# create legend
methods = []
artists = []
for method in benchmarks.method2format:
methods.append(method)
artists.append(
Line2D((0,0),(0,1),
**benchmarks.method2format[method]))
legend()
####################
# COMPLEXITY
####################
data = benchmarks.read('complexity.xml')
all_methods = data['methods']
all_cores = data['cores']
all_tolerances = data['tolerances']
all_charges = data['charges']
timing = data['timing']
cores = 1
ixcor = all_cores.index(cores)
tolerance = 1e-3
ixtol = all_tolerances.index(tolerance)
figure('Complexity')
for method in data['methods']:
time_per_particle = \
timing[method][:, ixtol, ixcor] / all_charges
import benchmarks
import matplotlib.pylab as plt
from matplotlib.image import NonUniformImage
import numpy
tolerance = 1e-3
interpolation='bicubic'
levels = numpy.arange(-4,4)
all_charges, all_tolerances, all_cores, timing, speedup, efficiency = \
benchmarks.read('3d-periodic/bm_cloud_wall_jugene.xml')
ixtol = all_tolerances.index(tolerance)
charges = numpy.array(all_charges)[::-1]
cores = numpy.array(all_cores)
Z = numpy.log(timing['p2nfft'][:,ixtol,:])
plt.subplot(221)
plt.imshow(Z, origin='lower', interpolation='nearest')
cbar = plt.colorbar(ticks=levels)
cbar.ax.set_yticklabels(10.0**levels)
plt.xticks(numpy.arange(len(cores)), cores)
plt.xlabel('#cores')
plt.yticks(numpy.arange(len(charges)), charges[::-1])
plt.ylabel('#charges')
plt.grid(True)
plt.subplot(223)
plt.contourf(Z, levels=levels, origin='lower', interpolation=interpolation)
cbar = plt.colorbar(ticks=levels)
cbar.ax.set_yticklabels(10.0**levels)
import benchmarks
from matplotlib.pylab import *
from numpy import *
testcase = 'cloud_wall_scaling_jugene.xml'
all_charges, all_tolerances, all_cores, timing = benchmarks.read(testcase)
def plot_timing(testcase, charges, tolerance):
ixcha = all_charges.index(charges)
ixtol = all_tolerances.index(tolerance)
methods = timing.keys()
methods.sort()
for method in methods:
data = timing[method]
if not all(isnan(data[ixcha, ixtol, :])):
loglog(all_cores,
data[ixcha, ixtol, :],
label=method,
**benchmarks.fmt(method))
# legend()
xlabel('#cores')
ylabel('Time [s]')
def plot_efficiency(testcase, charges, tolerance):
ixcha = all_charges.index(charges)
def create_legend():
# create legend
methods = []
artists = []
for method in benchmarks.method2format:
methods.append(method)
artists.append(
Line2D((0, 0), (0, 1), **benchmarks.method2format[method]))
legend()
####################
# COMPLEXITY
####################
data = benchmarks.read('complexity.xml')
all_methods = data['methods']
all_cores = data['cores']
all_tolerances = data['tolerances']
all_charges = data['charges']
timing = data['timing']
cores = 1
ixcor = all_cores.index(cores)
tolerance = 1e-3
ixtol = all_tolerances.index(tolerance)
figure('Complexity')
for method in data['methods']:
time_per_particle = \
timing[method][:, ixtol, ixcor] / all_charges
import benchmarks
from matplotlib.pylab import *
from numpy import *
testcase = 'cloud_wall_scaling_jugene.xml'
all_charges, all_tolerances, all_cores, timing = benchmarks.read(testcase)
def plot_timing(testcase, charges, tolerance):
ixcha = all_charges.index(charges)
ixtol = all_tolerances.index(tolerance)
methods = timing.keys()
methods.sort()
for method in methods:
data = timing[method]
if not all(isnan(data[ixcha,ixtol,:])):
loglog(all_cores, data[ixcha,ixtol,:],
label=method, **benchmarks.fmt(method))
# legend()
xlabel('#cores')
ylabel('Time [s]')
def plot_efficiency(testcase, charges, tolerance):
ixcha = all_charges.index(charges)
ixtol = all_tolerances.index(tolerance)
methods = timing.keys()
methods.sort()
if minimal_time is None:
print "No data for charges={} tolerance={}".format(charges, tolerance)
return
for method in methods:
data = timing[method]
if not all(isnan(data[ixcha,ixtol,:])):
semilogx(all_cores, minimal_time/(data[ixcha,ixtol,:]*all_cores),
label=method, **benchmarks.fmt(method))
# legend()
xlim((1, charges/200.))
