def main():
warnings.simplefilter('ignore', tunerwarnings.NewProgramCrash)
warnings.simplefilter('ignore', tunerwarnings.TargetNotMet)
warnings.simplefilter('ignore', tunerwarnings.NanAccuracy)
progress.push()
progress.status("compiling benchmarks")
pbutil.chdirToPetabricksRoot()
pbutil.compilePetabricks();
r, lines = pbutil.loadAndCompileBenchmarks("./scripts/pbbenchmark.tests")
if filter(lambda x: x.rv!=0, r):
print "compile failed"
sys.exit(1)
print
print "All scores are relative performance to a baseline system."
print "Higher is better."
print
baselines = dict()
for line in csv.reader(open("./testdata/configs/baselines.csv")):
if len(line)>=3:
baselines[line[0]] = line[1:]
benchmarks=[]
for benchmark, cfg, n, accTarg in lines:
try:
baseline = baselines[benchmark]
except KeyError:
baseline = (1.0, 1.0)
benchmarks.append(Benchmark(benchmark, cfg, n, accTarg, baseline[0], baseline[1]))
print LONGBAR
print "Fixed (no autotuning) scores:"
print SHORTBAR
progress.remainingTicks(len(benchmarks)+3)
progress.tick()
for b in benchmarks:
progress.status("running fixed "+fmtCfg(b.cfg))
b.runFixed()
b.printFixed()
progress.tick()
score_fixed = geomean(map(Benchmark.scoreFixed, benchmarks))
print SHORTBAR
print "Fixed Score (pbbenchmark v%s): %.2f" % (VERSION, geomean(map(Benchmark.scoreFixed, benchmarks)))
print LONGBAR
print
print LONGBAR
print "Tuned scores:"
print SHORTBAR
for b in benchmarks:
progress.status("running tuned "+fmtCfg(b.cfg))
progress.status("autotuning")
b.autotune()
b.runTuned()
b.printTuned()
progress.tick()
score_tuned = geomean(map(Benchmark.scoreTuned, benchmarks))
score_training_time = geomean(map(Benchmark.scoreTrainingTime, benchmarks))
print SHORTBAR
print "Tuned Score (pbbenchmark v%s): %.2f" % (VERSION, score_tuned)
print "Training Time Score (pbbenchmark v%s): %.2f" % (VERSION, score_training_time)
print LONGBAR
print
if DEBUG:
print LONGBAR
print "Debug:"
print SHORTBAR
for b in benchmarks:
b.printDebug()
print LONGBAR
print
fd = open("./testdata/configs/baselines.csv.latest", "w")
for b in benchmarks:
print >>fd, "%s, %f, %f" % (b.benchmark, b.tuned_perf['average'], b.tuning_time)
fd.close()
if not os.path.isdir(LOGDIR):
os.mkdir(LOGDIR)
for b in benchmarks:
writelog(expandLog(b.cfg), b.logEntry())
writelog(expandLog('scores.log'), {
'version' : VERSION,
'score_fixed' : score_fixed,
'score_tuned' : score_tuned,
'score_training_time' : score_training_time,
'hostname' : socket.gethostname(),
'timestamp' : TIMESTAMP,
})
progress.tick()
progress.status("done")
progress.pop()
from optparse import OptionParser
parser = OptionParser(usage="usage: smoketest.py [options]")
parser.add_option("--learning", action="store_true", dest="learning", default=False, help="enable heuristics learning")
parser.add_option("--heuristics", type="string", help="name of the file containing the set of heuristics to use. Automatically enables --learning", default=None)
(options, args) = parser.parse_args()
if options.heuristics:
options.learning = True
if options.learning:
print "Learning of heuristics is ACTIVE"
if options.heuristics:
print "Using heuristics file: "+ str(options.heuristics)
else:
print "Using only heuristics in the database"
t1=time.time()
results,b=pbutil.loadAndCompileBenchmarks("./scripts/smoketest.tests", args, testBenchmark, postfn=checkBenchmark, learning=options.learning, heuristicSetFileName=options.heuristics, noLearningList=check_exclude)
t2=time.time()
passed=len(filter(lambda x: x.rv==0, results))
total=len(results)
print "%d of %d tests passed (took %.2fs)"%(passed,total,(t2-t1))
sys.exit(min(total-passed, 124))
def main():
warnings.simplefilter('ignore', tunerwarnings.NewProgramCrash)
warnings.simplefilter('ignore', tunerwarnings.SmallInputProgramCrash)
warnings.simplefilter('ignore', tunerwarnings.TargetNotMet)
warnings.simplefilter('ignore', tunerwarnings.NanAccuracy)
#Parse input options
from optparse import OptionParser
parser = OptionParser(usage="usage: pbbenchmark [options]")
parser.add_option("--learning",
action="store_true",
dest="learning",
default=False,
help="enable heuristics learning")
parser.add_option(
"--heuristics",
type="string",
help=
"name of the file containing the set of heuristics to use. Automatically enables --learning",
default=None)
(options, args) = parser.parse_args()
if options.heuristics:
options.learning = True
if options.learning:
print "Learning of heuristics is ACTIVE"
if options.heuristics:
print "Using heuristics file: " + str(options.heuristics)
else:
print "Using only heuristics in the database"
progress.push()
progress.status("compiling benchmarks")
pbutil.chdirToPetabricksRoot()
pbutil.compilePetabricks()
global REV
try:
REV = pbutil.getRevision()
except:
pass
r, lines = pbutil.loadAndCompileBenchmarks(
"./scripts/pbbenchmark.tests",
searchterms=sys.argv[1:],
learning=options.learning,
heuristicSetFileName=options.heuristics)
if filter(lambda x: x.rv != 0, r):
print "compile failed"
sys.exit(1)
print
print "All scores are relative performance to a baseline system."
