def slowdown_cdf_big(args, datas):
args = args.args
L = int(args[0]) if args else 0
fig, ax = plt.subplots(nrows=1, ncols=1)
for number, data in enumerate(datas):
means = data.means
slowdowns = means / means[0, :]
graph = lnm.fromkeyvals(data.names, slowdowns)
graph = lnm.compute_lnm_times(graph, L)
results = graph.ungraph()[1]
results = zip(*results)
entries = means.shape[0]
for i, result in enumerate(results):
if i == 1:
continue
counts, bin_edges = np.histogram(result, bins=max(entries, 1024))
counts = counts * (100.0 / float(entries))
cdf = np.cumsum(counts)
ax.plot(bin_edges[:-1],
cdf,
LINESTYLES[number],
label=LABELS[i],
color=COLORS[i])
upper = 50
plt.xlim((1, upper))
ax.set_xticks([1] + range(5, upper + 1, 5))
ax.set_xticklabels(["1x"] +
["%dx" % i for i in range(5, upper + 1, 5)])
plt.ylim((0, 100))
def slowdown_cdf_big(args, datas):
args = args.args
L = int(args[0]) if args else 0
fig, ax = plt.subplots(nrows=1, ncols=1)
for number, data in enumerate(datas):
means = data.means
slowdowns = means / means[0,:]
graph = lnm.fromkeyvals(data.names, slowdowns)
graph = lnm.compute_lnm_times(graph, L)
results = graph.ungraph()[1]
results = zip(*results)
entries = means.shape[0]
for i, result in enumerate(results):
if i == 1:
continue
counts, bin_edges = np.histogram(result, bins=max(entries, 1024))
counts = counts * (100.0 / float(entries))
cdf = np.cumsum(counts)
ax.plot(bin_edges[:-1], cdf, LINESTYLES[number], label=LABELS[i], color=COLORS[i])
upper = 50
plt.xlim((1,upper))
ax.set_xticks([1] + range(5, upper + 1, 5))
ax.set_xticklabels(["1x"] + ["%dx" % i for i in range(5, upper + 1, 5)])
plt.ylim((0, 100))
def slowdown_cdf(args, datas):
assert datas
if not args.args:
LS = [0]
else:
LS = map(int, args.args)
if args.xmin is None:
xmin = 1
else:
xmin = args.xmin
if args.xmax is None:
xmax = 10
else:
xmax = args.xmax
names = datas[0].names
data = np.hstack([d.means for d in datas])
norm = args.norm and args.norm[0]
if norm is None or norm == -1:
norm = range(data.shape[-1])
else:
assert norm >= 0
slowdowns = data / data[0, norm]
systems = args.systems
if systems is not None:
slowdowns = slowdowns[:, systems]
colors = colors_array(args)
fig, ax = plt.subplots(nrows=1, ncols=1)
for number in LS:
graph = lnm.fromkeyvals(names, slowdowns)
graph = lnm.compute_lnm_times(graph, number)
results = graph.ungraph()[1]
results = zip(*results)
entries = data.shape[0]
for i, result in enumerate(results):
counts, bin_edges = np.histogram(result, bins=max(entries, 1024))
counts = counts * (100.0 / float(entries))
cdf = np.cumsum(counts)
ax.plot(bin_edges[:-1],
cdf,
LINESTYLES[number],
label=LABELS[i],
color=colors[i])
# plt.axvline(3, color=VLINE)
plt.xlim((xmin, xmax))
ax.set_xticks(range(1, xmax + 1))
ax.set_xticklabels(["%dx" % (i + 1) for i in range(xmax)])
plt.ylim((0, 100))
def slowdown_cdf(args, datas):
assert datas
if not args.args:
LS = [0]
else:
LS = map(int, args.args)
names = datas[0].names
data = np.hstack([d.means for d in datas])
norm = args.norm and args.norm[0]
if norm is None or norm == -1:
norm = range(data.shape[-1])
else:
assert norm >= 0
slowdowns = data / data[0,norm]
systems = args.systems
if systems is not None:
slowdowns = slowdowns[:,systems]
colors = colors_array(args)
