# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
import csv
import os
import re
import subprocess
import threading
import time
from .mozpowerutils
import get_logger
class IPGTimeoutError(Exception):
"""IPGTimeoutError is raised when we cannot stop Intel Power
Gadget
from running. One possble cause of this
is not calling
`stop_ipg` through a `finalize_power_measurements` call. The
other possiblity
is that IPG failed to stop.
"""
pass
class IPGMissingOutputFileError(Exception):
"""IPGMissingOutputFile is raised when a file path is given
to _clean_ipg_file but it does
not exist
or cannot be found
at the expected location.
"""
pass
class IPGEmptyFileError(Exception):
"""IPGEmptyFileError is raised when a file path is given
to _clean_ipg_file
and it exists but it
is empty (contains
no results to clean).
"""
pass
class IPGUnknownValueTypeError(Exception):
"""IPGUnknownValueTypeError is raised when a value within a
given results file (that was cleaned) cannot be converted to
its column
's expected data type.
"""
pass
class IntelPowerGadget(object):
"""IntelPowerGadget provides methods for using Intel Power Gadget
to measure power consumption.
::
from mozpower.intel_power_gadget
import IntelPowerGadget
# On Mac, the ipg_exe_path should point to '/Applications/Intel Power Gadget/PowerLog'
ipg = IntelPowerGadget(ipg_exe_path, ipg_measure_duration=600, sampling_rate=500)
ipg.start_ipg()
# Run tests...
ipg.stop_ipg()
# Now process results with IPGResultsHandler by passing it
# ipg.output_files, ipg.output_file_ext, ipg.ipg_measure_duration,
# and ipg.sampling_rate.
"""
def __init__(
self,
exe_file_path,
ipg_measure_duration=10,
sampling_rate=1000,
output_file_ext=
".txt",
file_counter=1,
output_file_path=
"powerlog",
logger_name=
"mozpower",
):
"""Initializes the IntelPowerGadget object.
:param str exe_file_path: path to Intel Power Gadget
'PowerLog' executable.
:param int ipg_measure_duration: duration to run IPG
for in seconds.
This does
not dictate when the tools shuts down. It only stops when stop_ipg
is called
in case the test runs
for a non-deterministic amount of time. The
IPG executable requires a duration to be supplied. The machinery
in place
is
to ensure that IPG keeps recording
as long
as the experiment runs, so
multiple files may result, which
is handled
in
IPGResultsHandler._combine_cumulative_rows. Defaults to 10s.
:param int sampling_rate: sampling rate of measurements
in milliseconds.
Defaults to 1000ms.
:param output_file_ext: file extension of data being output. Defaults to
'.txt'.
:param int file_counter: dictates the start of the file numbering (used
when test time exceeds duration value). Defaults to 0.
:param str output_file_path: path to the output location combined
with the output file prefix. Defaults to current working directory using the
prefix
'powerlog'.
:param str logger_name: logging logger name. Defaults to
'mozpower'.
"""
self._logger = get_logger(logger_name)
# Output-specific settings
self._file_counter = file_counter
self._output_files = []
self._output_file_path = output_file_path
self._output_file_ext = output_file_ext
self._output_dir_path, self._output_file_prefix = os.path.split(
self._output_file_path
)
# IPG-specific settings
self._ipg_measure_duration = ipg_measure_duration
# in seconds
self._sampling_rate = sampling_rate
# in milliseconds
self._exe_file_path = exe_file_path
# Setup thread for measurement gathering
self._thread = threading.Thread(
target=self.run, args=(exe_file_path, ipg_measure_duration)
)
self._thread.daemon =
True
self._running =
False
@property
def output_files(self):
"""Returns the list of files produced from running IPG.
:returns: list
"""
return self._output_files
@property
def output_file_ext(self):
"""Returns the extension of the files produced by IPG.
:returns: str
"""
return self._output_file_ext
@property
def output_file_prefix(self):
"""Returns the prefix of the files produces by IPG.
:returns: str
"""
return self._output_file_prefix
@property
def output_dir_path(self):
"""Returns the output directory of the files produced by IPG.
:returns: str
"""
return self._output_dir_path
@property
def sampling_rate(self):
"""Returns the specified sampling rate.
:returns: int
"""
return self._sampling_rate
@property
def ipg_measure_duration(self):
"""Returns the IPG measurement duration (see __init__ for description
of what this value does).
