def visualizePowerModel(path):
# Plot the measured values and the model
pw_model = powerModel(path)
freqs = get_range(args.freqs) if args.freqs else pw_model.frequencies
thrs = get_range(args.thrs) if args.thrs else pw_model.threads
est = pw_model.estimate(freqs, thrs)
plotData.setProps(xlabel="Frequencies (GHz)",
ylabel="Active Cores",
zlabel="Power (W)")
plotData.plot3D(x=pw_model.frequencies,
y=pw_model.threads,
z=pw_model.powers,
legend='Measurements')
plotData.plot3D(x=freqs, y=thrs, z=est, points=False, legend='Model')
plotData.plotShow(showLegend=True)
def update_data(val):
d = int(val)
plotData.setProps(xlabel='Frequencies (GHz)',
ylabel='Active threads',
zlabel='Time (S)',
title=title_)
df_ = df[df['in_cat'] == d]
df_pred_ = df_pred[df_pred['in_cat'] == d]
df_ = df_[df_['thr'].isin(df_pred_['thr'].unique())]
if not df_.empty:
plotData.plot3D(x=df_['freq'].unique(),
y=df_['thr'].unique(),
z=df_['time'].values)
plotData.plot3D(x=df_pred_['freq'].unique(),
y=df_pred_['thr'].unique(),
z=df_pred_['time'].values,
points=False)
def figures(appname=None, energy=True, in_cmp=3):
from energyOptimal import plotData
for app, title in zip(parsec_models, titles):
if (appname and not appname in app) or (not app):
continue
pw_model = powerModel('data/ipmi_2-32_cpuload.pw')
perf_model = performanceModel('data/dataframes/' + app,
'data/svr/' + app)
en_model = energyModel(pw_model,
perf_model,
freq_range_=np.arange(1.2e6, 2.3e6, 0.1e6) /
1e6)
plotData.setProps(xlabel='Frequencies (GHz)',
ylabel='Active threads',
zlabel='Energy (kJ)' if energy else 'Time (s)',
title=title)
df_ = perf_model.dataFrame[perf_model.dataFrame['in_cat'] ==
in_cmp].sort_values(['freq', 'thr'])
df_pred_ = en_model.dataFrame[en_model.dataFrame['in_cat'] ==
in_cmp].sort_values(['freq', 'thr'])
# df_pred_= df_pred_[df_pred_['thr'].isin(list(range(8,33,2)))]
# df_= df_[df_['thr'].isin(list(range(8,33,2)))]
plotData.plot3D(x=df_['freq'].unique(),
y=df_['thr'].unique(),
z=df_['energy'].values /
1e3 if energy else df_['time'].values,
points=True,
legend='Measurements')
plotData.plot3D(x=df_pred_['freq'].unique(),
y=df_pred_['thr'].unique(),
z=df_pred_['energy_model'].values /
1e3 if energy else df_pred_['time'].values,
points=False,
legend='Model')
plotData.ax.view_init(30, 60)
if 'HPL' in app:
plotData.ax.set_zlim(0, 15)
aux = 'energy' if energy else 'time'
plotData.savePlot('fotos/{}/{}.png'.format(aux, app), showLegend=True)
def update_data(val):
d = int(val)
plotData.setProps(xlabel='Frequencies (GHz)',
ylabel='Active threads',
zlabel='Energy (KJ)',
title=args.title)
df_pred_ = df_pred[df_pred['in_cat'] == d]
df_ = df[df['in_cat'] == d]
df_ = df_[df_['thr'].isin(df_pred_['thr'].unique())]
plotData.ax.view_init(30, 60)
if not df_.empty:
plotData.plot3D(x=df_['freq'].unique(),
y=df_['thr'].unique(),
z=df_['energy'].values / 1e3,
points=True,
legend='Measurements')
plotData.plot3D(x=df_pred_['freq'].unique(),
y=df_pred_['thr'].unique(),
z=df_pred_['energy_model'].values / 1e3,
points=False,
legend='Model')
freqs_filter=freq,
thrs_filter=thr)
pw_model.fit()
f_est, p_est = pw_model_full.frequencies, pw_model_full.threads
pws_est = pw_model.estimate(f_est, p_est)
df_est = pw_model.estimate(f_est, p_est, dataFrame=True)
df_full = pw_model_full.get_Dataframe()
df = pd.merge(df_est, df_full)
df['diff'] = np.abs((df['pw_est'] - df['pw']) / df['pw'] * 100)
print("Max error from all measured points {} ".format(
df['diff'].max()))
plotData.setProps(xlabel='Frequency', ylabel='Threads', zlabel='Power')
plotData.plot3D(pw_model_full.frequencies,
pw_model_full.threads,
pw_model_full.powers,
color_='blue',
legend='Measurement')
plotData.plot3D(pw_model.frequencies,
pw_model.threads,
pw_model.powers,
color_='red',
legend='Chosen points')
plotData.plot3D(f_est,
p_est,
pws_est,
points=False,
legend='Estimative')