000 [us]\n",
+ "2016-12-07 18:20:39,923 INFO : Workload : + phase_000009: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-07 18:20:39,924 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n",
+ "2016-12-07 18:20:39,924 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n",
+ "2016-12-07 18:20:51,246 INFO : Workload : Workload execution START:\n",
+ "2016-12-07 18:20:51,247 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
]
}
],
@@ -285,7 +303,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Parse trace and analyse data"
+ "## Parse trace and analyse data"
]
},
{
@@ -299,16 +317,16 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2016-09-02 09:26:11,282 INFO : Content of the output folder /data/lisa/results/IdleAnalysis\n"
+ "2016-12-07 18:21:31,546 INFO : root : Content of the output folder /home/vagrant/lisa/results/IdleAnalysis\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[01;34m/data/lisa/results/IdleAnalysis\u001b[00m\r\n",
- "├── \u001b[01;35mcluster_idle_state_residency.png\u001b[00m\r\n",
- "├── \u001b[01;35mcpu_idle_state_residency.png\u001b[00m\r\n",
+ "/home/vagrant/lisa/results/IdleAnalysis\r\n",
+ "├── cluster_idle_state_residency.png\r\n",
+ "├── cpu_idle_state_residency.png\r\n",
"├── output.log\r\n",
"├── platform.json\r\n",
"├── ramp_00.json\r\n",
@@ -342,18 +360,19 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2016-09-02 09:26:11,404 INFO : Parsing FTrace format...\n",
- "2016-09-02 09:26:11,766 INFO : Collected events spans a 6.927 [s] time interval\n",
- "2016-09-02 09:26:11,767 INFO : Set plots time range to (0.000000, 6.927481)[s]\n",
- "2016-09-02 09:26:11,768 INFO : Registering trace analysis modules:\n",
- "2016-09-02 09:26:11,772 INFO : frequency\n",
- "2016-09-02 09:26:11,773 INFO : functions\n",
- "2016-09-02 09:26:11,774 INFO : tasks\n",
- "2016-09-02 09:26:11,776 INFO : latency\n",
- "2016-09-02 09:26:11,777 INFO : eas\n",
- "2016-09-02 09:26:11,780 INFO : idle\n",
- "2016-09-02 09:26:11,780 INFO : status\n",
- "2016-09-02 09:26:11,781 INFO : cpus\n"
+ "2016-12-07 18:21:32,410 INFO : Trace : Parsing FTrace format...\n",
+ "2016-12-07 18:21:32,664 WARNING : Trace : Failed to load tasks names from trace events\n",
+ "2016-12-07 18:21:32,665 INFO : Trace : Collected events spans a 14.858 [s] time interval\n",
+ "2016-12-07 18:21:32,665 INFO : Trace : Set plots time range to (0.000000, 14.857782)[s]\n",
+ "2016-12-07 18:21:32,666 INFO : Analysis : Registering trace analysis modules:\n",
+ "2016-12-07 18:21:32,667 INFO : Analysis : tasks\n",
+ "2016-12-07 18:21:32,668 INFO : Analysis : status\n",
+ "2016-12-07 18:21:32,669 INFO : Analysis : frequency\n",
+ "2016-12-07 18:21:32,670 INFO : Analysis : cpus\n",
+ "2016-12-07 18:21:32,671 INFO : Analysis : latency\n",
+ "2016-12-07 18:21:32,673 INFO : Analysis : idle\n",
+ "2016-12-07 18:21:32,674 INFO : Analysis : functions\n",
+ "2016-12-07 18:21:32,675 INFO : Analysis : eas\n"
]
}
],
@@ -365,7 +384,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Per-CPU Idle State Residency"
+ "## Per-CPU Idle State Residency Profiling"
]
},
{
@@ -377,12 +396,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {
- "collapsed": false,
- "run_control": {
- "marked": false
- }
+ "collapsed": false
},
"outputs": [
{
@@ -403,26 +419,94 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>0.000000</td>\n",
+ " <td>0.009255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>0.014078</td>\n",
+ " <td>0.003381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>5.671125</td>\n",
+ " <td>14.483522</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " time\n",
- "idle_state \n",
- "0 0.000000\n",
- "1 0.014078\n",
- "2 5.671125"
+ " time\n",
+ "idle_state \n",
+ "0 0.009255\n",
+ "1 0.003381\n",
+ "2 14.483522"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Idle state residency for CPU 3\n",
+ "CPU=3\n",
+ "state_res = trace.data_frame.cpu_idle_state_residency(CPU)\n",
+ "state_res"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For the translation between the idle value and its description:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false,
+ "run_control": {
+ "marked": false
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>name</th>\n",
+ " <th>value</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>CpuidleState(WFI, ARM64 WFI)</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>CpuidleState(cpu-sleep-0, cpu-sleep-0)</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>CpuidleState(cluster-sleep-0, cluster-sleep-0)</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " name value\n",
+ "0 CpuidleState(WFI, ARM64 WFI) 0\n",
+ "1 CpuidleState(cpu-sleep-0, cpu-sleep-0) 1\n",
+ "2 CpuidleState(cluster-sleep-0, cluster-sleep-0) 2"
]
},
"execution_count": 10,
@@ -431,15 +515,15 @@
}
],
"source": [
- "# Idle state residency for CPU 3\n",
- "trace.data_frame.cpu_idle_state_residency(3)"
+ "DataFrame(data={'value': state_res.index.values,\n",
+ " 'name': [te.target.cpuidle.get_state(i, cpu=CPU) for i in state_res.index.values]})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The `IdleAnalysis` module provide methods for plotting residency data."
+ "The **IdleAnalysis** module provide methods for plotting residency data:"
]
},
{
@@ -462,9 +546,9 @@
"outputs": [
{
"data": {
- "image/png": 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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f68ab79c2d0>"
