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workload-automation/wlauto/workloads/geekbench/__init__.py
Sebastian Goscik 8608c3b747 workloads: Fixed versions to be backward compatible 
In a recent commit workload versions were changed to match their APK versions.
This commit adds the old versions to the allowed versions and automatically
maps them onto the new values.
2016-09-13 11:07:18 +01:00

363 lines
16 KiB
Python
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# Copyright 2013-2015 ARM Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# pylint: disable=E1101
import os
import re
import tempfile
import json
from collections import defaultdict
from wlauto import AndroidUiAutoBenchmark, Parameter, Artifact
from wlauto.exceptions import ConfigError, WorkloadError
from wlauto.utils.misc import capitalize
import wlauto.common.android.resources
class Geekbench(AndroidUiAutoBenchmark):
name = 'geekbench'
description = """
Geekbench provides a comprehensive set of benchmarks engineered to quickly
and accurately measure processor and memory performance.
http://www.primatelabs.com/geekbench/
From the website:
Designed to make benchmarks easy to run and easy to understand, Geekbench
takes the guesswork out of producing robust and reliable benchmark results.
Geekbench scores are calibrated against a baseline score of 1,000 (which is
the score of a single-processor Power Mac G5 @ 1.6GHz). Higher scores are
better, with double the score indicating double the performance.
The benchmarks fall into one of four categories:
- integer performance.
- floating point performance.
- memory performance.
- stream performance.
Geekbench benchmarks: http://www.primatelabs.com/geekbench/doc/benchmarks.html
Geekbench scoring methedology:
http://support.primatelabs.com/kb/geekbench/interpreting-geekbench-scores
"""
summary_metrics = ['score', 'multicore_score']
versions = {
'3.0.0': {
'package': 'com.primatelabs.geekbench3',
'activity': '.HomeActivity',
},
'2.2.7': {
'package': 'ca.primatelabs.geekbench2',
'activity': '.HomeActivity',
},
}
begin_regex = re.compile(r'^\s*D/WebViewClassic.loadDataWithBaseURL\(\s*\d+\s*\)'
r'\s*:\s*(?P<content>\<.*)\s*$')
replace_regex = re.compile(r'<[^>]*>')
parameters = [
Parameter('version', default=sorted(versions.keys())[-1], allowed_values=sorted(versions.keys() +
['2', '3']),
description='Specifies which version of the workload should be run.'),
Parameter('times', kind=int, default=1,
description=('Specfies the number of times the benchmark will be run in a "tight '
'loop", i.e. without performaing setup/teardown inbetween.')),
]
@property
def activity(self):
return self.versions[self.version]['activity']
@property
def package(self):
return self.versions[self.version]['package']
def __init__(self, device, **kwargs):
super(Geekbench, self).__init__(device, **kwargs)
if self.version == '3':
self.version = '3.0.0'
elif self.version == '2':
self.version = '2.2.7'
self.uiauto_params['version'] = self.version
self.uiauto_params['times'] = self.times
self.run_timeout = 5 * 60 * self.times
def initialize(self, context):
if self.version == '3.0.0' and not self.device.is_rooted:
raise WorkloadError('Geekbench workload only works on rooted devices.')
def init_resources(self, context):
self.apk_file = context.resolver.get(wlauto.common.android.resources.ApkFile(self), version=self.version)
self.uiauto_file = context.resolver.get(wlauto.common.android.resources.JarFile(self))
self.device_uiauto_file = self.device.path.join(self.device.working_directory,
os.path.basename(self.uiauto_file))
if not self.uiauto_package:
self.uiauto_package = os.path.splitext(os.path.basename(self.uiauto_file))[0]
def update_result(self, context):
super(Geekbench, self).update_result(context)
if self.version == "2.2.7":
self.update_result_2(context)
else:
self.update_result_3(context)
def validate(self):
if (self.times > 1) and (self.version == '2.2.7'):
raise ConfigError('times parameter is not supported for version 2.2.7 of Geekbench.')
