def regressContech(inputs, numthreads, benchmarks):
if os.environ.has_key("NAS_HOME"):
NAS_HOME = os.environ["NAS_HOME"]
else:
print ">Error: Could not find NAS installation. Set NAS_HOME to the root of your NAS directory."
exit(1)
# Rebuild benchmarks
# NAS must be built sequentially
compileJobIds = []
for input in inputs:
for b in benchmarks:
x = compilationTimeCompare(b, input)
compileJobIds.append(x)
util.waitForJobs(x)
time.sleep(10) #Wait for files to be copied back
buildRoot = scrape_build.processAll(
[util.getFileNameForJob(j) for j in compileJobIds])
# Run the benchmarks
os.environ["TIME"] = '{"real":%e, "user":%U, "sys":%S, "mem":%M }'
for input in inputs:
runJobIds = []
runJobIds.extend(
[statsRun(b, numthreads, input, "contech") for b in benchmarks])
runJobIds.extend([nativeRun(b, numthreads, input) for b in benchmarks])
util.waitForJobs(runJobIds)
root = buildRoot + scrape_run.processAll(
[util.getFileNameForJob(j) for j in runJobIds])
# Aggregate output
table = aggregate_scrape_run.aggregate(root)
aggregate_scrape_run.computeSlowdown(table)
aggregate_scrape_run.generateCsv(table, "results-{}.csv".format(input))
def regressContech(inputs, numthreads, benchmarks):
if os.environ.has_key("NAS_HOME"):
NAS_HOME = os.environ["NAS_HOME"]
else:
print ">Error: Could not find NAS installation. Set NAS_HOME to the root of your NAS directory."
exit(1)
# Rebuild benchmarks
# NAS must be built sequentially
compileJobIds = []
for input in inputs:
for b in benchmarks:
x = compilationTimeCompare(b, input)
compileJobIds.append(x)
util.waitForJobs(x)
time.sleep(1) #Wait for files to be copied back
buildRoot = scrape_build.processAll([util.getFileNameForJob(j) for j in compileJobIds])
# Run the benchmarks
os.environ["TIME"] = '{"real":%e, "user":%U, "sys":%S, "mem":%M }'
for input in inputs:
runJobIds = []
runJobIds.extend([statsRun(b, numthreads, input, "contech") for b in benchmarks])
runJobIds.extend([nativeRun(b, numthreads, input) for b in benchmarks])
util.waitForJobs(runJobIds)
root = buildRoot + scrape_run.processAll([util.getFileNameForJob(j) for j in runJobIds])
# Aggregate output
table = aggregate_scrape_run.aggregate(root)
aggregate_scrape_run.computeSlowdown(table)
aggregate_scrape_run.generateCsv(table, "results-{}.csv".format(input))
def main(args):
input = "qscrape"
root = scrape_run.processAll(args[1:])
# Aggregate output
table = aggregate_scrape_run.aggregate(root)
aggregate_scrape_run.computeSlowdown(table)
aggregate_scrape_run.generateRunCsv(table, "results-{}.csv".format(input))
def main(args):
input = "qscrape"
root = scrape_run.processAll(args[1:])
# Aggregate output
table = aggregate_scrape_run.aggregate(root)
aggregate_scrape_run.computeSlowdown(table)
aggregate_scrape_run.generateRunCsv(table, "results-{}.csv".format(input))
def regressContech(inputs, numthreads, benchmarks, ro):
# Rebuild benchmarks
if ro == False:
compileJobIds = [compilationTimeCompare(b) for b in benchmarks]
buildRoot = scrape_build.processAll(compileJobIds)
# Run the benchmarks
os.environ["TIME"] = '{"real":%e, "user":%U, "sys":%S, "mem":%M }'
for input in inputs:
runJobIds = []
runJobIds.extend(
[statsRun(b, numthreads, input, "contech") for b in benchmarks])
runJobIds.extend([nativeRun(b, numthreads, input) for b in benchmarks])
root = buildRoot + scrape_run.processAll(runJobIds)
# Aggregate output
table = aggregate_scrape_run.aggregate(root)
aggregate_scrape_run.computeSlowdown(table)
aggregate_scrape_run.generateCsv(table, "results-{}.csv".format(input))
def regressContech(inputs, numthreads, benchmarks):
# Rebuild benchmarks
compileJobIds = [compilationTimeCompare(b) for b in benchmarks]
util.waitForJobs(compileJobIds)
buildRoot = scrape_build.processAll([util.getFileNameForJob(j) for j in compileJobIds])
# Run the benchmarks
os.environ["TIME"] = '{"real":%e, "user":%U, "sys":%S, "mem":%M }'
for input in inputs:
runJobIds = []
runJobIds.extend([statsRun(b, numthreads, input, "contech") for b in benchmarks])
runJobIds.extend([nativeRun(b, numthreads, input) for b in benchmarks])
util.waitForJobs(runJobIds)
root = buildRoot + scrape_run.processAll([util.getFileNameForJob(j) for j in runJobIds])
# Aggregate output
table = aggregate_scrape_run.aggregate(root)
aggregate_scrape_run.computeSlowdown(table)
aggregate_scrape_run.generateCsv(table, "results-{}.csv".format(input))