+ "<matplotlib.figure.Figure at 0x7fe5e2a1f990>"
]
},
"metadata": {},
@@ -488,9 +572,9 @@
"outputs": [
{
"data": {
- "image/png": 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gJ5OhA0APJkMHgDmhGAMAADBqijEskOnQAaAH06EDQE+mQweAHkyHDgBzQjEG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\n766FqvrFqnp3VV1aVWfN1h0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3k1z15E/vTPLTqR4qOuj2JGfXrP32HHqPfpKM9ZbdNen9dakOctBX\nestO7gr/cZ76oOf9vWXvmvT+5iTn19R2XhxH4jgGHU3H0V9nsR9Hn+OY4DgqR/Nx3DTp/cV6HKX8\nPhyH4xg01+O4O2UcRym/D8cxwXFMmO1x9D9b7MfR5zgmOI4JpR/HilQzkp898Lq0Zl/zZaZNxa6c\nmOShJJcl+YNpltuZ5N8n+UCq3/E3Jn3+O0kuSvLCSe8vS/IPSV6a5G8nfbY+1TNBXzrFPkeTbEs2\n9X5ck1RvZHSaQoHF4ewkf9l1EUAr5BvKJd9QLvmGMt2UfkctK1L1UefNQh/puSfJbakaysfXfP6j\nA9/vStXAnNzwTKqG+E+kmt190MuSHEjy5TlXChTlmq4LAFoj31Au+YZyyTfQ1EJveiZVw/NpST6X\n5Nwky5OMpLqv/NO9ZQ43YvW2VLO9b07yM0lekGoU5x8m+dMkjw4se0qSl6RqkB6X5Kd6Pz997ocC\nLBZLuy4AaI18Q7nkG8ol30BTx3ZdwAzsTHXH+GWpHp75vFS3vP9tqtvWk+TgYbbxRJKVSa5INXL2\npCQPJLk+1bM/B12f5JUD27279/UFqR5BAAAAAAAsYIuh6ZlUt6y/o/eq84IZbOObSd46g+VePdOi\nAAAAAICFZzHc3g5wxE2eaQ4oh3xDueQbyiXfQFOangA19nddANAa+YZyyTeUS76BpjQ9AWps6LoA\noDXyDeWSbyiXfANNaXoCAAAAAEXR9AQAAAAAiqLpCVBjV9cFAK2RbyiXfEO55BtoStMToMYFXRcA\ntEa+oVzyDeWSb6ApTU+AGuu7LgBozfquCwBas77rAoDWrO+6AGDR0fQEqDHadQFAa+QbyiXfUC75\nBprS9AQAAAAAiqLpCQAAAAAURdMToMaNXRcAtEa+oVzyDeWSb6ApTU+AGmNdFwC0Rr6hXPIN5ZJv\noClNT4Aa13ZdANAa+YZyyTeUS76BpjQ9AQAAAICiaHoCAAAAAEXR9AQAAAAAiqLpCVDj7K4LAFoj\n31Au+YZyyTfQlKYnQI2Lui4AaI18Q7nkG8ol30BTmp4ANVZ2XQDQGvmGcsk3lEu+gaY0PQEAAACA\nomh6AgAAAABF0fQEqLG16wKA1sg3lEu+oVzyDTSl6QlQY3PXBQCtkW8ol3xDueQbaErTE6DGh7su\nAGiNfEO55BvKJd9AU5qeAAAAAEBRND0BAAAAgKJoegIAAAAARdH0BKhxftcFAK2RbyiXfEO55Bto\nStMToMbKrgsAWiPfUC75hnLJN9CUpidAjdVdFwC0Rr6hXPIN5ZJvoClNTwAAAACgKJqeAAAAAEBR\nND0BatzVdQFAa+QbyiXfUC75BprS9ASocWXXBQCtkW8ol3xDueQbaOrYrgtY/HYe8tN4R1UA8+td\nSca6LgJohXxDueQbyiXfUKadh19k1oZa3HbpRpNs67oIAAAAAFjkVmSe/23DSM852rRpU0ZGRrJ7\n9+4kyZIlSzquCAAAAAAWvvHx8axZs6aVbWt6ztHIyEhGR0e7LgMAAAAA6DGREUCNiy++uOsSgJbI\nN5RLvqFc8g00pekJUGPp0qVdlwC0RL6hXPIN5ZJvoCkTGc3eaJJt27Ztc3s7ACxSO3bsyN69e7su\nA5ij4eHhLF++vOsyAICGxsbGsmLFisRERgAA82PHjh059dRTuy4DmCfbt2/X+AQAnqTpCQAclfoj\nPDdt2pSRkZGOqwFmqz/rq1HbAMAgTU+AGvfee29OO+20rssAWjA53yMjIx5VA4Xw9xvKJd9AUyYy\nAqhxySWXdF0C0BL5hnLJN5RLvoGmND0BalxzzTVdlwC0RL6hXPIN5ZJvoClNT4AaS5cu7boEoCXy\nDeWSbyiXfANNeaYnAMAkO3bsWBCTogwPDxc7G/VCOMelnl/nFgBA0xMA4BA7duzIqaee2nUZT9q+\nfXvj5tG+ffvy7ne/O1u2bMmePXty2mmn5dJLL815553XUpXNLKRz3PT87tu3L+95z3vyhS98IXff\nfXd2796ddevWZd26dS1WOXOL+dwmyR133JEPfvCD+fSnP52vf/3redaznpWXvexlufzyy004BgA0\noukJUGPjxo1517ve1XUZQAsOl++JEXKbkowckZrqjSdZM6sRe+eee24+//nPZ+PGjTn11FNz0003\nZfXq1Tlw4EBWr149/6U29OQxnZvk2R0VsSvJLWl8fnft2pXrr78+L3nJS3LOOefkhhtuyNDQUDs1\nzkL/eLq8eqsrt/m5TZLrrrsuDz30UN75znfm9NNPz0MPPZSrrroqZ511Vm677ba8+tWvnnZ9f7+h\nXPINNKXpCVBj//79XZcAtGTm+R5JsvhGln384x/PJz/5yWzevPnJkZ2vfOUr88ADD+Tiiy/Oeeed\nl2OOWSCPdX92kh/vuohmli1blm9/+9tJkt27d+eGG27ouKJ6i/PqrSYqOemkkw55b9WqVTnllFNy\nxRVXHLbp6e83lEu+gaYWyP/xAiwsGzZs6LoEoCWl5/ujH/1ohoeH88Y3vvGQ988///w8+OCD+exn\nP9tRZeU5ePBg1yUUZ3LDM0lOOOGEjIyM5Gtf+9ph1y8933A0k2+gKU1PAICCfOlLX8rIyMhTRnOe\neeaZSZJ77rmni7Jg1h5++OGMjY3l9NNP77oUAGAR0fQEACjI7t27c+KJJz7l/f57u3fvPtIlwZy8\n/e1vz2OPPZbLLrus61IAgEVE0xOgxq5du7ouAWiJfMPisXbt2nzoQx/K+9///rz0pS897PLyDeWS\nb6ApTU+AGhdccEHXJQAtKT3fS5YsqR3NuWfPnic/h8Vgw4YNed/73pcrrrgiF1544YzWKT3fcDST\nb6ApTU+AGuvXr++6BKAlpef7xS9+ccbHx3PgwIFD3v/iF7+YJDnjjDO6KAsa2bBhw5OvSy+9dMbr\nlZ5vOJrJN9CUpidAjdHR0a5LAFpSer7POeec7Nu3Lx/5yEcOef8DH/hATj755LziFa/oqDKYmfe+\n973ZsGFD1q5dm7Vr1zZat/R8w9FMvoGmju26AAAA5s+qVavy2te+Nm9729vyyCOP5EUvelE2b96c\n22+/PTfddFOGhoa6LnHR+8QnPpFHH300e/fuTZLcc889TzaZX//61+e4447rsrxF7aqrrsq6deuy\natWqvO51r8tnPvOZQz4/66yzOqoMAFhsND0BAGqNL9r933LLLbnsssty+eWXZ8+ePRkZGcnNN9+c\nN73pTfNY3zzock6KOez7wgsvzAMPPJAkGRoaypYtW7Jly5YMDQ1l586dWbp06TwVOXtdXr1z2ffH\nPvaxDA0N5dZbb82tt956yGdDQ0N54okn5lYcAHDU0PQEqHHjjTfmrW99a9dlAC04XL6Hh4d73605\nMgUdxkQ9M3fCCSfk6quvztVXX91CRXP35DHd0m0dyezO786dO1uoZH70j2chXL2zObd33nnnnPbp\n7zeUS76BpjQ9AWqMjY35nyoo1OHyvXz58mzfvv3JW5e7NDw8nOXLl3ddxrxbKOe4xPN7tJ9bf7+h\nXPINNKXpCVDj2muv7boEoCUzyXdpjbCFyDluz9F8bv39hnLJN9CU2dsBAAAAgKJoegIAAAAARdH0\nBAAAAACKoukJUOPss8/uugSgJfIN5ZJvKJd8A01pegLUuOiii7ouAWiJfEO55BvKJd9AU2ZvB6ix\ncuXKrksAWjI53+Pj4x1VAsyHwQz7+w3lkm+gKU1PAOCoNDw8nCRZs2ZNx5UA86GfaQCARNMTADhK\nLV++PNu3b8/evXu7LgWYo+Hh4SxfvrzrMgCABUTTE6DG1q1b84Y3vKHrMoAWDOZbkwTK4u83lEu+\ngaZMZARQY+PGjV2XALREvqFc8g3lkm+gKU1PgBrPec5zui4BaIl8Q7nkG8ol30BTmp4AAAAAQFE0\nPQEAAACAomh6AgD8/+3deXBdVR3A8W8KVKCMLNKhla3UUikIFAqVEQVZy6Isw7CJQqsIojMVGVnK\nYiMwogxbLQUBWQeoFBGxHVC2FBGVlqaAQGFkpzYFSoGuUCD1j995k8vta/KS97Lc8P3MvMm795x3\n70lmfkne7/3OOZIkSZJ6FXdvr9KcOXO6ewiSOsGMGTNobGzs7mFI6gTGt9R7Gd9S72V8S71TZ+bV\n6jrtyr3fQOAhYFh3D0SSJEmSJEkqqDnAPkBTLS9q0rM6A9NDkiRJkiRJUvs1UeOEpyRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkjrox8ArwHLgCeDr3TscSR0wDpgJLALeBO4G\nhpbpVw/8D1gGNADbdtH4JNXGWUAzcHnufD3GtlRUmwK3AguApcBsYOdcn3qMcalo1gIuIt5rLwNe\nAs4D6nL96jG+pZ5sD2AqEafNwKFl+tTTehx/DpgIvA0sAe4h/v6rkx0NfAh8H/gy8SZqMbB5dw5K\nUrvdBxwPDAN2IH4pvwqsm+lzJvAecBiwHTCZ+MW8XlcOVFKH7Qq8DDwJXJY5b2xLxbUh8ff6emAX\nYAtgL2Bwpo8xLhXTeCLBcSAR20cQBQpjM32Mb6nnOwA4n4jTZuCQXHslcXw18AawNzAceIj4kLNP\nZw5c8DgwKXfuOeBX3TAWSbWzMfELuVS5XQc0Aadn+vQF3gVO6tqhSeqA9YAXiH+UGmhJehrbUrH9\nGniklXZjXCquqcB1uXN3ATen58a3VDz5pGclcbw+UWx4ZKbPQOBjYP9Kb2x2tP36ElNn7s+dvx/4\nWtcPR1INbZC+LkxftwI24dPxvoJ4o2W8Sz3fJGAa8DCfnhZnbEvFdggwC7iTWJ6mETgx026MS8U1\nDdgX2Dod7wjsDtybjo1vqfgqieMRxHIX2T5NwDO0I9bXrGqYn00bA2sQ/2BlvQUM6PrhSKqROmKp\nikeJym1oiely8b5FF41LUsccQ0yD2TUdr8y0GdtSsQ0GTgEuBS4ERgK/Jd4w3YIxLhXZNcAgYqbG\nx8R777OBO1K78S0VXyVxPID4u/5+rs+bRMK0IiY9JSlcSawlUummZCvb7iKpm2wOTCAqRVakc3Ws\nuglCOca21PP1AWYA56bjp4CvAD8ikp6tMcalnm0sMJr48PJZYCfgCqLCy/iWer+axrHT29tvAfAJ\nq2aWNyF+EUsqnonAt4hNEOZlzs9PX8vF+3wk9VQjgP7ElNeP0mMP4o3UCoxtqejm0TIro+R5WqpD\njHGpuM4BLgCmEEnPW4nZWONSu/EtFV8lcTyfWF5y/VyfAbQj1k16tt8KYg2h/MKp+wH/7PrhSKpC\nHVHheRix0clrufZXiF+o2XjvC+yJ8S71ZA8SVV87psdw4AnijdNwjG2p6B4DtsmdG0rs6A7GuFRk\ndUSRUVYzLbM1jG+p+CqJ41lE4UK2z0Bidqax3smOInaRGgMMIz55WkRMp5NUHFcRO8TtQXxiVHqs\nnelzRupzGJFEuR2YC/Tr0pFKqtZ04u91ibEtFdcuRCHCOGAI8B1gCXBspo8xLhXTtcAbwEHE2p6H\nE+v8XZTpY3xLPV8/othgOPHBxanpeSlvVkkcXwW8ThQo7QQ8RMzkqmTJKlXpFCI7/QEwk8rXAZTU\nczQTnyQ35x7H5/qNJ6bSLQcagG27cIySaqMBuCx3ztiWiutg4Gkifp8FflCmjzEuFU8/4BLivfYy\n4EXgfFbdj8T4lnq2b9Ly/jr7nvuGTJ+24rgvsVHhAmApcA+waWcOWpIkSZIkSZIkSZIkSZIkSZIk\nSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIk\nSZIkSZIkSapeM3BIK+2DUp8dumQ0HTedGGctxlq6zrtVXkeSJOkzqU93D0CSJEk92k20JOA+AuYC\nNwMDa3iPAcBfa3i97rISuJb4fp5N5zYCpgKLgVmsmgydBJxW5loDgFM7Z5iSJEm9n0lPSZIktWYl\ncB+RhNsSGAPsBdxSw3u8Bayo4fW60zLi+/kkHZ8D9AN2Ah4Bfp/puxswEri8zHXeAhZ13jAlSZJ6\nN5OekiRJak0d8CGRhJsHPADcSSTsssYAc4Dl6espmba+wJXp9cuBV4GzMu356e0jgdmp70wiYZi3\nLXAvUUE5n0jCfiHTPh2YAFwMvAM0AeNz19iAqMycn+71H+BgIkm5CDgi1//bwJLUXqltgD8ALwLX\npXEDrAVcDZxMJJYlSZJUQyY9JUmS1Ja6zPPBwAFEMrLkh8CFwDgiyXc2cAFwfGofSyQMjwSGAscR\nic9y1gOmEYnTnYF64JJcn4FE1WQjMCKNZxNgSq7fCURSdCRwBvALYN/U1oeoYN0tjWcYcDrwMbAU\nmEwkcrPGEAnfpasZezlPAfsAawKj0jFpPA3pe5AkSZIkSZLUhW4i1vJcTEzdbibWqNwo0+d14Ojc\n684FHkvPJwAPtnKPbKXnScACYO1M+8l8enOg81l1DdDNUp8h6Xg6kRjNehy4KD3fn0hwDqG8XYnv\ne0A67k9UvH6jle+jAbgsd+7zwG1EkreBSApvDbxA/Ax/B7wE3JH6Zo3GjYwkSZI6xEpPSZIkteVh\nYEfgq8BEYE+ishIiGbgZcAORGC09ziGqQiESp8OJRN8EYL9W7jUMeBL4IHPu37k+I4h1RbP3m0NM\nE/9S6rMSeDr3uqY0XtJ45hLTzsuZSWxGdEI6/i7wGvBoK2MvZxFRSToojfl54Brg5+mag4jq12VE\nJaokSZJqYM3uHoAkSZJ6vGXAy+n5T4HtgSuI6dqlD9FPJCops0qb+cwGtgIOJKaXTyEqP49czf3q\nVnM+2/4X4MwybfMzzz8q014a7/I27gGx6dBPgN8QU9tvrOA1bRkDLCSqZf8E/Jn4Od1JVLBKkiSp\nBkx6SpIkqb1+SUzV3plYk3IeUWE5uZXXLCaSnVOAPxLT0zcA3sv1ew74HjG9vVTtmd80qZHYZOg1\nWhKrlchuGPQ0UaG6NfDf1fS/jdgIaSyxAdHN7bhXOf2B84Dd03EfYpMn0tc1qry+JEmSEqe3S5Ik\nqb1KmwidkY7HE5sYjSWmam9PVDT+LLWfBhxDrGc5FDiKmGqeT3gC3E6szXk9kWg8iJgKnjWJWA9z\nMrH25mBijc7raakSrWPVitHsuUeAvwN3EdWnpUrUUZn+7xLVmBcDfyOSu9W4gtiUqSkdP0YkeIcR\na5n+o8rrS5IkSZIkSarAjUTiL+9YYAWwZea4kajOfIeoBD00tZ2Y2hYTic77iTVCS7IbGUGsHTo7\nXWsWcDhR0blDps8QImG5kNhN/Tng0kx7uU2F7ibWHi3ZkEiUvk1M4X+KSHxm7Z3Gd0T+B1BGuXuW\njLxB88UAAADFSURBVAL+lTu3DrGB0fvEz2TjXPto3MhIkiRJkiRJUo0dRyRFK1kWajpweQ3vPRqT\nnpIkSZIkSZJqZB1indJngAsqfE0D8CFR0bpdlfdfQmy2tLDK60iSJEmSJEkSAPXE9P0HgHUrfM0X\nifVFBwNrVXn/0nW2bKujJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJHXY/wFpIl/LAWsH/QAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f68aacace10>"
+ "<matplotlib.figure.Figure at 0x7fe5e28d6610>"
]
},
"metadata": {},
@@ -534,15 +618,15 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>0.101647</td>\n",
+ " <td>0.022823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>0.548901</td>\n",
+ " <td>0.010507</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>4.262977</td>\n",
+ " <td>7.827988</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -551,9 +635,9 @@
"text/plain": [
" time\n",
"idle_state \n",
- "0 0.101647\n",
- "1 0.548901\n",
- "2 4.262977"
+ "0 0.022823\n",
+ "1 0.010507\n",
+ "2 7.827988"
]
},
"execution_count": 14,
@@ -562,7 +646,7 @@
}
],
"source": [
- "# Idle state residency for the big cluster\n",
+ "# Idle state residency for CPUs in the big cluster\n",
"trace.data_frame.cluster_idle_state_residency('big')"
]
},
@@ -575,9 +659,9 @@
"outputs": [
{
"data": {
- "image/png": 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Mx7f774r5c/8E8OPAvdsZ458BvlFVbwbOAF5XVftV1TOTBHg/cBFw9zaO45M8ceS4zwDe\nVVX7t++XJKl3Fr2SJK2cP6+qq6pqK/AHwHMW2ecRwB5V9RdVdXNVnQV8eolj3hW4epniuwnYF7h/\nklTVl6pqy3b2/VHgblX1B22cm4C3AD83ss8nq+r9AFX1nWWKUZKkXbKq7wAkSZphV448/yrwg4vs\nc3fgawu2XbHIfvO+0b5nl1XVuUn+AvhL4JAk7wF+s6q+tcjuhwJrklzTjkPzy/N/G9lnqbglSeqF\nM72SJK2ce4w8PxS4apF9rgbWLPG+hT7C0sufF/pfYO+R8UGjL7YzzEcC96dZgv1b8y8tOM4VwOVV\ndUD7uEtV3bmqnj56uDHikiRpt7DolSRp5bwoyZokBwCvAN65yD6fBG5O8qIkeyR5JvCwJY75Z8B+\nSd6W5BCA9hx/muSBi+w/Bzw7yfcluTfwgvkXkhyZ5GFJVgHfBm4Ebmlf3gKM3uv308D1SV6WZK82\n1gckObLTNyFJUk8seiVJWhkFvAM4G7gMuJSmr/e2O1XdBDwb+CXgWpqLX70fWLQntqquBR5J04/7\nqSTfBD4MbG3PM3/ueW9o990MnAr83chr+wFvBq4BNgL/A7y+fe0U4AFJrknynqq6BXgasLbd9+vt\ne/fr9G1IktSTVLkSSZKkSZLk34G/rqq39R2LJEnTzpleSZJ6luTRSVa3S4aPBX4E+GDfcUmSNAu8\nerMkSf27L/AumgtOXQ781BK3DpIkSWNwebMkSZIkaWa5vFmSJEmSNLMGsbw5idPZkiRJkjTDqiqL\nbR9E0QvgMm51cdxxx3Haaaf1HYamhPmirswVdWWuqCtzReMYQr4ki9a7gMubJUmSJEkzzKJXGnHY\nYYf1HYKmiPmirswVdWWuqCtzReMYer5Y9Eoj1q1b13cImiLmi7oyV9SVuaKuzBWNY+j5YtErSZIk\nSZpZFr2SJEmSpJmVIVzVOEkN4XNKkiRJ0hAl2e4ti5zplSRJkiTNLIteacSGDRv6DkFTxHxRV+aK\nujJX1JW5onEMPV9W9R3A7rLUzYolSZK0uNVrVrP5ys19hyFJO20wPb2s7zsKSZKkKbQehvD/i5Km\nmz29kiRJkqRBsuiVRm3sOwBNFfNFXZkr6spcUUdD79HUeIaeLxa9kiRJkqSZNfU9vUkOBk4HVgO3\nAG+uqv+3YB97eiVJknbGent6JU2+pXp6Z+Hqzd8DTqyquST7AJ9JcnZVXdJ3YJIkSZKkfk398uaq\n2lxVc+3zbwEXA2v6jUpTy14qjcN8UVfmiroyV9TR0Hs0NZ6h58vUF72jkhwGrAU+1W8kkiRJkqRJ\nMPU9vfPapc0bgFdX1XsXvGZPryRJ0s5Yb0+vpMk36z29JFkFvBt4+8KCd5uzgP3b53sBBwGHt+P5\npUSOHTt27NixY8eObztuzS+PXLdunWPHjh33Pp6bm2Pr1q0AbNq0iaXMxExvktOB/6mqE7fzujO9\n6mYjt/5jL+2I+aKuzBV1NYm5st6Z3km0YcOGbQWAtCNDyJelZnqnvqc3yVHAMcDjklyU5MIkT+o7\nLkmSJElS/2ZipndHnOmVJEnaSeud6ZU0+WZ6pleSJEmSpO2x6JVGbdzxLtI25ou6MlfUlbmijuYv\n7CN1MfR8seiVJEmSJM0se3olSZK0fevt6ZU0+ezplSRJkiQNkkWvNMpeKo3DfFFX5oq6MlfU0dB7\nNDWeoefLqr4D2G3W9x2AJEnS9Fm9ZnXfIUjSLhlMT+8QPqckSZIkDZE9vZIkSZKkQbLolUYMvd9B\n4zFf1JW5oq7MFXVlrmgcQ88Xi15JkiRJ0syyp1eSJEmSNNXs6ZUkSZIkDZJFrzRi6P0OGo/5oq7M\nFXVlrqgrc0XjGHq+TFTRm2TvvmOQJEmSJM2OiejpTfJI4C3APlV1SJIHA79SVS9cpuPb0ytJkiRJ\nM2oaenrfABwNfAOgqj4LPLrXiCRJkiRJU29Sil6q6ooFm27uJRAN2tD7HTQe80VdmSvqylxRV+aK\nxjH0fFnVdwCtK9olzpXkjsDxwMU9xyRJkiRJmnKT0tN7N+CNwBOAAGcDL6mqa5bp+Pb0SpIkSdKM\nWqqnd1Jmeu9bVceMbkhyFHB+T/FIkiRJkmbApPT0/nnHbdKKGnq/g8Zjvqgrc0VdmSvqylzROIae\nL73O9CZ5BPBI4MAkJ468tB+wRz9RSZIkSZJmRa89vUkeA6wDfhX4m5GXrgfeX1WXLtN57OmVJEmS\npBm1VE/vpFzI6tCq+uoKHt+iV5IkSZJm1FJF76T09N6Q5PVJPpDknPlH30FpeIbe76DxmC/qylxR\nV+aKujJXNI6h58ukFL1nAJcAhwO/D2wCLugzIEmSJEnS9JuU5c2fqaqHJvnPqnpQu+2CqvrRZTp+\n/x9yyqxes5rNV27uOwxJkiRJ2qFpuE/vTe3Pq5M8FbgKOGBZz7B+WY8287as39J3CJIkSZK0yyZl\nefNrktwZeCnwm8BbgBP6DUlDNPR+B43HfFFX5oq6MlfUlbmicQw9XyZlpvfaqvom8E3gsQBJjuo3\nJEmSJEnStJuUnt4Lq+qIHW3bheOXy5vHtB4mITckSZIkaUcmtqc3ySOARwIHJjlx5KX9gD06HuMU\n4GnAlvmLYEmSJEmSBP339N4J2Iem+N535HEd8NMdj3EqcPSKRKfBGXq/g8Zjvqgrc0VdmSvqylzR\nOIaeL73O9FbVecB5SU6rqq8CJLkLsLU6rq2tqo8nOXQl45QkSZIkTadee3qTvBJ4V1VdkmRP4F+B\ntcD3gOdW1Uc6HudQ4P3bW95sT+9OWG9PryRJkqTpsFRPb9/Lm38W+FL7/FiaeA4EHgO8tq+gJEmS\nJEmzoe9bFn13ZBnz0cCZVXUzcHGS5Y3tLGD/9vlewEHA4e14Y/vT8W3HrfkegHXr1s38eLTfYRLi\ncTzZY/PFcdfx/LZJicfx5I7n5uY44YQTJiYex5M7Pvnkk1m7du3ExON4ssezmC9zc3Ns3boVgE2b\nNrGUvpc3/zvwS8AWmhnfh1bVxva1S6rqfh2PcxjN8uYf2c7rLm8e1/phLm/esGHDtr9M0o6YL+rK\nXFFX5oq6Mlc0jiHky1LLm/sueh8OvI1mSfPJVfXqdvtTgOdV1XM6HOMdwDrgrjTF80lVdeqCfSx6\nx7V+mEWvJEmSpOkzsUXv7mLRuxPWW/RKkiRJmg6TfCEraaLM9wtIXZgv6spcUVfmiroyVzSOoeeL\nRa8kSZIkaWa5vFmLW+/yZkmSJEnTYeKXNyfZO8n/TfLmdnyfJE/rOy5JkiRJ0nSbiKIXOBX4DvCI\ndvw14DX9haOhGnq/g8Zjvqgrc0VdmSvqylzROIaeL5NS9N6rql4H3ARQVTcAi05NS5IkSZLU1UT0\n9Cb5BPB44PyqOiLJvYAzq+phy3T8/j/klFm9ZjWbr9zcdxiSJEmStENL9fSu2t3BbMdJwAeBeyQ5\nAzgKOG45TzAJxb0kSZIkafeaiOXNVfVh4Nk0he6ZwJFVtaHPmDRMQ+930HjMF3Vlrqgrc0VdmSsa\nx9DzpdeZ3iRHLNh0dfvzkCSHVNWFuzsmSZIkSdLs6LWnN8m5S7xcVfW4ZTpPubxZkiRJkmbTUj29\nE3Ehq5Vm0StJkiRJs2uporfXnt4kz17q0WdsGqah9ztoPOaLujJX1JW5oq7MFY1j6PnS99Wbn97+\n/AHgkcA57fixwCeA9/QRlCRJkiRpNkzE8uYkZwPHVtXV7fjuwGlVdfQyHd/lzZIkSZI0oyZ2efOI\ne8wXvK0twCF9BSNJkiRJmg2TUvR+NMmHkhyX5DjgX4CP9ByTBmjo/Q4aj/mirswVdWWuqCtzReMY\ner703dMLQFW9uL1w1aPaTW+qqrP6jEmSJEmSNP0moqd3pdnTK0mSJEmza6me3l5nepNcDyxWjQao\nqtpvN4ckSZIkSZohvfb0VtW+VbXfIo99LXjVh6H3O2g85ou6MlfUlbmirswVjWPo+TIpF7KSJEmS\nJGnZ2dMrSZIkSZpq03CfXkmSJEmSlp1FrzRi6P0OGo/5oq7MFXVlrqgrc0XjGHq+WPRKkiRJkmbW\nYHp6d+Z9q9esZvOVm5c7HEmSJEnSMlqqp3c4Re/6nXjjehjC9yNJkiRJ08wLWUkdDb3fQeMxX9SV\nuaKuzBV1Za5oHEPPF4teSZIkSdLMcnnzUta7vFmSJEmSJp3LmyVJkiRJgzT1RW+SJyW5JMmXk/x2\n3/Foug2930HjMV/UlbmirswVdWWuaBxDz5epLnqT3AH4C+Bo4AHAc5Lcr9+oJEmSJEmTYqp7epP8\nGHBSVT25Hb8cqKr64wX72dMrSZIkSTNqlnt61wBXjIyvbLdJkiRJksSqvgPYbc4C9m+f7wUcBBze\njje2PxeOW/Nr4NetW+d4xsej/Q6TEI/jyR6bL467jue3TUo8jid3PDc3xwknnDAx8Tie3PHJJ5/M\n2rVrJyYex5M9nsV8mZubY+vWrQBs2rSJpczC8ub1VfWkduzyZu2SDRs2bPvLJO2I+aKuzBV1Za6o\nK3NF4xhCviy1vHnai949gC8BjweuBj4NPKeqLl6wn0WvJEmSJM2opYreqV7eXFU3J3kxcDZNf/Ip\nCwteSZIkSdJw3aHvAHZVVX2wqu5bVfepqj/qOx5Nt/l+AakL80VdmSvqylxRV+aKxjH0fJn6oleS\nJEmSpO2Z6p7eruzplSRJkqTZNcv36ZUkSZIkabsseqURQ+930HjMF3Vlrqgrc0VdmSsax9DzxaJX\nkiRJkjSz7Oldynp7eiVJkiRp0i3V0zuconcnrF6zms1Xbl7ucCRJkiRJy8gLWdHM2I77sOAdnqH3\nO2g85ou6MlfUlbmirswVjWPo+TKYoleSJEmSNDyDWd48hM8pSZIkSUPk8mZJkiRJ0iBZ9Eojht7v\noPGYL+rKXFFX5oq6Mlc0jqHni0WvNGJubq7vEDRFzBd1Za6oK3NFXZkrGsfQ88WiVxqxdevWvkPQ\nFDFf1JW5oq7MFXVlrmgcQ88Xi15JkiRJ0syy6JVGbNq0qe8QNEXMF3Vlrqgrc0VdmSsax9DzZTC3\nLOo7BkmSJEnSytneLYsGUfRKkiRJkobJ5c2SJEmSpJll0StJkiRJmlkWvZIkSZKkmWXRK0mSJEma\nWRa9kiRJkqSZZdErSZIkSZpZFr2SJEmSpJll0StJ0kAlOTXJq/qOQ5KklWTRK0nSGJJsTPK4RbY/\nJskV7fPPJ7mufXwvybeTXN+Of2fk+bfb169rt32uff8tSe65yDmOHdn/upHjHLREvC9J8rkk30ry\nX0n+PskDlvH72Pa5JUmaRKv6DkCSpBlSAFX1wPkNSc4FTq+qU0f2+8P2tWOBF1TVoxc7znZ8YpH9\nF5Xk/wFPBn4J+ASwB/As4KnAF7oco8tpWDrepd+c7FFVNy9TLJIk3Y4zvZIkrbys8P63P0Byb+CF\nwM9V1XlVdVNV3VhVZ1bV6xbZ/9gkH1uwbduMc5KnJPlCO7N8RZITk+wNfAD4wdFZ5zRenuSyJP+d\n5J1J9m+Pc2h73F9M8lXgo7v6WSVJWopFryRJs+nxwBVV9Zkx3rNwxnZ0/Bbgl6tqP+CBwDlVdQPN\nTPJVVbVvVe1XVZuBlwDPAB4F/CBwLfBXC479aOB+wNFjxCdJ0tgseiVJmi6PSHJN+7g2yaXb2e+u\nwNW7eK7RGefvAg9Ism9VfbOq5pZ4368Av1tVV1fVTcCrgJ9OMv//HQWcVFXfrqrv7GKMkiQtyaJX\nkqTp8smqOqB93KWq7rOd/b4B3H0Zz/tTNL3AX01ybpIfW2LfQ4Gz5otz4IvATcDqkX2uXMbYJEna\nLoteSZJm00eBg5Mc0XH//wX2nh+0V4Tetry5qj5TVT8JHAi8F3jX/EuLHOu/gCcvKM6/v6pGZ553\n+uJXkiSNw6JXkqTx3SnJniOPPVbgHHsuOMf8v9mdLnJVVZfR9NGe2d5W6I7tcX42ycsWectnaZYv\nPyjJnsBJ8y+0731ukv3aKy1fD8xfcXkLcNck+40c62+B1yY5pH3/gUmeMfL6Ll+oS5Kkrix6JUka\n378ANwDfbn+etMS+OzOjWcDnF5zjuPa1H1vkPr0PXfQgVccDfwH8Jc3FpC4DfhJ4/yL7XkrTe/tR\n4MvAxxYrAWImAAASZklEQVTs8jxgY5KtwP8Bjmnf9yXgTODydjnzQcAbaWaDz07yTZrbJT1sweeT\nJGm3SJX/7kiSJEmSZpMzvZIkSZKkmWXRK0mSJEmaWRa9kiRJkqSZZdErSZIkSZpZq/oOYHdI4tW6\nJEmSJGmGVdWit8QbRNEL4FWq1cVxxx3Haaed1ncYmhLmi7oyV9SVuaKuzBWNYwj5kmz/FvAub5Yk\nSZIkzSyLXmnEYYcd1ncImiLmi7oyV9SVuaKuzBWNY+j5YtErjVi3bl3fIWiKmC/qylxRV+aKujJX\nNI6h54tFryRJkiRpZln0SpIkSZJmVoZwVeMkNYTPKUmSJElDlGS7tyxypleSJEmSNLMseqURGzZs\n6DsETRHzRV2ZK+rKXFFX5orGMfR8WdV3ALvLUjcrliRJkiTd1uo1q9l85ea+w9hlg+npZX3fUUiS\nJEnSFFkP01Iv2tMrSZIkSRoki15p1Ma+A9BUMV/UlbmirswVdWWuaBwDzxeLXkmSJEnSzLLolUYd\n3ncAmirmi7oyV9SVuaKuzBWNY+D5MvVFb5KDk5yT5AtJPpfkJX3HJEmSJEmaDFNf9ALfA06sqgcA\njwBelOR+PcekaTXwfgeNyXxRV+aKujJX1JW5onEMPF+mvuitqs1VNdc+/xZwMbCm36gkSZIkSZNg\n6oveUUkOA9YCn+o3Ek2tgfc7aEzmi7oyV9SVuaKuzBWNY+D5sqrvAJZLkn2AdwPHtzO+t3UWsH/7\nfC/gIG79w5+f7nfs2LFjx44dO3bs2LFjx7exYcMGANatWzcx47m5ObZu3QrApk2bWEqqaskdpkGS\nVcA/A/9aVW9c5PVi/W4PS9NoI7f+ZZd2xHxRV+aKujJX1JW5onHsbL6sh2mpF5NQVVnstVlZ3vxW\n4IuLFbySJEmSpOGa+qI3yVHAMcDjklyU5MIkT+o7Lk0pf2OqcZgv6spcUVfmiroyVzSOgefL1Pf0\nVtX5wB59xyFJkiRJmjxTP9MrLauNO95F2sZ8UVfmiroyV9SVuaJxDDxfLHolSZIkSTPLolcaNfB+\nB43JfFFX5oq6MlfUlbmicQw8Xyx6JUmSJEkzy6JXGjXwfgeNyXxRV+aKujJX1JW5onEMPF8yLTcb\n3hVJZv9DSpIkSdIyWr1mNZuv3Nx3GJ0koaqy2GtTf8uiroZQ3EuSJEmSbsvlzZIkSZKkmWXRK43Y\nsGFD3yFoipgv6spcUVfmiroyVzSOoeeLRa8kSZIkaWYN5kJWQ/ickiRJkjRES13IypleSZIkSdLM\nsuiVRgy930HjMV/UlbmirswVdWWuaBxDz5eJKnqT7N13DJIkSZKk2TERPb1JHgm8Bdinqg5J8mDg\nV6rqhct0fHt6JUmSJGlGTUNP7xuAo4FvAFTVZ4FH9xqRJEmSJGnqTUrRS1VdsWDTzb0EokEber+D\nxmO+qCtzRV2ZK+rKXNE4hp4vq/oOoHVFu8S5ktwROB64uOeYJEmSJElTblJ6eu8GvBF4AhDgbOAl\nVXXNMh3fnl5JkiRJmlFL9fROykzvfavqmNENSY4Czu8pHkmSJEnSDJiUnt4/77hNWlFD73fQeMwX\ndWWuqCtzRV2ZKxrH0POl15neJI8AHgkcmOTEkZf2A/boJypJkiRJ0qzotac3yWOAdcCvAn8z8tL1\nwPur6tJlOo89vZIkSZI0o5bq6Z2UC1kdWlVfXcHjW/RKkiRJ0oxaquidlJ7eG5K8PskHkpwz/+g7\nKA3P0PsdNB7zRV2ZK+rKXFFX5orGMfR8mZSi9wzgEuBw4PeBTcAFfQYkSZIkSZp+k7K8+TNV9dAk\n/1lVD2q3XVBVP7pMx+//Q2q7Vq9ZzeYrN/cdhiRJkqQpNQ336b2p/Xl1kqcCVwEHLOsZ1i/r0bSM\ntqzf0ncIkiRJkmbUpCxvfk2SOwMvBX4TeAtwQr8haYiG3u+g8Zgv6spcUVfmiroyVzSOoefLpMz0\nXltV3wS+CTwWIMlR/YYkSZIkSZp2k9LTe2FVHbGjbbtw/HJ58wRbD5OQh5IkSZKm08T29CZ5BPBI\n4MAkJ468tB+wR8djnAI8DdgyfxEsSZIkSZKg/57eOwH70BTf+448rgN+uuMxTgWOXpHoNDhD73fQ\neMwXdWWuqCtzRV2ZKxrH0POl15neqjoPOC/JaVX1VYAkdwG2Vsf1rlX18SSHrmSckiRJkqTp1GtP\nb5JXAu+qqkuS7An8K7AW+B7w3Kr6SMfjHAq8f3vLm+3pnXDr7emVJEmStPOW6unte3nzzwJfap8f\nSxPPgcBjgNf2FZQkSZIkaTb0fcui744sYz4aOLOqbgYuTrK8sZ0F7N8+3ws4CDi8HW9sfzruZ0zT\nZ7Bu3bptz4FexqP9DpMQj+PJHpsvjruO57dNSjyOJ3c8NzfHCSecMDHxOJ7c8cknn8zatWsnJh7H\nkz2exXyZm5tj69atAGzatIml9L28+d+BXwK20Mz4PrSqNravXVJV9+t4nMNoljf/yHZed3nzJFs/\nOcubN2zYsO0vk7Qj5ou6MlfUlbmirswVjWMI+bLU8ua+i96HA2+jWdJ8clW9ut3+FOB5VfWcDsd4\nB7AOuCtN8XxSVZ26YB+L3km2fnKKXkmSJEnTZ2KL3t3FonfCrbfolSRJkrTzJvlCVtJEme8XkLow\nX9SVuaKuzBV1Za5oHEPPF4teSZIkSdLMcnmz+rfe5c2SJEmSdt7EL29OsneS/5vkze34Pkme1ndc\nkiRJkqTpNhFFL3Aq8B3gEe34a8Br+gtHQzX0fgeNx3xRV+aKujJX1JW5onEMPV8mpei9V1W9DrgJ\noKpuABadmpYkSZIkqauJ6OlN8gng8cD5VXVEknsBZ1bVw5bp+P1/SG3X6jWr2Xzl5r7DkCRJkjSl\nlurpXbW7g9mOk4APAvdIcgZwFHDccp5gEop7SZIkSdLuNRHLm6vqw8CzaQrdM4Ejq2pDnzFpmIbe\n76DxmC/qylxRV+aKujJXNI6h50uvM71Jjliw6er25yFJDqmqC3d3TJIkSZKk2dFrT2+Sc5d4uarq\ncct0nnJ5syRJkiTNpqV6eifiQlYrzaJXkiRJkmbXUkVvrz29SZ691KPP2DRMQ+930HjMF3Vlrqgr\nc0VdmSsax9Dzpe+rNz+9/fkDwCOBc9rxY4FPAO/pIyhJkiRJ0myYiOXNSc4Gjq2qq9vx3YHTquro\nZTq+y5slSZIkaUZN7PLmEfeYL3hbW4BD+gpGkiRJkjQbJqXo/WiSDyU5LslxwL8AH+k5Jg3Q0Psd\nNB7zRV2ZK+rKXFFX5orGMfR86bunF4CqenF74apHtZveVFVn9RmTJEmSJGn6TURP70qzp1eSJEmS\nZtdSPb29zvQmuR5YrBoNUFW1324OSZIkSZI0Q3rt6a2qfatqv0Ue+1rwqg9D73fQeMwXdWWuqCtz\nRV2ZKxrH0PNlUi5kJUmSJEnSsrOnV5IkSZI01abhPr2SJEmSJC07i15pxND7HTQe80VdmSvqylxR\nV+aKxjH0fLHolSRJkiTNrMH09AKsXrOazVdu7jscSZIkSdIysqcXYD1s+dqWvqOQJEmSJO1Gwyl6\npQ6G3u+g8Zgv6spcUVfmiro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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f68aab79050>"
+ "<matplotlib.figure.Figure at 0x7fe5e26cd4d0>"
]
},
"metadata": {},
@@ -585,6 +669,7 @@
}
],
"source": [
+ "# Actual time spent in each idle state for CPUs in the big and LITTLE clusters\n",
"ia.plotClusterIdleStateResidency(['big', 'LITTLE'])"
]
},
@@ -597,9 +682,9 @@
"outputs": [
{
"data": {
- "image/png": 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mGwAAAEam+AZWmc/nU0eAA6Yfsyz0ZZaBfgwdxTcAAACMzJjvgRjzDQAAsLyM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\ncJJ3JTmmtXZ10hXoSb5uumS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UCoWsQ8y9Bx54gG3bttHd3Q3AihUrXi14n3feeRx44IFZ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\nJEmSJEmSJKkP/w/FYH7LGNtoFQAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f68aa9aee10>"
+ "<matplotlib.figure.Figure at 0x7fe5e274a210>"
]
},
"metadata": {},
@@ -607,6 +692,7 @@
}
],
"source": [
+ "# Percentage of time spent in each idle state for CPUs in the big and LITTLE clusters\n",
"ia.plotClusterIdleStateResidency(['big', 'LITTLE'], pct=True)"
]
}
diff --git a/ipynb/examples/trace_analysis/TraceAnalysis_TasksLatencies.ipynb b/ipynb/examples/trace_analysis/TraceAnalysis_TasksLatencies.ipynb
index 784ce93..51e92a9 100644
--- a/ipynb/examples/trace_analysis/TraceAnalysis_TasksLatencies.ipynb
+++ b/ipynb/examples/trace_analysis/TraceAnalysis_TasksLatencies.ipynb
@@ -4,30 +4,34 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<font size=\"8\">Trace Analysis Examples</font>\n",
- "<br>\n",
- "<font size=\"5\">Tasks Latencies</font>\n",
- "<br>\n",
- "<hr>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Import Required Modules"
+ "# 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."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
- "collapsed": true,
+ "collapsed": false,
"run_control": {
"marked": false
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2016-12-12 12:58:10,343 INFO : root : Using LISA logging configuration:\n",
+ "2016-12-12 12:58:10,344 INFO : root : /home/vagrant/lisa/logging.conf\n"
+ ]
+ }
+ ],
"source": [
"import logging\n",
"from conf import LisaLogging\n",
@@ -60,7 +64,6 @@
"\n",
"# Support for trace analysis\n",
"from trace import Trace\n",
- "from trace_analysis import TraceAnalysis\n",
"\n",
"# Support for plotting\n",
"import numpy\n",
@@ -73,7 +76,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Target Configuration"
+ "## 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**."
