Trace Analysis Examples
Kernel Functions Profiling
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import logging
reload(logging)
logging.basicConfig(
format='%(asctime)-9s %(levelname)-8s: %(message)s',
datefmt='%I:%M:%S')
# Enable logging at INFO level
logging.getLogger().setLevel(logging.INFO)
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# Generate plots inline
%matplotlib inline
import json
import os
# Support to access the remote target
import devlib
from env import TestEnv
# RTApp configurator for generation of PERIODIC tasks
from wlgen import RTA, Ramp
# Support for trace events analysis
from trace import Trace
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# Setup target configuration
my_conf = {
# Target platform and board
"platform" : 'linux',
"board" : 'juno',
"host" : '192.168.0.1',
# Folder where all the results will be collected
"results_dir" : "TraceAnalysis_FunctionsProfiling",
# Define devlib modules to load
"exclude_modules" : [ 'hwmon' ],
# FTrace events to collect for all the tests configuration which have
# the "ftrace" flag enabled
"ftrace" : {
"functions" : [
"pick_next_task_fair",
"select_task_rq_fair",
"enqueue_task_fair",
"update_curr_fair",
"dequeue_task_fair",
],
"buffsize" : 100 * 1024,
},
# Tools required by the experiments
"tools" : [ 'trace-cmd', 'rt-app' ],
# Comment this line to calibrate RTApp in your own platform
"rtapp-calib" : {"0": 360, "1": 142, "2": 138, "3": 352, "4": 352, "5": 353},
}
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# Initialize a test environment using:
te = TestEnv(my_conf, wipe=False, force_new=True)
target = te.target
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def experiment(te):
# Create and RTApp RAMP task
rtapp = RTA(te.target, 'ramp', calibration=te.calibration())
rtapp.conf(kind='profile',
params={
'ramp' : Ramp(
start_pct = 60,
end_pct = 20,
delta_pct = 5,
time_s = 0.5).get()
})
# FTrace the execution of this workload
te.ftrace.start()
rtapp.run(out_dir=te.res_dir)
te.ftrace.stop()
# Collect and keep track of the trace
trace_file = os.path.join(te.res_dir, 'trace.dat')
te.ftrace.get_trace(trace_file)
# Collect and keep track of the Kernel Functions performance data
stats_file = os.path.join(te.res_dir, 'trace.stats')
te.ftrace.get_stats(stats_file)
# Dump platform descriptor
te.platform_dump(te.res_dir)
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experiment(te)
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# Base folder where tests folder are located
res_dir = te.res_dir
logging.info('Content of the output folder %s', res_dir)
!tree {res_dir}
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with open(os.path.join(res_dir, 'platform.json'), 'r') as fh:
platform = json.load(fh)
#print json.dumps(platform, indent=4)
logging.info('LITTLE cluster max capacity: %d',
platform['nrg_model']['little']['cpu']['cap_max'])
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trace = Trace(platform, res_dir, events=[])
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# Get the DataFrame for the specified list of kernel functions
df = trace.data_frame.functions_stats(['enqueue_task_fair', 'dequeue_task_fair'])
df
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# Get the DataFrame for the single specified kernel function
df = trace.data_frame.functions_stats('select_task_rq_fair')
df
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# Plot Average and Total execution time for the specified
# list of kernel functions
trace.analysis.functions.plotProfilingStats(
functions = [
'select_task_rq_fair',
'enqueue_task_fair',
'dequeue_task_fair'
],
metrics = [
'avg',
'time',
]
)
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# Plot Average execution time for the single specified kernel function
trace.analysis.functions.plotProfilingStats(
functions = 'update_curr_fair',
)
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