:returns: int
"""
return self._ipg_measure_duration
def start_ipg(self):
"""Starts the thread which runs IPG to start gathering measurements."""
self._logger.info(
"Starting IPG thread")
self._stop =
False
self._thread.start()
def stop_ipg(self, wait_interval=10, timeout=200):
"""Stops the thread which runs IPG and waits for it to finish it's work.
:param int wait_interval: interval time (
in seconds) at which to check
if
IPG
is finished.
:param int timeout: time to wait until _wait_for_ipg hits a time out,
in seconds.
"""
self._logger.info(
"Stopping IPG thread")
self._stop =
True
self._wait_for_ipg(wait_interval=wait_interval, timeout=timeout)
def _wait_for_ipg(self, wait_interval=10, timeout=200):
"""Waits for IPG to finish it's final dataset.
:param int wait_interval: interval time (
in seconds) at which to check
if
IPG
is finished.
:param int timeout: time to wait until this method hits a time out,
in seconds.
:raises: * IPGTimeoutError
"""
timeout_stime = time.time()
while self._running
and (time.time() - timeout_stime) < timeout:
self._logger.info(
"Waiting %s sec for Intel Power Gadget to stop" % wait_interval
)
time.sleep(wait_interval)
if self._running:
raise IPGTimeoutError(
"Timed out waiting for IPG to stop")
def _get_output_file_path(self):
"""Returns the next output file name to be used. Starts at 1 and increases
at each successive call. Used when the test duration exceeds the specified
duration through ipg_meaure_duration.
:returns: str
"""
self._output_file_path = os.path.join(
self._output_dir_path,
"%s_%s_%s"
% (self._output_file_prefix, self._file_counter, self._output_file_ext),
)
self._file_counter += 1
self._logger.info(
"Creating new file for IPG data measurements: %s" % self._output_file_path
)
return self._output_file_path
def run(self, exe_file_path, ipg_measure_duration):
"""Runs the IPG measurement gatherer. While stop has not been set to True,
it continuously gathers measurements into separate files that are merged
by IPGResultsHandler once the output_files are passed to it.
:param str exe_file_path: file path of where to find the IPG executable.
:param int ipg_measure_duration: time to gather measurements
for.
"""
self._logger.info(
"Starting to gather IPG measurements in thread")
self._running =
True
while not self._stop:
outname = self._get_output_file_path()
self._output_files.append(outname)
try:
subprocess.check_output(
[
exe_file_path,
"-duration",
str(ipg_measure_duration),
"-resolution",
str(self._sampling_rate),
"-file",
outname,
],
stderr=subprocess.STDOUT,
)
except subprocess.CalledProcessError
as e:
error_log = str(e)
if e.output:
error_log = e.output.decode()
self._logger.critical(
"Error while running Intel Power Gadget: %s" % error_log
)
self._running =
False
class IPGResultsHandler(object):
"""IPGResultsHandler provides methods for cleaning and formatting
the files that were created by IntelPowerGadget.
::
from mozpower.intel_power_gadget
import IntelPowerGadget, IPGResultsHandler
ipg = IntelPowerGadget(ipg_exe_path, ipg_measure_duration=600, sampling_rate=500)
# Run tests - call start_ipg and stop_ipg...
ipg_rh = IPGResultsHandler(
ipg.output_files,
ipg.output_dir_path,
ipg_measure_duration=ipg.ipg_measure_duration,
sampling_rate=ipg.sampling_rate
)
cleaned_data = ipg_rh.clean_ipg_data()
# you can also get the data from results after calling clean_ipg_data
cleaned_data = ipg_rh.results
perfherder_data = ipg_rh.format_ipg_data_to_partial_perfherder(
experiment_duration,
test_name
)
# you can also get the perfherder data from summarized_results
# after calling format_ipg_data_to_partial_perfherder
perfherder_data = ipg_rh.summarized_results
"""
def __init__(
self,
output_files,
output_dir_path,
ipg_measure_duration=10,
sampling_rate=1000,
logger_name=
"mozpower",
):
"""Initializes the IPGResultsHandler object.
:param list output_files: files output by IntelPowerGadget containing
the IPG data.
:param str output_dir_path: location to store cleaned files
and merged data.
:param int ipg_measure_duration: length of time that each IPG measurement lasted
in seconds (see IntelPowerGadget
for more information on this argument).
Defaults to 10s.
:param int sampling_rate: sampling rate of the measurements
in milliseconds.
Defaults to 1000ms.
:param str logger_name: logging logger name. Defaults to
'mozpower'.