print "Higher is better."
print
baselines = dict()
for line in csv.reader(open("./testdata/configs/baselines.csv")):
if len(line) >= 3:
baselines[line[0]] = line[1:]
benchmarks = []
for benchmark, cfg, n, accTarg in lines:
try:
baseline = baselines[benchmark]
except KeyError:
baseline = (1.0, 1.0)
benchmarks.append(
Benchmark(benchmark, cfg, n, accTarg, baseline[0], baseline[1]))
progress.remainingTicks(len(benchmarks))
#print LONGBAR
#print "Fixed (no autotuning) scores:"
#print SHORTBAR
#for b in benchmarks:
# progress.status("running fixed "+fmtCfg(b.cfg))
# b.runFixed()
# b.printFixed()
#score_fixed = geomean(map(Benchmark.scoreFixed, benchmarks))
#print SHORTBAR
#print "Fixed Score (pbbenchmark v%s): %.2f" % (VERSION, geomean(map(Benchmark.scoreFixed, benchmarks)))
#print LONGBAR
#print
print LONGBAR
print "Tuned scores:"
print SHORTBAR
for b in benchmarks:
progress.status("running tuned " + fmtCfg(b.cfg))
progress.status("autotuning")
b.autotune()
b.runTuned()
b.printTuned()
progress.tick()
score_tuned = geomean(map(Benchmark.scoreTuned, benchmarks))
score_training_time = geomean(map(Benchmark.scoreTrainingTime, benchmarks))
print SHORTBAR
print "Tuned Score (pbbenchmark v%s): %.2f" % (VERSION, score_tuned)
print "Training Time Score (pbbenchmark v%s): %.2f" % (VERSION,
score_training_time)
print LONGBAR
print
if DEBUG:
print LONGBAR
print "Debug:"
print SHORTBAR
for b in benchmarks:
b.printDebug()
print LONGBAR
print
fd = open("./testdata/configs/baselines.csv.latest", "w")
for b in benchmarks:
print >> fd, "%s, %f, %f" % (b.benchmark, b.tuned_perf['average'],
b.tuning_time)
fd.close()
if not os.path.isdir(LOGDIR):
os.mkdir(LOGDIR)
for b in benchmarks:
writelog(expandLog(b.cfg), b.logEntry())
writelog(
expandLog('scores.log'),
{
'version': VERSION,
'score_fixed': -1, #score_fixed,
'score_tuned': score_tuned,
'score_training_time': score_training_time,
'hostname': socket.gethostname(),
'timestamp': TIMESTAMP,
'revision': REV,
})
progress.status("done")
progress.pop()
(options, args) = parser.parse_args()
if options.heuristics:
options.learning = True
if options.learning:
print "Learning of heuristics is ACTIVE"
if options.heuristics:
print "Using heuristics file: " + str(options.heuristics)
else:
print "Using only heuristics in the database"
t1 = time.time()
results, b = pbutil.loadAndCompileBenchmarks(
"./scripts/smoketest.tests",
args,
testBenchmark,
postfn=checkBenchmark,
learning=options.learning,
heuristicSetFileName=options.heuristics,
noLearningList=check_exclude)
t2 = time.time()
passed = len(filter(lambda x: x.rv == 0, results))
total = len(results)
print "%d of %d tests passed (took %.2fs)" % (passed, total, (t2 - t1))
sys.exit(min(total - passed, 124))
def main():
warnings.simplefilter('ignore', tunerwarnings.NewProgramCrash)
warnings.simplefilter('ignore', tunerwarnings.SmallInputProgramCrash)
warnings.simplefilter('ignore', tunerwarnings.TargetNotMet)
warnings.simplefilter('ignore', tunerwarnings.NanAccuracy)
#Parse input options
from optparse import OptionParser
parser = OptionParser(usage="usage: pbbenchmark [options]")
parser.add_option("--learning", action="store_true", dest="learning", default=False, help="enable heuristics learning")
parser.add_option("--heuristics", type="string", help="name of the file containing the set of heuristics to use. Automatically enables --learning", default=None)
(options, args) = parser.parse_args()
if options.heuristics:
options.learning = True
if options.learning:
print "Learning of heuristics is ACTIVE"
if options.heuristics:
print "Using heuristics file: "+ str(options.heuristics)
else:
print "Using only heuristics in the database"
progress.push()
progress.status("compiling benchmarks")
pbutil.chdirToPetabricksRoot()
pbutil.compilePetabricks()
global REV
try:
REV=pbutil.getRevision()
except:
pass
r, lines = pbutil.loadAndCompileBenchmarks("./scripts/pbbenchmark.tests", searchterms=sys.argv[1:], learning=options.learning, heuristicSetFileName=options.heuristics)
if filter(lambda x: x.rv!=0, r):
print "compile failed"
sys.exit(1)
print
print "All scores are relative performance to a baseline system."
