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 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 generate_extension_documentation(source_dir, outdir, ignore_paths):
loader = ExtensionLoader(keep_going=True)
loader.clear()
loader.update(paths=[source_dir], ignore_paths=ignore_paths)
for ext_type in loader.extension_kinds:
if not ext_type in GENERATE_FOR:
continue
outfile = os.path.join(outdir, '{}s.rst'.format(ext_type))
with open(outfile, 'w') as wfh:
wfh.write('.. _{}s:\n\n'.format(ext_type))
wfh.write(underline(capitalize('{}s'.format(ext_type))))
exts = loader.list_extensions(ext_type)
for ext in sorted(exts, key=lambda x: x.name):
wfh.write(get_rst_from_extension(ext))
def generate_extension_documentation(source_dir, outdir, ignore_paths):
loader = ExtensionLoader(keep_going=True)
loader.clear()
loader.update(paths=[source_dir], ignore_paths=ignore_paths)
for ext_type in loader.extension_kinds:
if not ext_type in GENERATE_FOR:
continue
outfile = os.path.join(outdir, '{}s.rst'.format(ext_type))
with open(outfile, 'w') as wfh:
wfh.write('.. _{}s:\n\n'.format(ext_type))
wfh.write(underline(capitalize('{}s'.format(ext_type))))
exts = loader.list_extensions(ext_type)
for ext in sorted(exts, key=lambda x: x.name):
wfh.write(get_rst_from_extension(ext))