"""
self._logger = get_logger(logger_name)
self._results = {}
self._summarized_results = {}
self._cleaned_files = []
self._csv_header =
None
self._merged_output_path =
None
self._output_file_prefix =
None
self._output_file_ext =
None
self._sampling_rate = sampling_rate
self._ipg_measure_duration = ipg_measure_duration
self._output_files = output_files
self._output_dir_path = output_dir_path
if self._output_files:
# Gather the output file extension, and prefix
# for the cleaned files, and the merged file.
single_file = self._output_files[0]
_, file = os.path.split(single_file)
self._output_file_ext =
"." + file.split(
".")[-1]
# This prefix detection depends on the path names created
# by _get_output_file_path.
integer_re = re.compile(r
"""(.*)_[\d+]_%s""" % self._output_file_ext)
match = integer_re.match(file)
if match:
self._output_file_prefix = match.group(1)
else:
self._output_file_prefix = file.split(
"_")[0]
self._logger.warning(
"Cannot find output file prefix from output file name %s"
"using the following prefix: %s" % (file, self._output_file_prefix)
)
@property
def results(self):
"""Returns the cleaned IPG data in the form of a dict.
Each key
is a measurement name
with the values being the list
of measurements. All value lists are sorted
in increasing time.
:returns: dict
"""
return self._results
@property
def summarized_results(self):
"""Returns IPG data in the form of a dict that is formatted
into a perfherder data blob.
:returns: dict
"""
return self._summarized_results
@property
def cleaned_files(self):
"""Returns a list of cleaned IPG data files that were output
after running clean_ipg_data.
:returns: list
"""
return self._cleaned_files
@property
def merged_output_path(self):
"""Returns the path to the cleaned, and merged output file.
:returns: str
"""
return self._merged_output_path
def _clean_ipg_file(self, file):
"""Cleans an individual IPG file and writes out a cleaned file.
An uncleaned file looks like this (this contains a partial
sample of the time series data):
```
"System Time",
"RDTSC",
"Elapsed Time (sec)"
"12:11:05:769",
"61075218263548",
" 2.002"
"12:11:06:774",
"61077822279584",
" 3.007"
"12:11:07:778",
"61080424421708",
" 4.011"
"12:11:08:781",
"61083023535972",
" 5.013"
"12:11:09:784",
"61085623402302",
" 6.016"
"Total Elapsed Time (sec) = 10.029232"
"Measured RDTSC Frequency (GHz) = 2.592"
"Cumulative Processor Energy_0 (Joules) = 142.337524"
"Cumulative Processor Energy_0 (mWh) = 39.538201"
"Average Processor Power_0 (Watt) = 14.192265"
"Cumulative IA Energy_0 (Joules) = 121.888000"
"Cumulative IA Energy_0 (mWh) = 33.857778"
"Average IA Power_0 (Watt) = 12.153273"
"Cumulative DRAM Energy_0 (Joules) = 7.453308"
"Cumulative DRAM Energy_0 (mWh) = 2.070363"
"Average DRAM Power_0 (Watt) = 0.743158"
"Cumulative GT Energy_0 (Joules) = 0.079834"
"Cumulative GT Energy_0 (mWh) = 0.022176"
"Average GT Power_0 (Watt) = 0.007960"
```
While a cleaned file looks like:
```
System Time,RDTSC,Elapsed Time (sec)
12:11:05:769,61075218263548, 2.002
12:11:06:774,61077822279584, 3.007
12:11:07:778,61080424421708, 4.011
12:11:08:781,61083023535972, 5.013
12:11:09:784,61085623402302, 6.016
```
The first portion of the file before the
'"Total Elapsed Time' entry
is
considered
as the time series which
is captured
as a dict
in the returned
results.
All the text starting
from '"Total Elapsed Time' is removed
from the file
and
is considered
as a summary of the experiment (which
is returned). This
portion
is ignored
in any other processing done by IPGResultsHandler,
and is not saved, unlike the returned results value.
Note that the new lines are removed
from the summary that
is returned
(
as well
as the results).
:param str file: file to clean.
:returns: a tuple of (dict, list, str)
for
(results, summary, clean_output_path)
see method comments above
for information on what
this output contains.
