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Updating energy_model to mitigate cluster power issue

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A quadratic is now fitted to single and two-core power measured across
frequencies. This quadratic is then used in projection of cluster power.
This mitigates issues with cluster powers going negative or "crossing
over".
This commit is contained in:
Sergei Trofimov 2015-07-24 08:11:23 +01:00
parent 6ced04daf0
commit 0db14e2466
1 changed files with 40 additions and 20 deletions
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60
wlauto/instrumentation/energy_model/__init__.py
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@ -21,7 +21,6 @@ import math
import time
from tempfile import mktemp
from base64 import b64encode
from copy import deepcopy
from collections import Counter, namedtuple
try:
@ -48,7 +47,8 @@ from wlauto.utils.types import caseless_string, list_or_caseless_string, list_of
from wlauto.utils.misc import list_to_mask
FREQ_TABLE_FILE = 'frequency_power_perf_data.csv'
CPUS_TABLE_FILE = 'cap_power.csv'
CPUS_TABLE_FILE = 'projected_cap_power.csv'
MEASURED_CPUS_TABLE_FILE = 'measured_cap_power.csv'
IDLE_TABLE_FILE = 'idle_power_perf_data.csv'
REPORT_TEMPLATE_FILE = 'report.template'
EM_TEMPLATE_FILE = 'em.template'
@ -160,7 +160,7 @@ def generate_em_c_file(em, big_core, little_core, em_template_file, outfile):
return em_text
def generate_report(freq_power_table, cpus_table, idle_power_table,
def generate_report(freq_power_table, measured_cpus_table, cpus_table, idle_power_table,
report_template_file, device_name, em_text, outfile):
# pylint: disable=too-many-locals
cap_power_analysis = PowerPerformanceAnalysis(freq_power_table)
@ -171,7 +171,8 @@ def generate_report(freq_power_table, cpus_table, idle_power_table,
fig, axes = plt.subplots(1, 2)
fig.set_size_inches(16, 8)
for i, cluster in enumerate(reversed(cpus_table.columns.levels[0])):
plot_cpus_table(cpus_table[cluster].dropna(), axes[i], cluster)
projected = cpus_table[cluster].dropna()
plot_cpus_table(projected, axes[i], cluster)
cpus_plot_data = get_figure_data(fig)
with open(report_template_file) as fh:
@ -272,7 +273,7 @@ def get_cap_power_plot(data_single_core):
def get_idle_power_plot(df):
fig, axes = plt.subplots(1, 2, figsize=(15, 7))
for cluster, ax in zip(['big', 'little'], axes):
for cluster, ax in zip(['little', 'big'], axes):
data = df[df.cluster == cluster].pivot_table(index=['state'], columns='cpus', values='power')
err = df[df.cluster == cluster].pivot_table(index=['state'], columns='cpus', values='power_error')
data.plot(kind='bar', ax=ax, rot=30, yerr=err)
@ -282,30 +283,45 @@ def get_idle_power_plot(df):
return fig
def fit_polynomial(s, n):
coeffs = np.polyfit(s.index, s.values, n)
poly = np.poly1d(coeffs)
return poly(s.index)
def get_cpus_power_table(data, index):
power_table = data[[index, 'cluster', 'cpus', 'power']].pivot_table(index=index,
columns=['cluster', 'cpus'],
values='power')
bs_power_table = pd.DataFrame(index=power_table.index, columns=power_table.columns)
for cluster in power_table.columns.levels[0]:
power_table[cluster, 0] = (power_table[cluster, 1] -
(power_table[cluster, 2] -
power_table[cluster, 1]))
bs_power_table[cluster, 1][power_table[cluster, 1].notnull()] = fit_polynomial(power_table[cluster, 1].dropna(), 2)