]
},
{
@@ -94,6 +99,7 @@
" \"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",
@@ -134,35 +140,35 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "10:23:42 INFO : Target - Using base path: /home/derkling/Code/lisa\n",
- "10:23:42 INFO : Target - Loading custom (inline) target configuration\n",
- "10:23:42 INFO : Target - Devlib modules to load: ['bl', 'cpufreq']\n",
- "10:23:42 INFO : Target - Connecting linux target:\n",
- "10:23:42 INFO : Target - username : root\n",
- "10:23:42 INFO : Target - host : 192.168.0.1\n",
- "10:23:42 INFO : Target - password : \n",
- "10:23:42 INFO : Target - Connection settings:\n",
- "10:23:42 INFO : Target - {'username': 'root', 'host': '192.168.0.1', 'password': ''}\n",
- "10:23:46 INFO : Target - Initializing target workdir:\n",
- "10:23:46 INFO : Target - /root/devlib-target\n",
- "10:23:50 INFO : Target - Topology:\n",
- "10:23:50 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n",
- "10:23:52 INFO : Platform - Loading default EM:\n",
- "10:23:52 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/juno.json\n",
- "10:23:53 INFO : FTrace - Enabled tracepoints:\n",
- "10:23:53 INFO : FTrace - sched_switch\n",
- "10:23:53 INFO : FTrace - sched_wakeup\n",
- "10:23:53 INFO : FTrace - sched_load_avg_cpu\n",
- "10:23:53 INFO : FTrace - sched_load_avg_task\n",
- "10:23:53 WARNING : Target - Using configuration provided RTApp calibration\n",
- "10:23:53 INFO : Target - Using RT-App calibration values:\n",
- "10:23:53 INFO : Target - {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n",
- "10:23:53 INFO : EnergyMeter - HWMON module not enabled\n",
- "10:23:53 WARNING : EnergyMeter - Energy sampling disabled by configuration\n",
- "10:23:53 INFO : TestEnv - Set results folder to:\n",
- "10:23:53 INFO : TestEnv - /home/derkling/Code/lisa/results/TraceAnalysis_TaskLatencies\n",
- "10:23:53 INFO : TestEnv - Experiment results available also in:\n",
- "10:23:53 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n"
+ "2016-12-12 12:58:17,443 INFO : TestEnv : Using base path: /home/vagrant/lisa\n",
+ "2016-12-12 12:58:17,444 INFO : TestEnv : Loading custom (inline) target configuration\n",
+ "2016-12-12 12:58:17,444 INFO : TestEnv : Devlib modules to load: ['bl', 'cpufreq']\n",
+ "2016-12-12 12:58:17,445 INFO : TestEnv : Connecting linux target:\n",
+ "2016-12-12 12:58:17,445 INFO : TestEnv : username : root\n",
+ "2016-12-12 12:58:17,446 INFO : TestEnv : host : 192.168.0.1\n",
+ "2016-12-12 12:58:17,446 INFO : TestEnv : password : juno\n",
+ "2016-12-12 12:58:17,447 INFO : TestEnv : Connection settings:\n",
+ "2016-12-12 12:58:17,447 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n",
+ "2016-12-12 12:58:24,242 INFO : TestEnv : Initializing target workdir:\n",
+ "2016-12-12 12:58:24,243 INFO : TestEnv : /root/devlib-target\n",
+ "2016-12-12 12:58:40,880 INFO : TestEnv : Topology:\n",
+ "2016-12-12 12:58:40,881 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n",
+ "2016-12-12 12:58:42,134 INFO : TestEnv : Loading default EM:\n",
+ "2016-12-12 12:58:42,135 INFO : TestEnv : /home/vagrant/lisa/libs/utils/platforms/juno.json\n",
+ "2016-12-12 12:58:45,386 INFO : TestEnv : Enabled tracepoints:\n",
+ "2016-12-12 12:58:45,387 INFO : TestEnv : sched_switch\n",
+ "2016-12-12 12:58:45,387 INFO : TestEnv : sched_wakeup\n",
+ "2016-12-12 12:58:45,388 INFO : TestEnv : sched_load_avg_cpu\n",
+ "2016-12-12 12:58:45,389 INFO : TestEnv : sched_load_avg_task\n",
+ "2016-12-12 12:58:45,389 WARNING : TestEnv : Using configuration provided RTApp calibration\n",
+ "2016-12-12 12:58:45,390 INFO : TestEnv : Using RT-App calibration values:\n",
+ "2016-12-12 12:58:45,390 INFO : TestEnv : {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n",
+ "2016-12-12 12:58:45,391 INFO : EnergyMeter : HWMON module not enabled\n",
+ "2016-12-12 12:58:45,391 WARNING : EnergyMeter : Energy sampling disabled by configuration\n",
+ "2016-12-12 12:58:45,392 INFO : TestEnv : Set results folder to:\n",
+ "2016-12-12 12:58:45,392 INFO : TestEnv : /home/vagrant/lisa/results/TraceAnalysis_TaskLatencies\n",
+ "2016-12-12 12:58:45,393 INFO : TestEnv : Experiment results available also in:\n",
+ "2016-12-12 12:58:45,393 INFO : TestEnv : /home/vagrant/lisa/results_latest\n"
]
}
],
@@ -176,7 +182,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Workload Execution"
+ "## Workload Configuration and Execution\n",
+ "\n",
+ "Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**."
]
},
{
@@ -228,42 +236,42 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "10:23:53 INFO : WlGen - Setup new workload ramp\n",
- "10:23:53 INFO : RTApp - Workload duration defined by longest task\n",
- "10:23:53 INFO : RTApp - Default policy: SCHED_OTHER\n",
- "10:23:53 INFO : RTApp - ------------------------\n",
- "10:23:53 INFO : RTApp - task [ramp], sched: using default policy\n",
- "10:23:53 INFO : RTApp - | calibration CPU: 1\n",
- "10:23:53 INFO : RTApp - | loops count: 1\n",
- "10:23:53 INFO : RTApp - + phase_000001: duration 0.500000 [s] (5 loops)\n",
- "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 60 %\n",
- "10:23:53 INFO : RTApp - | run_time 60000 [us], sleep_time 40000 [us]\n",
- "10:23:53 INFO : RTApp - + phase_000002: duration 0.500000 [s] (5 loops)\n",
- "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 55 %\n",
- "10:23:53 INFO : RTApp - | run_time 55000 [us], sleep_time 45000 [us]\n",
- "10:23:53 INFO : RTApp - + phase_000003: duration 0.500000 [s] (5 loops)\n",
- "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 50 %\n",
- "10:23:53 INFO : RTApp - | run_time 50000 [us], sleep_time 50000 [us]\n",
- "10:23:53 INFO : RTApp - + phase_000004: duration 0.500000 [s] (5 loops)\n",
- "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n",
- "10:23:53 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n",
- "10:23:53 INFO : RTApp - + phase_000005: duration 0.500000 [s] (5 loops)\n",
- "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 40 %\n",
- "10:23:53 INFO : RTApp - | run_time 40000 [us], sleep_time 60000 [us]\n",
- "10:23:53 INFO : RTApp - + phase_000006: duration 0.500000 [s] (5 loops)\n",
- "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 35 %\n",
- "10:23:53 INFO : RTApp - | run_time 35000 [us], sleep_time 65000 [us]\n",
- "10:23:53 INFO : RTApp - + phase_000007: duration 0.500000 [s] (5 loops)\n",
- "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 30 %\n",
- "10:23:53 INFO : RTApp - | run_time 30000 [us], sleep_time 70000 [us]\n",
- "10:23:53 INFO : RTApp - + phase_000008: duration 0.500000 [s] (5 loops)\n",
- "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n",
- "10:23:53 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n",
- "10:23:53 INFO : RTApp - + phase_000009: duration 0.500000 [s] (5 loops)\n",
- "10:23:53 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n",
- "10:23:53 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n",
- "10:23:57 INFO : WlGen - Workload execution START:\n",
- "10:23:57 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n"
+ "2016-12-12 12:58:52,280 INFO : Workload : Setup new workload ramp\n",
+ "2016-12-12 12:58:52,281 INFO : Workload : Workload duration defined by longest task\n",
+ "2016-12-12 12:58:52,282 INFO : Workload : Default policy: SCHED_OTHER\n",
+ "2016-12-12 12:58:52,282 INFO : Workload : ------------------------\n",
+ "2016-12-12 12:58:52,283 INFO : Workload : task [ramp], sched: using default policy\n",
+ "2016-12-12 12:58:52,283 INFO : Workload : | calibration CPU: 1\n",
+ "2016-12-12 12:58:52,284 INFO : Workload : | loops count: 1\n",
+ "2016-12-12 12:58:52,284 INFO : Workload : + phase_000001: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-12 12:58:52,285 INFO : Workload : | period 100000 [us], duty_cycle 60 %\n",
+ "2016-12-12 12:58:52,285 INFO : Workload : | run_time 60000 [us], sleep_time 40000 [us]\n",
+ "2016-12-12 12:58:52,286 INFO : Workload : + phase_000002: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-12 12:58:52,286 INFO : Workload : | period 100000 [us], duty_cycle 55 %\n",
+ "2016-12-12 12:58:52,287 INFO : Workload : | run_time 55000 [us], sleep_time 45000 [us]\n",
+ "2016-12-12 12:58:52,288 INFO : Workload : + phase_000003: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-12 12:58:52,288 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n",
+ "2016-12-12 12:58:52,289 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n",
+ "2016-12-12 12:58:52,290 INFO : Workload : + phase_000004: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-12 12:58:52,291 INFO : Workload : | period 100000 [us], duty_cycle 45 %\n",
