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Merge pull request #33 from vflouris/master

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Energy model instruments: allows power adjustment for thermal effect
This commit is contained in:
setrofim
2015-06-29 14:19:20 +01:00
parent b018adac11 7f5952aa9c
commit b3c9d43ccd
1 changed files with 44 additions and 1 deletions
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45
wlauto/instrumentation/energy_model/__init__.py
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@@ -371,6 +371,9 @@ class EnergyModelInstrument(Instrument):
Parameter('no_hotplug', kind=bool, default=False,
description='''This options allows running the instrument without hotpluging cores on and off.
Disabling hotplugging will most likely produce a less accurate power model.'''),
Parameter('num_of_freqs_to_thermal_adjust', kind=int, default=0,
description="""The number of frequencies begining from the highest, to be adjusted for
the thermal effect."""),
]
def validate(self):
@@ -394,6 +397,9 @@ class EnergyModelInstrument(Instrument):
self.big_core = self.device.core_names[-1] # the last core is usually "big" in existing big.LITTLE devices
if not self.device_name:
self.device_name = self.device.name
if self.num_of_freqs_to_thermal_adjust and not instrument_is_installed('daq'):
self.logger.warn('Adjustment for thermal effect requires daq instrument. Disabling adjustment')
self.num_of_freqs_to_thermal_adjust = 0
def initialize(self, context):
self.number_of_cpus = {}
@@ -426,20 +432,38 @@ class EnergyModelInstrument(Instrument):
def on_iteration_start(self, context):
self.setup_measurement(context.spec.cluster)
def thermal_correction(self, context):
if not self.num_of_freqs_to_thermal_adjust or self.num_of_freqs_to_thermal_adjust > len(self.big_frequencies):
return 0
freqs = self.big_frequencies[-self.num_of_freqs_to_thermal_adjust:]
spec = context.result.spec
if spec.frequency not in freqs:
return 0
data_path = os.path.join(context.output_directory, 'daq', '{}.csv'.format(self.big_core))
data = pd.read_csv(data_path)['power']
return _adjust_for_thermal(data, filt_method=lambda x: pd.rolling_median(x, 1000), thresh=0.9, window=5000)
# slow to make sure power results have been generated
def slow_update_result(self, context):
def slow_update_result(self, context): # pylint: disable=too-many-branches
spec = context.result.spec
cluster = spec.cluster
is_freq_iteration = spec.label.startswith('freq_')
perf_metric = 0
power_metric = 0
thermal_adjusted_power = 0
if is_freq_iteration and cluster == 'big':
thermal_adjusted_power = self.thermal_correction(context)
for metric in context.result.metrics:
if metric.name == self.performance_metric:
perf_metric = metric.value
elif thermal_adjusted_power and metric.name in self.big_power_metrics:
power_metric += thermal_adjusted_power * self.power_scaling_factor
elif (cluster == 'big') and metric.name in self.big_power_metrics:
power_metric += metric.value * self.power_scaling_factor
elif (cluster == 'little') and metric.name in self.little_power_metrics:
power_metric += metric.value * self.power_scaling_factor
elif thermal_adjusted_power and metric.name in self.big_energy_metrics:
power_metric += thermal_adjusted_power / self.run_time * self.power_scaling_factor
elif (cluster == 'big') and metric.name in self.big_energy_metrics:
power_metric += metric.value / self.run_time * self.power_scaling_factor
elif (cluster == 'little') and metric.name in self.little_energy_metrics:
@@ -708,6 +732,25 @@ class EnergyModelInstrument(Instrument):
return self.little_frequencies
def _adjust_for_thermal(data, filt_method=lambda x: x, thresh=0.9, window=5000, tdiff_threshold=10000):
n = filt_method(data)
n = n[~np.isnan(n)] # pylint: disable=no-member
d = np.diff(n) # pylint: disable=no-member
d = d[~np.isnan(d)] # pylint: disable=no-member
dmin = min(d)
dmax = max(d)
index_up = np.max((d > dmax * thresh).nonzero()) # pylint: disable=no-member
index_down = np.min((d < dmin * thresh).nonzero()) # pylint: disable=no-member
low_average = np.average(n[index_up:index_up + window]) # pylint: disable=no-member
high_average = np.average(n[index_down - window:index_down]) # pylint: disable=no-member
if low_average > high_average or index_down - index_up < tdiff_threshold:
return 0
else:
return low_average
if __name__ == '__main__':
import sys
indir, outdir = sys.argv[1], sys.argv[2]