:raises: * IPGMissingOutputFileError
* IPGEmptyFileError
"""
self._logger.info(
"Cleaning IPG data file %s" % file)
txt =
""
if os.path.exists(file):
with open(file,
"r")
as f:
txt = f.read()
else:
# This should never happen, so prevent IPGResultsHandler
# from continuing to clean files if it does occur.
raise IPGMissingOutputFileError(
"The following file does not exist so it cannot be cleaned: %s " % file
)
if txt ==
"":
raise IPGEmptyFileError(
"The following file is empty so it cannot be cleaned: %s" % file
)
# Split the time series data from the summary
tseries, summary = re.split(
'"Total Elapsed Time', txt)
# Clean the summary
summary =
'"Total Elapsed Time' + summary
summary = [
line
for line
in re.split(r
"""\n""", summary.replace(
"\r",
""))
if line
]
# Clean the time series data, store the clean rows to write out later,
# and format the rows into a dict entry for each measure.
results = {}
clean_rows = []
csv_header =
None
for c, row
in enumerate(
csv.reader(tseries.split(
"\n"), quotechar=str(
'"'), delimiter=str(
","))
):
if not row:
continue
# Make sure we don't have any bad line endings
# contaminating the cleaned rows.
fmt_row = [
val.replace(
"\n",
"")
.replace(
"\t",
"")
.replace(
"\r",
"")
.replace(
"\\n",
"")
.strip()
for val
in row
]
if not fmt_row
or not any(fmt_row):
continue
clean_rows.append(fmt_row)
if c == 0:
csv_header = fmt_row
for col
in fmt_row:
results[col] = []
continue
for i, col
in enumerate(fmt_row):
results[csv_header[i]].append(col)
# Write out the cleaned data and check to make sure
# the csv header hasn't changed mid-experiment
_, fname = os.path.split(file)
clean_output_path = os.path.join(
self._output_dir_path,
fname.replace(
self._output_file_ext,
"_clean.%s" % self._output_file_ext.replace(
".",
""),
),
)
self._logger.info(
"Writing cleaned IPG results to %s" % clean_output_path)
try:
with open(clean_output_path,
"w")
as csvfile:
writer = csv.writer(csvfile)
for count, row
in enumerate(clean_rows):
if count == 0:
if self._csv_header
is None:
self._csv_header = row
elif self._csv_header != row:
self._logger.warning(
"CSV Headers from IPG data have changed during the experiment "
"expected: %s; got: %s"
% (str(self._csv_header), str(row))
)
writer.writerow(row)
except Exception
as e:
self._logger.warning(
"Could not write out cleaned results of %s to %s due to the following error"
", skipping this step: %s" % (file, clean_output_path, str(e))
)
# Check to make sure the expected number of samples
# exist in the file and that the columns have the correct
# data type.
column_datatypes = {
"System Time": str,
"RDTSC": int,
"default": float}
# pylint --py3k W1619
expected_samples = int(
self._ipg_measure_duration / (float(self._sampling_rate) / 1000)
)
for key
in results:
if len(results[key]) != expected_samples:
self._logger.warning(
"Unexpected number of samples in %s for column %s - "
"expected: %s, got: %s"
% (clean_output_path, key, expected_samples, len(results[key]))
)
dtype = column_datatypes[
"default"]
if key
in column_datatypes:
dtype = column_datatypes[key]
for val
in results[key]:
try:
# Check if converting from a string to its expected data
# type works, if not, it's not the expected data type.
dtype(val)
except ValueError
as e:
raise IPGUnknownValueTypeError(
"Cleaned file %s entry %s in column %s has unknown type "
"instead of the expected type %s - data corrupted, "
"cannot continue: %s"
% (clean_output_path, str(val), key, dtype.__name__, str(e))
)
# Check to make sure that IPG measured for the expected
# amount of time.
etime =
"Elapsed Time (sec)"
if etime
in results:
total_time = int(float(results[etime][-1]))
if total_time != self._ipg_measure_duration:
self._logger.warning(
"Elapsed time found in file %s is different from expected length - "
"expected: %s, got: %s"
% (clean_output_path, self._ipg_measure_duration, total_time)
)
else:
self._logger.warning(
"Missing 'Elapsed Time (sec)' in file %s" % clean_output_path
)
return results, summary, clean_output_path
def _combine_cumulative_rows(self, cumulatives):
"""Combine cumulative rows from multiple IPG files into
a single time series.
:param list cumulatives: list of cumulative time series collected
over time, on
for each file. Must be ordered
in increasing time.
