da55cb8756
Find matching SAR and PM script output files, parse them and put calculated average from accross all workload iterations into final CSV with aggregated results. Signed-off-by: Karol Latecki <karol.latecki@intel.com> Change-Id: I9d3c445941e54eefbe93dd47d152d2ede22c9560 Reviewed-on: https://review.spdk.io/gerrit/c/spdk/spdk/+/15467 Tested-by: SPDK CI Jenkins <sys_sgci@intel.com> Reviewed-by: Konrad Sztyber <konrad.sztyber@intel.com> Reviewed-by: Jim Harris <james.r.harris@intel.com> Reviewed-by: Michal Berger <michal.berger@intel.com>
209 lines
10 KiB
Python
209 lines
10 KiB
Python
# SPDX-License-Identifier: BSD-3-Clause
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# Copyright (C) 2018 Intel Corporation.
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# All rights reserved.
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import os
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import re
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import json
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import logging
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from subprocess import check_output
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from collections import OrderedDict
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from json.decoder import JSONDecodeError
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def read_json_stats(file):
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with open(file, "r") as json_data:
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data = json.load(json_data)
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job_pos = 0 # job_post = 0 because using aggregated results
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# Check if latency is in nano or microseconds to choose correct dict key
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def get_lat_unit(key_prefix, dict_section):
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# key prefix - lat, clat or slat.
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# dict section - portion of json containing latency bucket in question
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# Return dict key to access the bucket and unit as string
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for k, _ in dict_section.items():
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if k.startswith(key_prefix):
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return k, k.split("_")[1]
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def get_clat_percentiles(clat_dict_leaf):
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if "percentile" in clat_dict_leaf:
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p99_lat = float(clat_dict_leaf["percentile"]["99.000000"])
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p99_9_lat = float(clat_dict_leaf["percentile"]["99.900000"])
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p99_99_lat = float(clat_dict_leaf["percentile"]["99.990000"])
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p99_999_lat = float(clat_dict_leaf["percentile"]["99.999000"])
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return [p99_lat, p99_9_lat, p99_99_lat, p99_999_lat]
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else:
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# Latest fio versions do not provide "percentile" results if no
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# measurements were done, so just return zeroes
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return [0, 0, 0, 0]
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read_iops = float(data["jobs"][job_pos]["read"]["iops"])
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read_bw = float(data["jobs"][job_pos]["read"]["bw"])
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lat_key, lat_unit = get_lat_unit("lat", data["jobs"][job_pos]["read"])
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read_avg_lat = float(data["jobs"][job_pos]["read"][lat_key]["mean"])
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read_min_lat = float(data["jobs"][job_pos]["read"][lat_key]["min"])
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read_max_lat = float(data["jobs"][job_pos]["read"][lat_key]["max"])
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clat_key, clat_unit = get_lat_unit("clat", data["jobs"][job_pos]["read"])
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read_p99_lat, read_p99_9_lat, read_p99_99_lat, read_p99_999_lat = get_clat_percentiles(
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data["jobs"][job_pos]["read"][clat_key])
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if "ns" in lat_unit:
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read_avg_lat, read_min_lat, read_max_lat = [x / 1000 for x in [read_avg_lat, read_min_lat, read_max_lat]]
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if "ns" in clat_unit:
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read_p99_lat = read_p99_lat / 1000
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read_p99_9_lat = read_p99_9_lat / 1000
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read_p99_99_lat = read_p99_99_lat / 1000
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read_p99_999_lat = read_p99_999_lat / 1000
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write_iops = float(data["jobs"][job_pos]["write"]["iops"])
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write_bw = float(data["jobs"][job_pos]["write"]["bw"])
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lat_key, lat_unit = get_lat_unit("lat", data["jobs"][job_pos]["write"])
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write_avg_lat = float(data["jobs"][job_pos]["write"][lat_key]["mean"])
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write_min_lat = float(data["jobs"][job_pos]["write"][lat_key]["min"])
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write_max_lat = float(data["jobs"][job_pos]["write"][lat_key]["max"])
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clat_key, clat_unit = get_lat_unit("clat", data["jobs"][job_pos]["write"])
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write_p99_lat, write_p99_9_lat, write_p99_99_lat, write_p99_999_lat = get_clat_percentiles(
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data["jobs"][job_pos]["write"][clat_key])
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if "ns" in lat_unit:
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write_avg_lat, write_min_lat, write_max_lat = [x / 1000 for x in [write_avg_lat, write_min_lat, write_max_lat]]
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if "ns" in clat_unit:
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write_p99_lat = write_p99_lat / 1000
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write_p99_9_lat = write_p99_9_lat / 1000
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write_p99_99_lat = write_p99_99_lat / 1000
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write_p99_999_lat = write_p99_999_lat / 1000
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return [read_iops, read_bw, read_avg_lat, read_min_lat, read_max_lat,
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read_p99_lat, read_p99_9_lat, read_p99_99_lat, read_p99_999_lat,
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write_iops, write_bw, write_avg_lat, write_min_lat, write_max_lat,
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write_p99_lat, write_p99_9_lat, write_p99_99_lat, write_p99_999_lat]
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def read_target_stats(measurement_name, results_file_list, results_dir):
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# Read additional metrics measurements done on target side and
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# calculate the average from across all workload iterations.
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# Currently only works for SAR CPU utilization and power draw measurements.
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# Other (bwm-ng, pcm, dpdk memory) need to be refactored and provide more
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# structured result files instead of a output dump.