def update_result_2(self, context):
score_calculator = GBScoreCalculator()
score_calculator.parse(self.logcat_log)
score_calculator.update_results(context)
def update_result_3(self, context):
outfile_glob = self.device.path.join(self.device.package_data_directory, self.package, 'files', '*gb3')
on_device_output_files = [f.strip() for f in self.device.execute('ls {}'.format(outfile_glob),
as_root=True).split('\n') if f]
for i, on_device_output_file in enumerate(on_device_output_files):
host_temp_file = tempfile.mktemp()
self.device.pull_file(on_device_output_file, host_temp_file)
host_output_file = os.path.join(context.output_directory, os.path.basename(on_device_output_file))
with open(host_temp_file) as fh:
data = json.load(fh)
os.remove(host_temp_file)
with open(host_output_file, 'w') as wfh:
json.dump(data, wfh, indent=4)
context.iteration_artifacts.append(Artifact('geekout', path=os.path.basename(on_device_output_file),
kind='data',
description='Geekbench 3 output from device.'))
context.result.add_metric(namemify('score', i), data['score'])
context.result.add_metric(namemify('multicore_score', i), data['multicore_score'])
for section in data['sections']:
context.result.add_metric(namemify(section['name'] + '_score', i), section['score'])
context.result.add_metric(namemify(section['name'] + '_multicore_score', i),
section['multicore_score'])
class GBWorkload(object):
"""
Geekbench workload (not to be confused with WA's workloads). This is a single test run by
geek bench, such as preforming compression or generating Madelbrot.
"""
# Index maps onto the hundreds digit of the ID.
categories = [None, 'integer', 'float', 'memory', 'stream']
# 2003 entry-level Power Mac G5 is considered to have a baseline score of
# 1000 for every category.
pmac_g5_base_score = 1000
units_conversion_map = {
'K': 1,
'M': 1000,
'G': 1000000,
}
def __init__(self, wlid, name, pmac_g5_st_score, pmac_g5_mt_score):
"""
:param wlid: A three-digit workload ID. Uniquely identifies a workload and also
determines the category a workload belongs to.
:param name: The name of the workload.
:param pmac_g5_st_score: Score achieved for this workload on 2003 entry-level
Power Mac G5 running in a single thread.
:param pmac_g5_mt_score: Score achieved for this workload on 2003 entry-level
Power Mac G5 running in multiple threads.
"""
self.wlid = wlid
self.name = name
self.pmac_g5_st_score = pmac_g5_st_score
self.pmac_g5_mt_score = pmac_g5_mt_score
self.category = self.categories[int(wlid) // 100]
self.collected_results = []
def add_result(self, value, units):
self.collected_results.append(self.convert_to_kilo(value, units))
def convert_to_kilo(self, value, units):
return value * self.units_conversion_map[units[0]]
def clear(self):
self.collected_results = []
def get_scores(self):
"""
Returns a tuple (single-thraded score, multi-threaded score) for this workload.
Some workloads only have a single-threaded score, in which case multi-threaded
score will be ``None``.
Geekbench will perform four iterations of each workload in single-threaded and,
for some workloads, multi-threaded configurations. Thus there should always be
either four or eight scores collected for each workload. Single-threaded iterations
are always done before multi-threaded, so the ordering of the scores can be used
to determine which configuration they belong to.
This method should not be called before score collection has finished.
"""
no_of_results = len(self.collected_results)
if no_of_results == 4:
return (self._calculate(self.collected_results[:4], self.pmac_g5_st_score), None)
if no_of_results == 8:
return (self._calculate(self.collected_results[:4], self.pmac_g5_st_score),
self._calculate(self.collected_results[4:], self.pmac_g5_mt_score))
else:
msg = 'Collected {} results for Geekbench {} workload;'.format(no_of_results, self.name)
msg += ' expecting either 4 or 8.'
raise WorkloadError(msg)
def _calculate(self, values, scale_factor):
return max(values) * self.pmac_g5_base_score / scale_factor
def __str__(self):
return self.name
__repr__ = __str__
class GBScoreCalculator(object):
"""
Parses logcat output to extract raw Geekbench workload values and converts them into
category and overall scores.