:returns: list
"""
combined_cumulatives = []
val_mod = 0
for count, cumulative
in enumerate(cumulatives):
# Add the previous file's maximum cumulative value
# to the current one
mod_cumulative = [val_mod + float(val)
for val
in cumulative]
val_mod = mod_cumulative[-1]
combined_cumulatives.extend(mod_cumulative)
return combined_cumulatives
def clean_ipg_data(self):
"""Cleans all IPG files, or data, that was produced by an IntelPowerGadget object.
Returns a dict containing the merged data
from all files.
:returns: dict
"""
self._logger.info(
"Cleaning IPG data...")
if not self._output_files:
self._logger.warning(
"No IPG files to clean.")
return
# If this is called a second time for the same set of files,
# then prevent it from duplicating the cleaned file entries.
if self._cleaned_files:
self._cleaned_files = []
# Clean each file individually and gather the results
all_results = []
for file
in self._output_files:
results, summary, output_file_path = self._clean_ipg_file(file)
self._cleaned_files.append(output_file_path)
all_results.append(results)
# Merge all the results into a single result
combined_results = {}
for measure
in all_results[0]:
lmeasure = measure.lower()
if "cumulative" not in lmeasure
and "elapsed time" not in lmeasure:
# For measures which are not cumulative, or elapsed time,
# combine them without changing the data.
for count, result
in enumerate(all_results):
if "system time" in lmeasure
or "rdtsc" in lmeasure:
new_results = result[measure]
else:
new_results = [float(val)
for val
in result[measure]]
if count == 0:
combined_results[measure] = new_results
else:
combined_results[measure].extend(new_results)
else:
# For cumulative, and elapsed time measures, we need to
# modify all values - see _combine_cumulative_rows for
# more information on this procedure.
cumulatives = [result[measure]
for result
in all_results]
self._logger.info(
"Combining cumulative rows for '%s' measure" % measure
)
combined_results[measure] = self._combine_cumulative_rows(cumulatives)
# Write merged results to a new file
merged_output_path = os.path.join(
self._output_dir_path,
"%s_merged.%s"
% (self._output_file_prefix, self._output_file_ext.replace(
".",
"")),
)
self._merged_output_path = merged_output_path
self._logger.info(
"Writing merged IPG results to %s" % merged_output_path)
try:
with open(merged_output_path,
"w")
as csvfile:
writer = csv.writer(csvfile)
writer.writerow(self._csv_header)
# Use _csv_header list to keep the header ordering
# the same as the cleaned and raw files.
first_key = list(combined_results.keys())[0]
for row_count, _
in enumerate(combined_results[first_key]):
row = []
for measure
in self._csv_header:
row.append(combined_results[measure][row_count])
writer.writerow(row)
except Exception
as e:
self.merged_output_path =
None
self._logger.warning(
"Could not write out merged results to %s due to the following error"
", skipping this step: %s" % (merged_output_path, str(e))
)
# Check that the combined results have the expected number of samples.
# pylint W16919
expected_samples = int(
self._ipg_measure_duration / (float(self._sampling_rate) / 1000)
)
combined_expected_samples = len(self._cleaned_files) * expected_samples
for key
in combined_results:
if len(combined_results[key]) != combined_expected_samples:
self._logger.warning(
"Unexpected number of merged samples in %s for column %s - "
"expected: %s, got: %s"
% (
merged_output_path,
key,
combined_expected_samples,
len(results[key]),
)
)
self._results = combined_results
return self._results
def format_ipg_data_to_partial_perfherder(self, duration, test_name):
"""Format the merged IPG data produced by clean_ipg_data into a
partial/incomplete perfherder data blob. Returns the perfherder
data
and stores it
in _summarized_results.
This perfherder data still needs more information added to it before it
can be validated against the perfherder schema. The entries returned
through here are missing many required fields. Furthermore, the
'values'
entries need to be formatted into
'subtests'.
Here
is a sample of a complete perfherder data which uses
the
'utilization' results within the subtests:
```
{
"framework": {
"name":
"raptor"},
"type":
"power",
"unit":
"mWh"
"suites": [
{
"name":
"raptor-tp6-amazon-firefox-power",
"lowerIsBetter":
true,
"alertThreshold": 2.0,
"subtests": [
{
"lowerIsBetter":
true,
"unit":
"%",
"name":
"raptor-tp6-youtube-firefox-power-cpu",
"value": 14.409090909090908,
"alertThreshold": 2.0
},
{
"lowerIsBetter":
true,
"unit":
"%",
"name":
"raptor-tp6-youtube-firefox-power-gpu",
"value": 20.1,
"alertThreshold": 2.0
},
]
}
]
}
```
To obtain data that
is formatted to a complete perfherder data blob,
see get_complete_perfherder_data
in MozPower.
