def fullscale_comp(lang,
plot_std=True,
homedir=None,
cache_opt_default=False,
smem_default=False,
loc_override=None,
text_loc=None,
fontsize=10,
color_list=['b', 'g', 'r', 'k']):
if lang == 'c':
langs = ['c', 'tchem']
desc = 'cpu'
smem_default = False
elif lang == 'cuda':
langs = ['cuda']
desc = 'gpu'
else:
raise Exception('unknown lang {}'.format(lang))
def thefilter(x):
return x.cache_opt == cache_opt_default and x.smem == smem_default
fit_vals = []
data = get_data(homedir)
data = [x for x in data if x.lang in langs]
data = filter(thefilter, data)
data = get_fullscale(data)
if not len(data):
print 'no data found... exiting'
sys.exit(-1)
fig, ax = plt.subplots()
miny = None
linestyle = ''
fitvals = []
retdata = []
color_ind = 0
text_ind = None
if text_loc:
text_ind = 0
#FD
plotdata = [x for x in data if x.finite_difference]
if plotdata:
color = color_list[color_ind]
color_ind += 1
miny, they, thez = plot(plotdata,
FD_marker,
'Finite Difference',
miny,
return_y=True,
color=color)
theloc = None
if text_ind is not None:
theloc = text_loc[text_ind]
text_ind += 1
fitvals.append(
fit_order(plotdata,
they,
thez,
None,
color,
text_loc=theloc,
fontsize=fontsize))
retdata.append(they)
for lang in langs:
plotdata = [
x for x in data if not x.finite_difference and x.lang == lang
]
color = color_list[color_ind]
color_ind += 1
if plotdata:
name, marker = nice_names(plotdata[0])
miny, they, thez = plot(plotdata,
marker,
name,
miny,
return_y=True,
color=color)
theloc = None
if text_ind is not None:
theloc = text_loc[text_ind]
text_ind += 1
fitvals.append(
fit_order(plotdata,
they,
thez,
None,
color,
text_loc=theloc,
fontsize=fontsize))
retdata.append(they)
ax.set_yscale('log')
ax.set_ylim(ymin=miny * 0.95)
loc = 0 if loc_override is None else loc_override
ax.legend(loc=loc, numpoints=1, fontsize=10)
# add some text for labels, title and axes ticks
ax.set_ylabel('Mean evaluation time / condition (ms)')
#ax.set_title('GPU Jacobian Evaluation Performance for {} mechanism'.format(thedir))
ax.set_xlabel('Number of Reactions')
#ax.legend(loc=0)
plt.savefig('{}_norm.pdf'.format(desc))
plt.close()
return fitvals, retdata
# x position of text label, and multiplier for y position
label_locs = [(10, 1.75),
(10, 0.6),
(10, 1.75),
(10, 1.75)
]
if home_dir is None:
home_dir = os.path.join(sys.path[0], '../')
home_dir = os.path.realpath(home_dir)
d = os.path.join(home_dir, 'figures')
# Get CUDA pyJac datapoints, without cache optimization or shared memory
data = get_data()
plotdata = [x for x in data if x.lang == 'cuda'
and not x.cache_opt
and not x.smem
and not x.finite_difference
]
fig, ax = plt.subplots()
minx, miny = plot_scaling(plotdata, legend_markers, legend_colors,
label_locs=label_locs
)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylim(ymin=miny*0.85)
ax.set_xlim(xmin=minx*0.85)
#ax.legend(loc=0, numpoints=1, frameon=False)
def fullscale_comp(lang, plot_std=True, homedir=None,
cache_opt_default=False,
smem_default=False,
loc_override=None,
text_loc=None, fontsize=10,
color_list=['b', 'g', 'r', 'k']):
if lang == 'c':
langs = ['c', 'tchem']
desc = 'cpu'
smem_default=False
elif lang == 'cuda':
langs = ['cuda']
desc = 'gpu'
else:
raise Exception('unknown lang {}'.format(lang))
def thefilter(x):
return x.cache_opt==cache_opt_default and x.smem == smem_default
fit_vals = []
data = get_data(homedir)
data = [x for x in data if x.lang in langs]
data = filter(thefilter, data)
data = get_fullscale(data)
if not len(data):
print 'no data found... exiting'
sys.exit(-1)
fig, ax = plt.subplots()
miny = None
linestyle = ''
fitvals = []
retdata = []
color_ind = 0
text_ind = None
if text_loc:
text_ind = 0
#FD
plotdata = [x for x in data if x.finite_difference]
if plotdata:
color = color_list[color_ind]
color_ind += 1
miny, they, thez = plot(plotdata, FD_marker, 'Finite Difference', miny, return_y=True, color=color)
theloc = None
if text_ind is not None:
theloc = text_loc[text_ind]
text_ind += 1
fitvals.append(