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total_util = 0
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for result_file in results_file_list:
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with open(os.path.join(results_dir, result_file), "r") as result_file_fh:
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total_util += float(result_file_fh.read())
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avg_util = total_util / len(results_file_list)
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return {measurement_name: "{0:.3f}".format(avg_util)}
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def parse_results(results_dir, csv_file):
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files = os.listdir(results_dir)
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fio_files = filter(lambda x: ".fio" in x, files)
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json_files = [x for x in files if ".json" in x]
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sar_files = [x for x in files if "sar" in x and "util" in x]
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pm_files = [x for x in files if "pm" in x and "avg" in x]
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headers = ["read_iops", "read_bw", "read_avg_lat_us", "read_min_lat_us", "read_max_lat_us",
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"read_p99_lat_us", "read_p99.9_lat_us", "read_p99.99_lat_us", "read_p99.999_lat_us",
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"write_iops", "write_bw", "write_avg_lat_us", "write_min_lat_us", "write_max_lat_us",
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"write_p99_lat_us", "write_p99.9_lat_us", "write_p99.99_lat_us", "write_p99.999_lat_us"]
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header_line = ",".join(["Name", *headers])
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rows = set()
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for fio_config in fio_files:
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logging.info("Getting FIO stats for %s" % fio_config)
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job_name, _ = os.path.splitext(fio_config)
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aggr_headers = ["iops", "bw", "avg_lat_us", "min_lat_us", "max_lat_us",
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"p99_lat_us", "p99.9_lat_us", "p99.99_lat_us", "p99.999_lat_us"]
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# Look in the filename for rwmixread value. Function arguments do
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# not have that information.
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# TODO: Improve this function by directly using workload params instead
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# of regexing through filenames.
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if "read" in job_name:
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rw_mixread = 1
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elif "write" in job_name:
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rw_mixread = 0
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else:
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rw_mixread = float(re.search(r"m_(\d+)", job_name).group(1)) / 100
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# If "_CPU" exists in name - ignore it
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# Initiators for the same job could have different num_cores parameter
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job_name = re.sub(r"_\d+CPU", "", job_name)
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job_result_files = [x for x in json_files if x.startswith(job_name)]
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sar_result_files = [x for x in sar_files if x.startswith(job_name)]
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pm_result_files = [x for x in pm_files if x.startswith(job_name)]
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logging.info("Matching result files for current fio config %s:" % job_name)
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for j in job_result_files:
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logging.info("\t %s" % j)
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# There may have been more than 1 initiator used in test, need to check that
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# Result files are created so that string after last "_" separator is server name
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inits_names = set([os.path.splitext(x)[0].split("_")[-1] for x in job_result_files])
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inits_avg_results = []
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for i in inits_names:
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logging.info("\tGetting stats for initiator %s" % i)
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# There may have been more than 1 test run for this job, calculate average results for initiator
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i_results = [x for x in job_result_files if i in x]
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i_results_filename = re.sub(r"run_\d+_", "", i_results[0].replace("json", "csv"))
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separate_stats = []
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for r in i_results:
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try:
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stats = read_json_stats(os.path.join(results_dir, r))
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separate_stats.append(stats)
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logging.info(stats)
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except JSONDecodeError:
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logging.error("ERROR: Failed to parse %s results! Results might be incomplete!" % r)
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init_results = [sum(x) for x in zip(*separate_stats)]
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init_results = [x / len(separate_stats) for x in init_results]
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inits_avg_results.append(init_results)
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logging.info("\tAverage results for initiator %s" % i)
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logging.info(init_results)
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with open(os.path.join(results_dir, i_results_filename), "w") as fh:
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fh.write(header_line + "\n")
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fh.write(",".join([job_name, *["{0:.3f}".format(x) for x in init_results]]) + "\n")
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# Sum results of all initiators running this FIO job.
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# Latency results are an average of latencies from accros all initiators.
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inits_avg_results = [sum(x) for x in zip(*inits_avg_results)]
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inits_avg_results = OrderedDict(zip(headers, inits_avg_results))
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for key in inits_avg_results:
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if "lat" in key:
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inits_avg_results[key] /= len(inits_names)
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# Aggregate separate read/write values into common labels
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# Take rw_mixread into consideration for mixed read/write workloads.
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aggregate_results = OrderedDict()
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for h in aggr_headers:
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read_stat, write_stat = [float(value) for key, value in inits_avg_results.items() if h in key]
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if "lat" in h:
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_ = rw_mixread * read_stat + (1 - rw_mixread) * write_stat
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else:
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_ = read_stat + write_stat
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aggregate_results[h] = "{0:.3f}".format(_)
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if sar_result_files:
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aggr_headers.append("target_avg_cpu_util")
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aggregate_results.update(read_target_stats("target_avg_cpu_util", sar_result_files, results_dir))
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if pm_result_files:
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aggr_headers.append("target_avg_power")
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aggregate_results.update(read_target_stats("target_avg_power", pm_result_files, results_dir))
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rows.add(",".join([job_name, *aggregate_results.values()]))
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# Create empty results file with just the header line
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aggr_header_line = ",".join(["Name", *aggr_headers])
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with open(os.path.join(results_dir, csv_file), "w") as fh:
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fh.write(aggr_header_line + "\n")
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# Save results to file
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for row in rows:
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with open(os.path.join(results_dir, csv_file), "a") as fh:
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fh.write(row + "\n")
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logging.info("You can find the test results in the file %s" % os.path.join(results_dir, csv_file))
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