"""
result_regex = re.compile(r'workload (?P<id>\d+) (?P<value>[0-9.]+) '
r'(?P<units>[a-zA-Z/]+) (?P<time>[0-9.]+)s')
# Indicates contribution to the overall score.
category_weights = {
'integer': 0.3357231,
'float': 0.3594,
'memory': 0.1926489,
'stream': 0.1054738,
}
#pylint: disable=C0326
workloads = [
# ID Name Power Mac ST Power Mac MT
GBWorkload(101, 'Blowfish', 43971, 40979),
GBWorkload(102, 'Text Compress', 3202, 3280),
GBWorkload(103, 'Text Decompress', 4112, 3986),
GBWorkload(104, 'Image Compress', 8272, 8412),
GBWorkload(105, 'Image Decompress', 16800, 16330),
GBWorkload(107, 'Lua', 385, 385),
GBWorkload(201, 'Mandelbrot', 665589, 653746),
GBWorkload(202, 'Dot Product', 481449, 455422),
GBWorkload(203, 'LU Decomposition', 889933, 877657),
GBWorkload(204, 'Primality Test', 149394, 185502),
GBWorkload(205, 'Sharpen Image', 2340, 2304),
GBWorkload(206, 'Blur Image', 791, 787),
GBWorkload(302, 'Read Sequential', 1226708, None),
GBWorkload(304, 'Write Sequential', 683782, None),
GBWorkload(306, 'Stdlib Allocate', 3739, None),
GBWorkload(307, 'Stdlib Write', 2070681, None),
GBWorkload(308, 'Stdlib Copy', 1030360, None),
GBWorkload(401, 'Stream Copy', 1367892, None),
GBWorkload(402, 'Stream Scale', 1296053, None),
GBWorkload(403, 'Stream Add', 1507115, None),
GBWorkload(404, 'Stream Triad', 1384526, None),
]
def __init__(self):
self.workload_map = {wl.wlid: wl for wl in self.workloads}
def parse(self, filepath):
"""
Extract results from the specified file. The file should contain a logcat log of Geekbench execution.
Iteration results in the log appear as 'I/geekbench' category entries in the following format::
| worklod ID value units timing
| \------------- | ----/ ---/
| | | | |
| I/geekbench(29026): [....] workload 101 132.9 MB/sec 0.0300939s
| | |
| | -----\
| label random crap we don't care about
"""
for wl in self.workloads:
wl.clear()
with open(filepath) as fh:
for line in fh:
match = self.result_regex.search(line)
if match:
wkload = self.workload_map[int(match.group('id'))]
wkload.add_result(float(match.group('value')), match.group('units'))
def update_results(self, context):
"""
http://support.primatelabs.com/kb/geekbench/interpreting-geekbench-2-scores
From the website:
Each workload's performance is compared against a baseline to determine a score. These
scores are averaged together to determine an overall, or Geekbench, score for the system.
Geekbench uses the 2003 entry-level Power Mac G5 as the baseline with a score of 1,000
points. Higher scores are better, with double the score indicating double the performance.
Geekbench provides three different kinds of scores:
:Workload Scores: Each time a workload is executed Geekbench calculates a score based
on the computer's performance compared to the baseline
performance. There can be multiple workload scores for the
same workload as Geekbench can execute each workload multiple
times with different settings. For example, the "Dot Product"
workload is executed four times (single-threaded scalar code,
multi-threaded scalar code, single-threaded vector code, and
multi-threaded vector code) producing four "Dot Product" scores.
:Section Scores: A section score is the average of all the workload scores for
workloads that are part of the section. These scores are useful
for determining the performance of the computer in a particular
area. See the section descriptions above for a summary on what
each section measures.
:Geekbench Score: The Geekbench score is the weighted average of the four section
scores. The Geekbench score provides a way to quickly compare
performance across different computers and different platforms
without getting bogged down in details.
"""
scores_by_category = defaultdict(list)
for wkload in self.workloads:
st_score, mt_score = wkload.get_scores()
scores_by_category[wkload.category].append(st_score)
context.result.add_metric(wkload.name + ' (single-threaded)', int(st_score))
if mt_score is not None:
scores_by_category[wkload.category].append(mt_score)
context.result.add_metric(wkload.name + ' (multi-threaded)', int(mt_score))
overall_score = 0
for category in scores_by_category:
scores = scores_by_category[category]
category_score = sum(scores) / len(scores)
overall_score += category_score * self.category_weights[category]
context.result.add_metric(capitalize(category) + ' Score', int(category_score))
context.result.add_metric('Geekbench Score', int(overall_score))
def namemify(basename, i):
return basename + (' {}'.format(i) if i else '')
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