def plotall(name, strats, legend_loc):
lines = []
maxX = 0.0
maxY = 0.0
for experiment_id in ids:
problem_name = get.problem_name_of_experiment(experiment_id)
#print problem_name
(maxtime, bl_avg, norms) = ets(experiment_id, n_procs)
for strat in strats:
lab = pretty_bench_name(problem_name)
ns = norms
xys = []
for (input, pinput, avg, stddev) in ns:
if is_ebsap(input):
s = param_of_strategy(input)
(k, v) = s.split(",")
x = float(k)
else:
x = int(param_of_mls(input))
x = float(math.log(x, 2))
y = (bl_avg / avg)
#print (lab, avg, y, bl_avg)
xys.append((x, y))
maxX = max(x, maxX)
maxY = max(y, maxY)
xys.sort(cmp)
lines.append((lab, xys))
yaxvals = np.arange(0, 49, 8)
xaxlabs = []
for i in range(0, 15):
xaxlabs.append(('$2^{' + str(i) + '}$'))
line_plot.plot(
'mls' + name,
lines,
14,
49.0,
chart_title='',
connect_dots=True,
#formats=['r+', 'b+', 'r-', 'b-', 'rx', 'bx', 'r^', 'b^', 'ro', 'bo'],
linecolors=[
'#000000', 'r', 'b', 'g', 'b', 'r', 'b', 'r', 'g', 'r', 'b', 'g',
'r', 'b'
],
formats=['b+', 'b,', 'b.', 'b1', 'b2', 'b3', 'b4', 'b<', 'b>', 'b|'],
dashes=[
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False
],
yax_label='speedup',
xax_label='$M$',
dimensions=(35, 23),
legend_loc=legend_loc,
marker=(43.0, 4.0),
yaxvals=yaxvals,
xaxlabs=xaxlabs)
def plotall(name, leaf_sizes, legend_loc):
lines = []
maxX = 0.0
maxY = 0.0
for experiment_id in ids:
problem_name = get.problem_name_of_experiment(experiment_id)
#print problem_name
(maxtime, bl_avg, mlt_avg, mltlbsovhd, lbs_ovhd,
norms) = compare_wall_clock(experiment_id, n_procs)
lab = problem_name
ns = norms
xys = []
print problem_name
print ns
print bl_avg
print 'n_procs'
print n_procs
for (branch, input, pinput, avg, stddev, max_leaf_size) in ns:
if max_leaf_size == 500:
x = math.log(512, 2)
else:
x = math.log(max_leaf_size, 2)
y = (bl_avg / avg)
xys.append((x, y))
maxX = max(x, maxX)
maxY = max(y, maxY)
xys.sort(cmp)
print xys
lines.append((lab, xys))
yaxvals = np.arange(0, 49, 8)
xaxlabs = []
for i in range(0, 13):
xaxlabs.append(('$2^{' + str(i) + '}$'))
line_plot.plot(
name,
lines,
maxX,
48,
chart_title='',
connect_dots=True,
#formats=['r+', 'b+', 'r-', 'b-', 'rx', 'bx', 'r^', 'b^', 'ro', 'bo'],
formats=['b+', 'b,', 'b.', 'b1', 'b2', 'b3', 'b4', 'b<', 'b>', 'b|'],
dashes=[
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False
],
yax_label='speedup',
xax_label='leaf size',
dimensions=(35, 23),
legend_loc=legend_loc,
yaxvals=yaxvals,
xaxlabs=xaxlabs,
marker=(43.0, 4.0))
def plotall(name, strats, legend_loc):
lines = []
maxX = 0.0
maxY = 0.0
for experiment_id in ids:
problem_name = get.problem_name_of_experiment(experiment_id)
#print problem_name
(maxtime, bl_avg, mlt_avg, mltlbsovhd, lbs_ovhd,
norms) = compare_wall_clock(experiment_id, n_procs)
for strat in strats:
lab = problem_name
ns = [n for n in norms if id_of_strategy(n[0]) == strat[0]]
xys = []
for (input, pinput, avg, stddev) in ns:
if is_ebsap(input):
s = param_of_strategy(input)
(k, v) = s.split(",")
x = float(k)
else:
x = int(param_of_strategy(input))
x = float(math.log(x, 2))
# x=x-4.0
y = ((bl_avg / avg) / float(n_procs)) * 100.0
#print (lab, avg, y, bl_avg)
xys.append((x, y))
maxX = max(x, maxX)
maxY = max(y, maxY)
xys.sort(cmp)
lines.append((lab, xys))
line_plot.plot(
'tps' + name,
lines,
maxX,
102.0,
chart_title='',
connect_dots=True,
#formats=['r+', 'b+', 'r-', 'b-', 'rx', 'bx', 'r^', 'b^', 'ro', 'bo'],
linecolors=[
'#000000', 'r', 'b', 'g', 'b', 'r', 'b', 'r', 'g', 'r', 'b', 'g',
'r', 'b'
],
formats=['b+', 'b,', 'b.', 'b1', 'b2', 'b3', 'b4', 'b<', 'b>', 'b|'],
dashes=[
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False
],
yax_label='parallel efficiency',
xax_label='SST (log scale)',
dimensions=(35, 23),
legend_loc=legend_loc,
marker=(43.0, 4.0))
def plotall(name, leaf_sizes, legend_loc):
lines = []
maxX = 0.0
maxY = 0.0
for experiment_id in ids:
problem_name = get.problem_name_of_experiment(experiment_id)
#print problem_name
(maxtime, bl_avg, mlt_avg, mltlbsovhd, lbs_ovhd,
norms) = compare_wall_clock(experiment_id, n_procs)
lab = problem_name
ns = [n for n in norms if id_of_strategy(n[1]) == "lbs"]
xys = []
print problem_name
print ns
print bl_avg
for (branch, input, pinput, avg, stddev, max_leaf_size) in ns:
if max_leaf_size == 500:
x = math.log(512, 2)
else:
x = math.log(max_leaf_size, 2)
y = ((bl_avg / avg) / float(n_procs)) * 100.0
xys.append((x, y))
maxX = max(x, maxX)
maxY = max(y, maxY)
xys.sort(cmp)
print xys
lines.append((lab, xys))
line_plot.plot(
name,
lines,
maxX,
150.0,
chart_title='',
connect_dots=True,
#formats=['r+', 'b+', 'r-', 'b-', 'rx', 'bx', 'r^', 'b^', 'ro', 'bo'],
formats=['b+', 'b,', 'b.', 'b1', 'b2', 'b3', 'b4', 'b<', 'b>', 'b|'],
dashes=[
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False
],
yax_label='parallel efficiency',
xax_label='Max leaf size (log_2 scale)',
dimensions=(35, 23),
legend_loc=legend_loc,
marker=(43.0, 4.0))