print "Higher is better."
print
baselines = dict()
for line in csv.reader(open("./testdata/configs/baselines.csv")):
if len(line)>=3:
baselines[line[0]] = line[1:]
benchmarks=[]
for benchmark, cfg, n, accTarg in lines:
try:
baseline = baselines[benchmark]
except KeyError:
baseline = (1.0, 1.0)
benchmarks.append(Benchmark(benchmark, cfg, n, accTarg, baseline[0], baseline[1]))
progress.remainingTicks(len(benchmarks))
#print LONGBAR
#print "Fixed (no autotuning) scores:"
#print SHORTBAR
#for b in benchmarks:
# progress.status("running fixed "+fmtCfg(b.cfg))
# b.runFixed()
# b.printFixed()
#score_fixed = geomean(map(Benchmark.scoreFixed, benchmarks))
#print SHORTBAR
#print "Fixed Score (pbbenchmark v%s): %.2f" % (VERSION, geomean(map(Benchmark.scoreFixed, benchmarks)))
#print LONGBAR
#print
print LONGBAR
print "Tuned scores:"
print SHORTBAR
for b in benchmarks:
progress.status("running tuned "+fmtCfg(b.cfg))
progress.status("autotuning")
b.autotune()
b.runTuned()
b.printTuned()
progress.tick()
score_tuned = geomean(map(Benchmark.scoreTuned, benchmarks))
score_training_time = geomean(map(Benchmark.scoreTrainingTime, benchmarks))
print SHORTBAR
print "Tuned Score (pbbenchmark v%s): %.2f" % (VERSION, score_tuned)
print "Training Time Score (pbbenchmark v%s): %.2f" % (VERSION, score_training_time)
print LONGBAR
print
if DEBUG:
print LONGBAR
print "Debug:"
print SHORTBAR
for b in benchmarks:
b.printDebug()
print LONGBAR
print
fd = open("./testdata/configs/baselines.csv.latest", "w")
for b in benchmarks:
print >>fd, "%s, %f, %f" % (b.benchmark, b.tuned_perf['average'], b.tuning_time)
fd.close()
if not os.path.isdir(LOGDIR):
os.mkdir(LOGDIR)
for b in benchmarks:
writelog(expandLog(b.cfg), b.logEntry())
writelog(expandLog('scores.log'), {
'version' : VERSION,
'score_fixed' : -1,#score_fixed,
'score_tuned' : score_tuned,
'score_training_time' : score_training_time,
'hostname' : socket.gethostname(),
'timestamp' : TIMESTAMP,
'revision' : REV,
})
progress.status("done")
progress.pop()
return False
if diffFiles(outfile, outfile+".latest"):
time.sleep(0.1) #try letting the filesystem settle down
if diffFiles(outfile, outfile+".latest"):
print "run FAILED (wrong output)"
return False
print "run PASSED"
return True
return test()
if 'nocheck' in sys.argv[1:]:
sys.argv[1:] = filter(lambda x: x!='nocheck', sys.argv[1:])
CHECK = False
t1=time.time()
results,b=pbutil.loadAndCompileBenchmarks("./scripts/smoketest.tests", sys.argv[1:], testBenchmark, postfn=checkBenchmark)
t2=time.time()
passed=len(filter(lambda x: x.rv==0, results))
total=len(results)
print "%d of %d tests passed (took %.2fs)"%(passed,total,(t2-t1))
sys.exit(min(total-passed, 124))