bs_power_table[cluster, 2][power_table[cluster, 2].notnull()] = fit_polynomial(power_table[cluster, 2].dropna(), 2)
bs_power_table[cluster, 0] = (bs_power_table[cluster, 1] -
(bs_power_table[cluster, 2] -
bs_power_table[cluster, 1]))
# re-order columns and rename colum '0' to 'cluster'
power_table = power_table[sorted(power_table.columns,
cmp=lambda x, y: cmp(y[0], x[0]) or cmp(x[1], y[1]))]
bs_power_table = bs_power_table[sorted(bs_power_table.columns,
cmp=lambda x, y: cmp(y[0], x[0]) or cmp(x[1], y[1]))]
old_levels = power_table.columns.levels
power_table.columns.set_levels([old_levels[0], list(map(str, old_levels[1])[:-1]) + ['cluster']],
inplace=True)
return power_table
bs_power_table.columns.set_levels([old_levels[0], list(map(str, old_levels[1])[:-1]) + ['cluster']],
inplace=True)
return power_table, bs_power_table
def plot_cpus_table(cpus_table, ax, cluster):
cpus_table.T.plot(ax=ax, marker='o')
def plot_cpus_table(projected, ax, cluster):
projected.T.plot(ax=ax, marker='o')
ax.set_title('{} cluster'.format(cluster))
ax.set_xticklabels(cpus_table.columns)
ax.set_xticklabels(projected.columns)
ax.set_xticks(range(0, 5))
ax.set_xlim(-0.5, len(cpus_table.columns) - 0.5)
ax.set_xlim(-0.5, len(projected.columns) - 0.5)
ax.set_ylabel('Power (mW)')
ax.grid(True)
@ -514,7 +530,11 @@ class EnergyModelInstrument(Instrument):
freq_power_table.to_csv(wfh, index=False)
context.add_artifact('freq_power_table', freq_output, 'export')
cpus_table = get_cpus_power_table(freq_power_table, 'frequency')
measured_cpus_table, cpus_table = get_cpus_power_table(freq_power_table, 'frequency')
measured_cpus_output = os.path.join(output_directory, MEASURED_CPUS_TABLE_FILE)
with open(measured_cpus_output, 'w') as wfh:
measured_cpus_table.to_csv(wfh)
context.add_artifact('measured_cpus_table', measured_cpus_output, 'export')
cpus_output = os.path.join(output_directory, CPUS_TABLE_FILE)
with open(cpus_output, 'w') as wfh:
cpus_table.to_csv(wfh)
@ -527,9 +547,9 @@ class EnergyModelInstrument(Instrument):
context.add_artifact('em', em_file, 'data')
report_file = os.path.join(output_directory, 'report.html')
generate_report(freq_power_table, cpus_table, idle_power_table,
self.report_template_file, self.device_name, em_text,
report_file)
generate_report(freq_power_table, measured_cpus_table, cpus_table,
idle_power_table, self.report_template_file,
self.device_name, em_text, report_file)
context.add_artifact('pm_report', report_file, 'export')
def initialize_result_tracking(self):
@ -764,8 +784,8 @@ if __name__ == '__main__':
em_template_file = os.path.join(this_dir, EM_TEMPLATE_FILE)
freq_power_table = pd.read_csv(os.path.join(indir, FREQ_TABLE_FILE))
cpus_table = pd.read_csv(os.path.join(indir, CPUS_TABLE_FILE),
header=range(2), index_col=0)
measured_cpus_table, cpus_table = pd.read_csv(os.path.join(indir, CPUS_TABLE_FILE),
header=range(2), index_col=0)
idle_power_table = pd.read_csv(os.path.join(indir, IDLE_TABLE_FILE))
if not os.path.exists(outdir):
@ -777,6 +797,6 @@ if __name__ == '__main__':
idle_power_table, first_cluster_idle_state)
em_text = generate_em_c_file(em, big_core, little_core,
em_template_file, em_file)
generate_report(freq_power_table, cpus_table, idle_power_table,
report_template_file, device_name, em_text,
report_file)
generate_report(freq_power_table, measured_cpus_table, cpus_table,
idle_power_table, report_template_file, device_name,
em_text, report_file)