def run(self, phased=False):
counters = [
'inst_retired.any', 'instructions', 'mem_inst_retired.all_loads',
'mem_inst_retired.all_stores', 'L1-dcache-loads',
'L1-dcache-stores', 'LLC-load-misses', 'LLC-store-misses',
'cycles', 'fp_arith_inst_retired.128b_packed_double',
'fp_arith_inst_retired.128b_packed_single',
'fp_arith_inst_retired.256b_packed_double',
'fp_arith_inst_retired.256b_packed_single'
]
for prereq in self.prereqs:
self.logger.info("Running prereq %s" % prereq)
run_command(prereq.split(), self.logger)
for task in self.commands:
name = task["name"]
command = task["command"]
self.logger.info("Running command %s" % name)
p = Perf()
p.set_command(command)
p.set_counters(counters)
p.set_repeat_factor(1)
if phased:
result = p.run_phased()
for res in result:
res["name"] = name
self.data.extend(result)
else:
result = p.run()
if result is not None:
result["name"] = name
self.data.append(result)
def __init__(self, cols):
self._rows = []
self.logger = logging.Logger.manager.getLogger(self.__class__.__name__)
#self.prio_rows = {}
self.cols = cols
self._passes = 0 # perf counter to see how many tests we do on a given table
self.perf = Perf(self.logger)
def optimize(self, nIter):
if self._t is None:
self._t0 = time.time()
self._t = self._t0
pAsk = Perf()
pTell = Perf()
pPhi = MultiPerf()
for _ in range(nIter):
pAsk.start()
ws = np.array(self._es.ask())
pAsk.stop()
phis = self._phi(pPhi, ws)
pTell.start()
self._es.tell(phis)
pTell.stop()
self._iReport += 1
if self._iReport == self._nReport:
t = time.time()
print(
"EVAL: iter = %d t = %.2f dt = %.4f phi = %.4e wmn = %.4f wmx = %.4f sigma = %.4e nEvalsPerW = %d"
% (self._nIter, t - self._t0, t - self._t, phis.mean(),
ws.mean(), np.abs(ws).max(), self._es.getSigma(),
self._nEvalsThisTime))
self._phiLatest = phis.mean()
perf = self._collectPerf()
print("PERF: pA = %.3f pT = %.3f pP = %s %s" %
(1000 * pAsk.mean(), 1000 * pTell.mean(), ' '.join(
["%.3f" % (1000 * p) for p in pPhi.means()]), perf))
self._t = t
self._nIter += 1
self._trace.append(phis.mean())
self._iReport = 0
self._wfav = np.array(self._es.getXFavorite())
self._bbo.setParams(self._wfav)
def reset(self):
# This ensures that the traffic arrays (which size is dynamic)
# are all reset as well, so all lat,lon,sdp etc but also objects adsb
super(Traffic, self).reset()
self.ntraf = 0
# Reset models
self.wind.clear()
# Build new modules for area and turbulence
self.area = Area()
self.Turbulence = Turbulence()
# Noise (turbulence, ADBS-transmission noise, ADSB-truncated effect)
self.setNoise(False)
# Default: BlueSky internal performance model.
# Insert your BADA files to the folder "BlueSky/data/coefficients/BADA"
# for working with EUROCONTROL`s Base of Aircraft Data revision 3.12
self.perf = Perf()
self.trails.reset()
from logger import create_logger
if __name__ == "__main__":
logger = create_logger()
cmd = "/home/prathyushpv/work/cpu2017/bin/runcpu --config=config1.cfg --threads 1 "
benchmarks = [
'607.cactuBSSN_s'
'619.lbm_s', '621.wrf_s', '627.cam4_s', '628.pop2_s', '638.imagick_s',
'644.nab_s', '649.fotonik3d_s', '654.roms_s'
]
counters = [
'LLC-load-misses', 'LLC-store-misses', 'instructions',
'L1-dcache-loads', 'L1-dcache-stores'
]
for benchmark in benchmarks:
p = Perf(counters, cmd + benchmark)
result = p.run_continuous()
size = min([len(counter_values) for counter_values in result.values()])
y_mem = [(result["LLC-load-misses"][i] + result["LLC-store-misses"][i])
for i in range(size)]
y_cpu = [(result["instructions"][i] - result["L1-dcache-loads"][i] -
result["L1-dcache-stores"][i]) / 1000 for i in range(size)]
x = range(size)
print(y_mem)
print(y_cpu)
fig = plt.figure()
ax = fig.add_subplot(
1,
1,