+ "2016-12-12 12:58:52,291 INFO : Workload : | run_time 45000 [us], sleep_time 55000 [us]\n",
+ "2016-12-12 12:58:52,292 INFO : Workload : + phase_000005: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-12 12:58:52,293 INFO : Workload : | period 100000 [us], duty_cycle 40 %\n",
+ "2016-12-12 12:58:52,293 INFO : Workload : | run_time 40000 [us], sleep_time 60000 [us]\n",
+ "2016-12-12 12:58:52,294 INFO : Workload : + phase_000006: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-12 12:58:52,294 INFO : Workload : | period 100000 [us], duty_cycle 35 %\n",
+ "2016-12-12 12:58:52,295 INFO : Workload : | run_time 35000 [us], sleep_time 65000 [us]\n",
+ "2016-12-12 12:58:52,295 INFO : Workload : + phase_000007: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-12 12:58:52,296 INFO : Workload : | period 100000 [us], duty_cycle 30 %\n",
+ "2016-12-12 12:58:52,296 INFO : Workload : | run_time 30000 [us], sleep_time 70000 [us]\n",
+ "2016-12-12 12:58:52,296 INFO : Workload : + phase_000008: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-12 12:58:52,297 INFO : Workload : | period 100000 [us], duty_cycle 25 %\n",
+ "2016-12-12 12:58:52,297 INFO : Workload : | run_time 25000 [us], sleep_time 75000 [us]\n",
+ "2016-12-12 12:58:52,298 INFO : Workload : + phase_000009: duration 0.500000 [s] (5 loops)\n",
+ "2016-12-12 12:58:52,298 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n",
+ "2016-12-12 12:58:52,298 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n",
+ "2016-12-12 12:58:58,853 INFO : Workload : Workload execution START:\n",
+ "2016-12-12 12:58:58,853 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n"
]
}
],
@@ -275,7 +283,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Parse Trace and Profiling Data"
+ "## Parse Trace and Profiling Data"
]
},
{
@@ -292,26 +300,21 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "10:24:05 INFO : Content of the output folder /home/derkling/Code/lisa/results/TraceAnalysis_TaskLatencies\n"
+ "2016-12-12 12:59:36,883 INFO : root : Content of the output folder /home/vagrant/lisa/results/TraceAnalysis_TaskLatencies\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[01;34m/home/derkling/Code/lisa/results/TraceAnalysis_TaskLatencies\u001b[00m\r\n",
+ "/home/vagrant/lisa/results/TraceAnalysis_TaskLatencies\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_latencies_1236_1236__ramp,_rt-app.png\u001b[00m\r\n",
- "├── \u001b[01;35mtask_latencies_1295_1295__ramp,_rt-app.png\u001b[00m\r\n",
- "├── \u001b[01;35mtask_latencies_1644_1644__ramp,_rt-app.png\u001b[00m\r\n",
- "├── trace.dat\r\n",
- "├── trace.raw.txt\r\n",
- "└── trace.txt\r\n",
+ "└── trace.dat\r\n",
"\r\n",
- "0 directories, 10 files\r\n"
+ "0 directories, 5 files\r\n"
]
}
],
@@ -336,14 +339,13 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "10:24:05 INFO : LITTLE cluster max capacity: 447\n"
+ "2016-12-12 12:59:38,100 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",
- "#print json.dumps(platform, indent=4)\n",
"logging.info('LITTLE cluster max capacity: %d',\n",
" platform['nrg_model']['little']['cpu']['cap_max'])"
]
@@ -362,20 +364,18 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "10:24:05 INFO : Parsing FTrace format...\n",
- "10:24:06 INFO : Collected events spans a 6.641 [s] time interval\n",
- "10:24:06 INFO : Set plots time range to (0.000000, 6.641493)[s]\n",
- "10:24:06 INFO : Registering trace analysis modules:\n",
- "10:24:06 WARNING : No performance data found in:\n",
- "10:24:06 WARNING : /home/derkling/Code/lisa/results/TraceAnalysis_TaskLatencies\n",
- "10:24:06 INFO : perf\n",
- "10:24:06 INFO : latency\n",
- "10:24:06 INFO : eas\n",
- "10:24:06 INFO : tasks\n",
- "10:24:06 INFO : cpus\n",
- "10:24:06 INFO : functions\n",
- "10:24:06 INFO : status\n",
- "10:24:06 INFO : frequency\n"
+ "2016-12-12 12:59:39,175 INFO : Trace : Parsing FTrace format...\n",
+ "2016-12-12 12:59:39,536 INFO : Trace : Collected events spans a 13.065 [s] time interval\n",
+ "2016-12-12 12:59:39,536 INFO : Trace : Set plots time range to (0.000000, 13.064847)[s]\n",
+ "2016-12-12 12:59:39,537 INFO : Analysis : Registering trace analysis modules:\n",
+ "2016-12-12 12:59:39,538 INFO : Analysis : tasks\n",
+ "2016-12-12 12:59:39,539 INFO : Analysis : status\n",
+ "2016-12-12 12:59:39,539 INFO : Analysis : frequency\n",
+ "2016-12-12 12:59:39,540 INFO : Analysis : cpus\n",
+ "2016-12-12 12:59:39,541 INFO : Analysis : latency\n",
+ "2016-12-12 12:59:39,541 INFO : Analysis : idle\n",
+ "2016-12-12 12:59:39,542 INFO : Analysis : functions\n",
+ "2016-12-12 12:59:39,542 INFO : Analysis : eas\n"
]
}
],
@@ -390,7 +390,7 @@
"collapsed": true
},
"source": [
- "# Trace visualization"
+ "## Trace visualization"
]
},
{
@@ -490,7 +490,7 @@
" shape-rendering: crispEdges;\n",
"}\n",
"</style>\n",
- "<div id=\"fig_be1eab72adb54d86845c6c51b363175a\" class=\"eventplot\">\n",
+ "<div id=\"fig_f889c74eef67416a9de6996283d63119\" class=\"eventplot\">\n",
"<!-- TRAPPY_PUBLISH_SOURCE_LIB = \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.6/d3.min.js\" -->\n",
"<!-- TRAPPY_PUBLISH_SOURCE_LIB = \"http://labratrevenge.com/d3-tip/javascripts/d3.tip.v0.6.3.js\" -->\n",
"\n",
@@ -521,7 +521,7 @@
" /* TRAPPY_PUBLISH_REMOVE_STOP */\n",
" \n",
" req([\"require\", \"EventPlot\"], function() { /* TRAPPY_PUBLISH_REMOVE_LINE */\n",
- " EventPlot.generate('fig_be1eab72adb54d86845c6c51b363175a', '/nbextensions/', {\"lanes\": [{\"id\": 0, \"label\": \"CPU :0\"}, {\"id\": 1, \"label\": \"CPU :1\"}, {\"id\": 2, \"label\": \"CPU :2\"}, {\"id\": 3, \"label\": \"CPU :3\"}, {\"id\": 4, \"label\": \"CPU :4\"}, {\"id\": 5, \"label\": \"CPU :5\"}], \"keys\": [\"ramp-1423\", \"sudo-1420\", \"sudo-1427\", \"sh-1417\", \"sh-1429\", \"sh-1419\", \"sh-1426\", \"sh-1431\", \"sshd-1424\", \"sshd-1425\", \"sudo-1418\", \"sudo-1430\", \"shutils-1421\", \"shutils-1428\", \"sudo-1432\", \"sh-1422\", \"sh-1379\", \"sudo-1415\", \"sudo-1426\", \"sudo-1419\", \"sudo-1429\", \"sudo-1417\", \"sshd-1132\", \"rt-app-1422\", \"shutils-1420\", \"shutils-1427\", \"jbd2/sda2-8-875\", \"scp-1424\", \"scp-1425\", \"ksoftirqd/0-3\", \"syslogd-1104\", \"khugepaged-337\", \"usb-storage-853\", \"kworker/u12:2-1323\", \"kworker/u12:0-1320\", \"kworker/1:1-461\", \"init-1\", \"ksoftirqd/2-23\", \"ksoftirqd/1-17\", \"rcu_preempt-7\", \"rt-app-1423\", \"kworker/2:0-24\", \"rcu_sched-8\", \"kworker/1:1H-874\", \"kworker/2:1H-873\", \"sudo-1431\", \"watchdog/3-27\", \"watchdog/4-33\", \"watchdog/5-39\", \"watchdog/0-12\", \"watchdog/2-21\", \"watchdog/1-15\"], \"stride\": false, \"showSummary\": true, \"xDomain\": [3.0000010156072676e-06, 6.6414930000028107], \"data\": {\"shutils-1420\": [[0.63584999999875436, 0.63587199999892619, 2], [0.63592800000333227, 0.6359359999987646, 2], [0.63599700000486337, 0.63600600000063423, 2], [0.63606300000537885, 0.63607100000081118, 2], [0.63612999999895692, 0.6361380000016652, 2], [0.63619499999913387, 0.63620200000150362, 2], [0.63640500000474276, 0.6374950000026729, 2]], \"shutils-1421\": [[0.63326600000436883, 0.63448000000062166, 2], [0.63471800000115763, 0.63642600000457605, 1]], \"watchdog/4-33\": [[2.841762000003655, 2.8417780000017956, 4]], \"sudo-1430\": [[6.4265610000002198, 6.4294270000027609, 1]], \"sh-1426\": [[5.9956850000016857, 6.0020369999983814, 1]], \"shutils-1427\": 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" </script>\n",
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@@ -542,7 +542,7 @@
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"source": [
- "# Latency DataFrames"
+ "## Latency DataFrames"
]
},
{
@@ -553,19 +553,6 @@
},
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{
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- "10:24:06 WARNING : The [sched_switch] events contain 'prev_state' value [4096.0]\n",
- "10:24:06 WARNING : The [sched_switch] events contain 'prev_state' value [1.0]\n",
- "10:24:06 WARNING : The [sched_switch] events contain 'prev_state' value [0.0]\n",
- "10:24:06 WARNING : The [sched_switch] events contain 'prev_state' value [64.0]\n",
- "10:24:06 WARNING : which are not currently mapped into a task state.\n",
- "10:24:06 WARNING : Check mappings in:\n",
- "10:24:06 WARNING : /home/derkling/Code/lisa/libs/utils/analysis/latency_analysis.py::LatencyAnalysis _taskState()\n"
- ]
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- " <td>120</td>\n",
- " <td>trace-cmd</td>\n",
- " <td>1416</td>\n",
+ " <td>935</td>\n",
" <td>120</td>\n",