:param float duration: the actual duration of the test
in case some
data needs to be cut off
from the end.
:param str test_name: the name of the test.
:returns: dict
"""
self._logger.info(
"Formatting cleaned IPG data into partial perfherder data")
if not self._results:
self._logger.warning(
"No merged results found - cannot format data to perfherder format."
)
return
def replace_measure_name(name):
"""Replaces the long IPG names with shorter versions.
Returns the given name
if no conversions exist.
:param str name: name of the entry to replace.
:returns: str
"""
lname = name.lower()
if "ia " in lname:
return "processor-cores"
elif "processor " in lname:
return "processor-package"
elif "gt " in lname:
return "gpu"
elif "dram " in lname:
return "dram"
else:
return name
# Cut out entries which surpass the test duration.
# Occurs when IPG continues past the call to stop it.
cut_results = self._results
if duration:
cutoff_index = 0
for count, etime
in enumerate(self._results[
"Elapsed Time (sec)"]):
if etime > duration:
cutoff_index = count
break
if cutoff_index > 0:
for measure
in self._results:
cut_results[measure] = self._results[measure][:cutoff_index]
# Get the cumulative power used in mWh
cumulative_mwh = {}
for measure
in cut_results:
if "cumulative" in measure.lower()
and "mwh" in measure.lower():
cumulative_mwh[replace_measure_name(measure)] = float(
cut_results[measure][-1]
)
# Get the power usage rate in Watts
watt_usage = {}
for measure
in cut_results:
if "watt" in measure.lower()
and "limit" not in measure.lower():
# pylint --py3k W1619
watt_usage[replace_measure_name(measure) +
"-avg"] = sum(
[float(val)
for val
in cut_results[measure]]
) / len(cut_results[measure])
watt_usage[replace_measure_name(measure) +
"-max"] = max(
[float(val)
for val
in cut_results[measure]]
)
# Get average CPU and GPU utilization
average_utilization = {}
for utilization
in (
"CPU Utilization(%)",
"GT Utilization(%)"):
if utilization
not in cut_results:
self._logger.warning(
"Could not find measurements for: %s" % utilization
)
continue
utilized_name = utilization.lower()
if "cpu " in utilized_name:
utilized_name =
"cpu"
elif "gt " in utilized_name:
utilized_name =
"gpu"
# pylint --py3k W1619
average_utilization[utilized_name] = sum(
[float(val)
for val
in cut_results[utilization]]
) / len(cut_results[utilization])
# Get average and maximum CPU and GPU frequency
frequency_info = {
"cpu": {},
"gpu": {}}
for frequency_measure
in (
"CPU Frequency_0(MHz)",
"GT Frequency(MHz)"):
if frequency_measure
not in cut_results:
self._logger.warning(
"Could not find measurements for: %s" % frequency_measure
)
continue
fmeasure_name = frequency_measure.lower()
if "cpu " in fmeasure_name:
fmeasure_name =
"cpu"
elif "gt " in fmeasure_name:
fmeasure_name =
"gpu"
# pylint --py3k W1619
frequency_info[fmeasure_name][
"favg"] = sum(
[float(val)
for val
in cut_results[frequency_measure]]
) / len(cut_results[frequency_measure])
frequency_info[fmeasure_name][
"fmax"] = max(
[float(val)
for val
in cut_results[frequency_measure]]
)
frequency_info[fmeasure_name][
"fmin"] = min(
[float(val)
for val
in cut_results[frequency_measure]]
)
summarized_results = {
"utilization": {
"type":
"power",
"test": str(test_name) +
"-utilization",
"unit":
"%",
"values": average_utilization,
},
"power-usage": {
"type":
"power",
"test": str(test_name) +
"-cumulative",
"unit":
"mWh",
"values": cumulative_mwh,
},
"power-watts": {
"type":
"power",
"test": str(test_name) +
"-watts",
"unit":
"W",
"values": watt_usage,
},
"frequency-cpu": {
"type":
"power",
"test": str(test_name) +
"-frequency-cpu",
"unit":
"MHz",
"values": frequency_info[
"cpu"],
},
"frequency-gpu": {
"type":
"power",
"test": str(test_name) +
"-frequency-gpu",
"unit":
"MHz",
"values": frequency_info[
"gpu"],
},
}
self._summarized_results = summarized_results
return self._summarized_results
