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energy_model: added energy_metric

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It is now possible to specify energy_metric instead of power_metric for
cases where continuous power is not available but energy can be
measured.
This commit is contained in:
Sergei Trofimov 2015-04-28 14:16:22 +01:00
parent c5b884cf81
commit 68ae8b9277
1 changed files with 41 additions and 9 deletions
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50
wlauto/instrumentation/energy_model/__init__.py
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@ -2,6 +2,7 @@
from __future__ import division
import os
import math
import time
from tempfile import mktemp
from base64 import b64encode
from copy import deepcopy
@ -319,10 +320,18 @@ class EnergyModelInstrument(Instrument):
one of the values in ``device.core_names``. """),
Parameter('performance_metric', kind=caseless_string, mandatory=True,
description="""Metric to be used as the performance indicator."""),
Parameter('power_metric', kind=list_or_caseless_string, mandatory=True,
Parameter('power_metric', kind=list_or_caseless_string,
description="""Metric to be used as the power indicator. The value may contain a
``{core}`` format specifier that will be replaced with names of big
and little cores to drive the name of the metric for that cluster."""),
and little cores to drive the name of the metric for that cluster.
Ether this or ``energy_metric`` must be specified but not both."""),
Parameter('energy_metric', kind=list_or_caseless_string,
description="""Metric to be used as the energy indicator. The value may contain a
``{core}`` format specifier that will be replaced with names of big
and little cores to drive the name of the metric for that cluster.
this metric will be used to derive power by deviding through by
execution time. Either this or ``power_metric`` must be specified, but
not both."""),
Parameter('power_scaling_factor', kind=float, default=1.0,
description="""Power model specfies power in milliWatts. This is a scaling factor that
power_metric values will be multiplied by to get milliWatts."""),
@ -334,7 +343,6 @@ class EnergyModelInstrument(Instrument):
description="""List of frequencies to be used for little cores. These frequencies must
be supported by the cores. If this is not specified, all available
frequencies for the core (as read from cpufreq) will be used."""),
Parameter('idle_workload', kind=str, default='idle',
description="Workload to be used while measuring idle power."),
Parameter('idle_workload_params', kind=dict, default={},
@ -354,6 +362,8 @@ class EnergyModelInstrument(Instrument):
message = 'The Device does not appear to support {}; does it have the right module installed?'
raise ConfigError(message.format(capability))
device_cores = set(self.device.core_names)
if (self.power_metric and self.energy_metric) or not (self.power_metric or self.energy_metric):
raise ConfigError('Either power_metric or energy_metric must be specified (but not both).')
if not device_cores:
raise ConfigError('The Device does not appear to have core_names configured.')
elif len(device_cores) != 2:
@ -386,6 +396,12 @@ class EnergyModelInstrument(Instrument):
else:
idle_state.disable = 1
def fast_start(self, context): # pylint: disable=unused-argument
self.start_time = time.time()
def fast_stop(self, context): # pylint: disable=unused-argument
self.run_time = time.time() - self.start_time
def on_iteration_start(self, context):
self.setup_measurement(context.spec.cluster)
@ -403,6 +419,10 @@ class EnergyModelInstrument(Instrument):
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 (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:
power_metric += metric.value / self.run_time * self.power_scaling_factor
if not (power_metric and (perf_metric or not is_freq_iteration)):
message = 'Incomplete results for {} iteration{}'
@ -466,12 +486,24 @@ class EnergyModelInstrument(Instrument):
def initialize_result_tracking(self):
self.freq_data = []
self.idle_data = []
if isinstance(self.power_metric, basestring):
self.big_power_metrics = [self.power_metric.format(core=self.big_core)]
self.little_power_metrics = [self.power_metric.format(core=self.little_core)]
else:
self.big_power_metrics = [pm.format(core=self.big_core) for pm in self.power_metric]
self.little_power_metrics = [pm.format(core=self.little_core) for pm in self.power_metric]
self.big_power_metrics = []
self.little_power_metrics = []
self.big_energy_metrics = []
self.little_energy_metrics = []
if self.power_metric:
if isinstance(self.power_metric, basestring):
self.big_power_metrics = [self.power_metric.format(core=self.big_core)]
self.little_power_metrics = [self.power_metric.format(core=self.little_core)]
else:
self.big_power_metrics = [pm.format(core=self.big_core) for pm in self.power_metric]
self.little_power_metrics = [pm.format(core=self.little_core) for pm in self.power_metric]
else: # must be energy_metric
if isinstance(self.energy_metric, basestring):
self.big_energy_metrics = [self.energy_metric.format(core=self.big_core)]
self.little_energy_metrics = [self.energy_metric.format(core=self.little_core)]
else:
self.big_energy_metrics = [em.format(core=self.big_core) for em in self.energy_metric]
self.little_energy_metrics = [em.format(core=self.little_core) for em in self.energy_metric]
def configure_clusters(self):
self.measured_cores = None