" <td>64</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>0.000875</th>\n",
+ " <th>0.000108</th>\n",
" <td><idle></td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sh</td>\n",
- " <td>1379</td>\n",
+ " <td>934</td>\n",
" <td>120</td>\n",
" <td>swapper/1</td>\n",
" <td>0</td>\n",
@@ -741,27 +715,40 @@
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>0.000877</th>\n",
- " <td>sudo</td>\n",
+ " <th>0.000501</th>\n",
+ " <td><idle></td>\n",
" <td>2</td>\n",
- " <td>1415</td>\n",
+ " <td>0</td>\n",
+ " <td>sudo</td>\n",
+ " <td>933</td>\n",
+ " <td>120</td>\n",
" <td>swapper/2</td>\n",
" <td>0</td>\n",
" <td>120</td>\n",
- " <td>sudo</td>\n",
- " <td>1415</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0.000507</th>\n",
+ " <td>sh</td>\n",
+ " <td>1</td>\n",
+ " <td>934</td>\n",
+ " <td>swapper/1</td>\n",
+ " <td>0</td>\n",
+ " <td>120</td>\n",
+ " <td>sh</td>\n",
+ " <td>934</td>\n",
" <td>120</td>\n",
" <td>64</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>0.001094</th>\n",
+ " <th>0.000691</th>\n",
" <td><idle></td>\n",
- " <td>2</td>\n",
+ " <td>1</td>\n",
" <td>0</td>\n",
- " <td>kworker/u12:0</td>\n",
- " <td>1320</td>\n",
+ " <td>rcu_preempt</td>\n",
+ " <td>7</td>\n",
" <td>120</td>\n",
- " <td>swapper/2</td>\n",
+ " <td>swapper/1</td>\n",
" <td>0</td>\n",
" <td>120</td>\n",
" <td>0</td>\n",
@@ -771,21 +758,21 @@
"</div>"
],
"text/plain": [
- " __comm __cpu __pid next_comm next_pid next_prio \\\n",
- "Time \n",
- "0.000003 <idle> 2 0 sudo 1415 120 \n",
- "0.000004 trace-cmd 1 1416 swapper/1 0 120 \n",
- "0.000875 <idle> 1 0 sh 1379 120 \n",
- "0.000877 sudo 2 1415 swapper/2 0 120 \n",
- "0.001094 <idle> 2 0 kworker/u12:0 1320 120 \n",
+ " __comm __cpu __pid next_comm next_pid next_prio \\\n",
+ "Time \n",
+ "0.000007 trace-cmd 2 935 swapper/2 0 120 \n",
+ "0.000108 <idle> 1 0 sh 934 120 \n",
+ "0.000501 <idle> 2 0 sudo 933 120 \n",
+ "0.000507 sh 1 934 swapper/1 0 120 \n",
+ "0.000691 <idle> 1 0 rcu_preempt 7 120 \n",
"\n",
" prev_comm prev_pid prev_prio prev_state \n",
"Time \n",
- "0.000003 swapper/2 0 120 0 \n",
- "0.000004 trace-cmd 1416 120 64 \n",
- "0.000875 swapper/1 0 120 0 \n",
- "0.000877 sudo 1415 120 64 \n",
- "0.001094 swapper/2 0 120 0 "
+ "0.000007 trace-cmd 935 120 64 \n",
+ "0.000108 swapper/1 0 120 0 \n",
+ "0.000501 swapper/2 0 120 0 \n",
+ "0.000507 sh 934 120 64 \n",
+ "0.000691 swapper/1 0 120 0 "
]
},
"execution_count": 12,
@@ -794,6 +781,7 @@
}
],
"source": [
+ "# Report information on sched_switch events\n",
"df = trace.data_frame.trace_event('sched_switch')\n",
"df.head()"
]
@@ -822,24 +810,24 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>1.147042</th>\n",
- " <td>0.000251</td>\n",
+ " <th>1.873757</th>\n",
+ " <td>0.000010</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>1.246761</th>\n",
- " <td>0.000007</td>\n",
+ " <th>1.973805</th>\n",
+ " <td>0.000020</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>1.346992</th>\n",
- " <td>0.000013</td>\n",
+ " <th>2.073804</th>\n",
+ " <td>0.000023</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>1.447048</th>\n",
- " <td>0.000016</td>\n",
+ " <th>2.173801</th>\n",
+ " <td>0.000020</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>1.547064</th>\n",
- " <td>0.000022</td>\n",
+ " <th>2.273804</th>\n",
+ " <td>0.000020</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -848,11 +836,11 @@
"text/plain": [
" wakeup_latency\n",
"Time \n",
- "1.147042 0.000251\n",
- "1.246761 0.000007\n",
- "1.346992 0.000013\n",
- "1.447048 0.000016\n",
- "1.547064 0.000022"
+ "1.873757 0.000010\n",
+ "1.973805 0.000020\n",
+ "2.073804 0.000023\n",
+ "2.173801 0.000020\n",
+ "2.273804 0.000020"
]
},
"execution_count": 13,
@@ -889,24 +877,24 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>1.349451</th>\n",
- " <td>0.000167</td>\n",
+ " <th>2.512645</th>\n",
+ " <td>0.000019</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>1.449459</th>\n",
- " <td>0.000027</td>\n",
+ " <th>2.516644</th>\n",
+ " <td>0.000052</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>3.449462</th>\n",
- " <td>0.000023</td>\n",
+ " <th>3.512646</th>\n",
+ " <td>0.000019</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>4.449462</th>\n",
- " <td>0.000033</td>\n",
+ " <th>4.512646</th>\n",
+ " <td>0.000017</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>5.449460</th>\n",
- " <td>0.000028</td>\n",
+ " <th>4.516644</th>\n",
+ " <td>0.000088</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -915,11 +903,11 @@
"text/plain": [
" preempt_latency\n",
"Time \n",
- "1.349451 0.000167\n",
- "1.449459 0.000027\n",
- "3.449462 0.000023\n",
- "4.449462 0.000033\n",
- "5.449460 0.000028"
+ "2.512645 0.000019\n",
+ "2.516644 0.000052\n",
+ "3.512646 0.000019\n",
+ "4.512646 0.000017\n",
+ "4.516644 0.000088"
]
},
"execution_count": 14,
@@ -950,24 +938,17 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "10:24:07 WARNING : The [sched_switch] events contain 'prev_state' value [4096.0]\n",
- "10:24:07 WARNING : The [sched_switch] events contain 'prev_state' value [1.0]\n",
- "10:24:07 WARNING : The [sched_switch] events contain 'prev_state' value [0.0]\n",
- "10:24:07 WARNING : The [sched_switch] events contain 'prev_state' value [64.0]\n",
- "10:24:07 WARNING : which are not currently mapped into a task state.\n",
- "10:24:07 WARNING : Check mappings in:\n",
- "10:24:07 WARNING : /home/derkling/Code/lisa/libs/utils/analysis/latency_analysis.py::LatencyAnalysis _taskState()\n",
- "10:24:07 INFO : Found: 45 WAKEUP latencies\n",
- "10:24:07 INFO : Found: 5 PREEMPT latencies\n",
- "10:24:07 INFO : Total: 50 latency events\n",
- "10:24:07 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n"
+ "2016-12-12 12:59:56,078 INFO : Analysis : Found: 46 WAKEUP latencies\n",
+ "2016-12-12 12:59:56,104 INFO : Analysis : Found: 5 PREEMPT latencies\n",
+ "2016-12-12 12:59:56,105 INFO : Analysis : Total: 51 latency events\n",
+ "2016-12-12 12:59:56,180 WARNING : Analysis : Event [sched_overutilized] not found, plot DISABLED!\n"
]
},
{
"data": {
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33FGHHXaY5s+fry222EIzZ85cIq4LL7xQf/nLX/TXv/5VP/vZz3T66adriy22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\nozsb63JMMvN35eo0ij+Sv8K6HJOImENxA/oSMDjiqXWpkTR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Dg4M577zz8sQnPjE33HBDLrzwwtx+++255JJLkiRr1qzJiSeemK99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\nRK5CYII=\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7ff18e5670d0>"
+ "<matplotlib.figure.Figure at 0x7f3943425e10>"
]
},
"metadata": {},
@@ -1007,25 +988,25 @@
" <tbody>\n",
" <tr>\n",
" <th>latency</th>\n",
- " <td>50</td>\n",
- " <td>0.000029</td>\n",
- " <td>0.000038</td>\n",
- " <td>0.000007</td>\n",
- " <td>0.000022</td>\n",
- " <td>0.000031</td>\n",
- " <td>0.00021</td>\n",
- " <td>0.000251</td>\n",
+ " <td>51.0</td>\n",
+ " <td>0.00002</td>\n",
+ " <td>0.000012</td>\n",
+ " <td>0.000006</td>\n",
+ " <td>0.00002</td>\n",
+ " <td>0.000024</td>\n",
+ " <td>0.00007</td>\n",
+ " <td>0.000088</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " count mean std min 50% 95% 99% \\\n",
- "latency 50 0.000029 0.000038 0.000007 0.000022 0.000031 0.00021 \n",
+ " count mean std min 50% 95% 99% \\\n",
+ "latency 51.0 0.00002 0.000012 0.000006 0.00002 0.000024 0.00007 \n",
"\n",
" max \n",
- "latency 0.000251 "
+ "latency 0.000088 "
]
},
"execution_count": 16,
@@ -1047,9 +1028,9 @@
"outputs": [
{
"data": {
- "image/png": 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8k3K64oorim6C1HbmXiky90qRuVeKzL1SY+a714SKd2effTalUqnu67TTTqOv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\nrkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7ff18dccbc90>"
+ "<matplotlib.figure.Figure at 0x7f394417b350>"
]
},
"metadata": {},
@@ -1063,7 +1044,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 18,
"metadata": {
"collapsed": false
},
@@ -1072,14 +1053,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "10:25:42 INFO : Set plots time range to (3.445000, 3.450000)[s]\n"
+ "2016-12-12 13:00:07,509 INFO : Trace : Set plots time range to (3.445000, 3.450000)[s]\n"
]
},
{
"data": {
- "image/png": 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Soer1etlFkNQjxrdUXca3VF3Gt6SiTClhnzOAgYGBAQd4lSRJkiRJkrpUr9fp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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7ff18dccbd90>"
+ "<matplotlib.figure.Figure at 0x7f39644f18d0>"
]
},
"metadata": {},
@@ -1109,7 +1090,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.9"
+ "version": "2.7.6"
},
"toc": {
"toc_cell": false,