fit_order(plotdata, they, thez, None, color, text_loc=theloc, fontsize=fontsize)
)
retdata.append(they)
for lang in langs:
plotdata = [x for x in data if not x.finite_difference
and x.lang == lang]
color = color_list[color_ind]
color_ind += 1
if plotdata:
name, marker = nice_names(plotdata[0])
miny, they, thez = plot(plotdata, marker, name, miny, return_y=True, color=color)
theloc = None
if text_ind is not None:
theloc = text_loc[text_ind]
text_ind += 1
fitvals.append(
fit_order(plotdata, they, thez, None, color, text_loc=theloc, fontsize=fontsize)
)
retdata.append(they)
ax.set_yscale('log')
ax.set_ylim(ymin=miny*0.95)
loc = 0 if loc_override is None else loc_override
ax.legend(loc=loc, numpoints=1, fontsize=10)
# add some text for labels, title and axes ticks
ax.set_ylabel('Mean evaluation time / condition (ms)')
#ax.set_title('GPU Jacobian Evaluation Performance for {} mechanism'.format(thedir))
ax.set_xlabel('Number of Reactions')
#ax.legend(loc=0)
plt.savefig('{}_norm.pdf'.format(desc))
plt.close()
return fitvals, retdata
}
if hollow:
argdict['markerfacecolor'] = 'None'
argdict['label'] += ' (smem)'
if plot_std:
argdict['yerr'] = thez
if plot_std:
line = plt.errorbar(**argdict)
else:
line = plt.plot(**argdict)
return miny
legend_markers = ['o', 'v', 's', '>']
data = get_data()
plotdata = [
x for x in data if x.lang == 'cuda' and not x.cache_opt and not x.smem
and not x.finite_difference
]
fig, ax = plt.subplots()
miny = None
miny = plot_scaling(plotdata, legend_markers, miny)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylim(ymin=miny * 0.95)
ax.legend(loc=0, numpoints=1)
# add some text for labels, title and axes ticks
ax.set_ylabel('Mean evaluation time')
def fullscale_comp(lang, plot_std=True, homedir=None,
cache_opt_default=False,
smem_default=False,
loc_override=None,
text_loc=None,
color_list=['b', 'g', 'r', 'k']
):
if lang == 'c':
langs = ['c', 'tchem']
desc = 'cpu'
smem_default = False
elif lang == 'cuda':
langs = ['cuda']
desc = 'gpu'
else:
raise Exception('unknown lang {}'.format(lang))
def thefilter(x):
return (x.cache_opt==cache_opt_default and
x.smem == smem_default and
x.lang in langs
)
fit_vals = []
data = get_data(homedir)
data = [x for x in data if thefilter(x)]
data = get_fullscale(data)
if not len(data):
print('no data found... exiting')
sys.exit(-1)
fig, ax = plt.subplots()
linestyle = ''
fitvals = []
retdata = []
color_ind = 0
text_ind = None
if text_loc:
text_ind = 0
# finite difference
plotdata = [x for x in data if x.finite_difference]
if plotdata:
color = color_list[color_ind]
color_ind += 1
(minx, miny), y_vals, err_vals = plot(plotdata, FD_marker,
'Finite Difference',
return_y=True, color=color
)
theloc = None
if text_ind is not None:
theloc = text_loc[text_ind]
text_ind += 1
fitvals.append(
fit_order(plotdata, y_vals, err_vals, color=color, text_loc=theloc)
)
retdata.append(y_vals)
for lang in langs:
plotdata = [x for x in data if not x.finite_difference
and x.lang == lang
]
color = color_list[color_ind]
color_ind += 1
if plotdata:
name, marker = nice_names(plotdata[0])
(minx, miny), y_vals, err_vals = plot(plotdata, marker, name,
minx, miny,
return_y=True, color=color
)
theloc = None
if text_ind is not None:
theloc = text_loc[text_ind]
text_ind += 1
fitvals.append(
fit_order(plotdata, y_vals, err_vals, None, color, text_loc=theloc)
)
retdata.append(y_vals)
ax.set_yscale('log')
ax.set_ylim(ymin=miny*0.85)
ax.set_xlim(xmin=0)
loc = 0 if loc_override is None else loc_override
ax.legend(loc=loc, numpoints=1, fontsize=font_size,
shadow=True, fancybox=True
)
# add some text for labels, title and axes ticks
ax.set_ylabel('Mean evaluation time / condition (ms)', fontsize=font_size)
ax.set_xlabel('Number of Reactions', fontsize=font_size)
if homedir is None:
homedir = os.path.join(sys.path[0], '../')
homedir = os.path.realpath(homedir)
d = os.path.join(homedir, 'figures')
pp = PdfPages(os.path.join(d, '{}_performance_comparison.pdf'.format(desc)))
pp.savefig()
pp.close()
plt.close()
return fitvals, retdata