xlabel('#cores')
ylabel('Efficiency')
all_charges, all_tolerances, all_cores, timing = \
benchmarks.read('silica_melt_scaling_jugene.xml')
sp = 1
for charges in all_charges:
subplot(2, 3, sp)
sp += 1
title('Jugene {} charges'.format(charges))
plot_efficiency(charges, 1e-3)
subplot(231)
legend()
#tight_layout()
show()
print "No data for charges={} tolerance={}".format(charges, tolerance)
return
for method in methods:
data = timing[method]
if not all(isnan(data[ixcha,ixtol,:])):
semilogx(all_cores, minimal_time/(data[ixcha,ixtol,:]*all_cores),
label=method, **benchmarks.fmt(method))
# legend()
xlim((1, charges/200.))
xlabel('#cores')
ylabel('Efficiency')
all_charges, all_tolerances, all_cores, timing = \
benchmarks.read('silica_melt_scaling_juropa.xml')
sp = 1
for charges in all_charges:
subplot(2, 3, sp)
sp += 1
title('Juropa, {} charges'.format(charges))
plot_efficiency(charges, 1e-3)
# create legend
methods = []
artists = []
for method in benchmarks.method2format:
methods.append(method)
artists.append(
Line2D((0,0),(0,1),
return
for method in methods:
data = timing[method]
if not all(isnan(data[ixcha, ixtol, :])):
semilogx(all_cores,
minimal_time / (data[ixcha, ixtol, :] * all_cores),
label=method,
**benchmarks.fmt(method))
# legend()
xlim((1, charges / 200.))
xlabel('#cores')
ylabel('Efficiency')
all_charges, all_tolerances, all_cores, timing = \
benchmarks.read('silica_melt_scaling_jugene.xml')
sp = 1
for charges in all_charges:
subplot(2, 3, sp)
sp += 1
title('Jugene {} charges'.format(charges))
plot_efficiency(charges, 1e-3)
subplot(231)
legend()
#tight_layout()
show()
import benchmarks
import matplotlib.pylab as plt
from matplotlib.image import NonUniformImage
import numpy
tolerance = 1e-3
interpolation = 'bicubic'
levels = numpy.arange(-4, 4)
all_charges, all_tolerances, all_cores, timing, speedup, efficiency = \
benchmarks.read('3d-periodic/bm_cloud_wall_jugene.xml')
ixtol = all_tolerances.index(tolerance)
charges = numpy.array(all_charges)[::-1]
cores = numpy.array(all_cores)
Z = numpy.log(timing['p2nfft'][:, ixtol, :])
plt.subplot(221)
plt.imshow(Z, origin='lower', interpolation='nearest')
cbar = plt.colorbar(ticks=levels)
cbar.ax.set_yticklabels(10.0**levels)
plt.xticks(numpy.arange(len(cores)), cores)
plt.xlabel('#cores')
plt.yticks(numpy.arange(len(charges)), charges[::-1])
plt.ylabel('#charges')
plt.grid(True)
plt.subplot(223)
plt.contourf(Z, levels=levels, origin='lower', interpolation=interpolation)
cbar = plt.colorbar(ticks=levels)
cbar.ax.set_yticklabels(10.0**levels)
import benchmarks
from matplotlib.pylab import *
from numpy import *
testcase = 'cloud_wall_scaling_juropa.xml'
all_charges, all_tolerances, all_cores, timing = \
benchmarks.read(testcase)
def plot_timing(charges, tolerance):
ixcha = all_charges.index(charges)
ixtol = all_tolerances.index(tolerance)
methods = timing.keys()
methods.sort()
for method in methods:
data = timing[method]
if not all(isnan(data[ixcha,ixtol,:])):
plt.loglog(all_cores, data[ixcha,ixtol,:],
label=method, **benchmarks.fmt(method))
plt.legend()
plt.xlim((1, charges/200.))
plt.xlabel('#cores')
plt.ylabel('Time [s]')
def plot_efficiency(charges, tolerance):
ixcha = all_charges.index(charges)
ixtol = all_tolerances.index(tolerance)
for method in methods:
data = timing[method]
if not all(isnan(data[ixcha, ixtol, :])):
semilogx(all_cores,
minimal_time / (data[ixcha, ixtol, :] * all_cores),
label=method,
**benchmarks.fmt(method))
# legend()
xlim((1, charges / 200.))
xlabel('#cores')
ylabel('Efficiency')
all_charges, all_tolerances, all_cores, timing = \
benchmarks.read('silica_melt_scaling_juropa.xml')
sp = 1
for charges in all_charges:
subplot(2, 3, sp)
sp += 1
title('Juropa, {} charges'.format(charges))
plot_efficiency(charges, 1e-3)
# create legend
methods = []
artists = []
for method in benchmarks.method2format:
methods.append(method)
artists.append(Line2D((0, 0), (0, 1), **benchmarks.method2format[method]))