fig, ax = plt.subplots(nrows=1, ncols=1)
for number in LS:
graph = lnm.fromkeyvals(names, slowdowns)
graph = lnm.compute_lnm_times(graph, number)
results = graph.ungraph()[1]
results = zip(*results)
entries = data.shape[0]
for i, result in enumerate(results):
counts, bin_edges = np.histogram(result, bins=max(entries, 1024))
counts = counts * (100.0 / float(entries))
cdf = np.cumsum(counts)
ax.plot(bin_edges[:-1], cdf, LINESTYLES[number], label=LABELS[i], color=colors[i])
upper = 10
plt.axvline(3, color=VLINE)
plt.xlim((1,upper))
ax.set_xticks(range(1, upper + 1))
ax.set_xticklabels(["%dx" % (i + 1) for i in range(upper)])
plt.ylim((0, 100))
def slowdown_cdf_hidden(args, datas):
if args.args:
upper = int(args.args[0])
else:
upper = 5
L = 0
LINESTYLES = ['-', ':']
fig, ax = plt.subplots(nrows=1, ncols=1)
for number, data in enumerate(datas):
means = data.means
slowdowns = means / means[0, :]
graph = lnm.fromkeyvals(data.names, slowdowns)
graph = lnm.compute_lnm_times(graph, L)
print np.sum(slowdowns < 3.0, axis=0)
results = graph.ungraph()[1]
results = zip(*results)
entries = means.shape[0]
for i, result in enumerate(results):
if args.systems is not None and i not in args.systems:
continue
median = np.median(result)
counts, bin_edges = np.histogram(result, bins=max(entries, 1024))
counts = counts * (100.0 / float(entries))
cdf = np.cumsum(counts)
ax.plot(bin_edges[:-1],
cdf,
LINESTYLES[number],
label=LABELS[i],
color=COLORS[i])
# plt.axvline(3, color=VLINE)
plt.xlim((1, upper))
ax.set_xticks(range(1, upper + 1))
ax.set_xticklabels(["%dx" % (i + 1) for i in range(upper)])
plt.ylim((0, 100))
def slowdown_cdf_hidden(args, datas):
if args.args:
upper = int(args.args[0])
else:
upper = 5
L = 0
LINESTYLES = ['-', ':']
fig, ax = plt.subplots(nrows=1, ncols=1)
for number, data in enumerate(datas):
means = data.means
slowdowns = means / means[0,:]
graph = lnm.fromkeyvals(data.names, slowdowns)
graph = lnm.compute_lnm_times(graph, L)
print np.sum(slowdowns < 3.0, axis=0)
results = graph.ungraph()[1]
results = zip(*results)
entries = means.shape[0]
for i, result in enumerate(results):
if args.systems is not None and i not in args.systems:
continue
median = np.median(result)
counts, bin_edges = np.histogram(result, bins=max(entries, 1024))
counts = counts * (100.0 / float(entries))
cdf = np.cumsum(counts)
ax.plot(bin_edges[:-1], cdf, LINESTYLES[number], label=LABELS[i], color=COLORS[i])
plt.axvline(3, color=VLINE)
plt.xlim((1,upper))
ax.set_xticks(range(1, upper + 1))
ax.set_xticklabels(["%dx" % (i + 1) for i in range(upper)])
plt.ylim((0, 100))
def slowdown_cdf(datas):
fig, ax = plt.subplots(nrows=1, ncols=1)
for number, data in enumerate(datas):
weights = [
np.array([1.0 / float(d.means.shape[0])] * d.means.shape[0])
for d in data
]
slowdowns = [d.means / d.means[0, :] for d in data]
graphs = [
lnm.fromkeyvals(d.names, slowdown)
for d, slowdown in zip(data, slowdowns)
]
graphs = [lnm.compute_lnm_times(g, L=1) for g in graphs]
slowdowns1 = [g.ungraph()[1] for g in graphs]
graphs = [
lnm.fromkeyvals(d.names, slowdown)
for d, slowdown in zip(data, slowdowns)
]
graphs = [lnm.compute_lnm_times(g, L=2) for g in graphs]
slowdowns2 = [g.ungraph()[1] for g in graphs]
largest_dims = max(*[s.shape[-1] for s in slowdowns])
_, slowdowns = pad_weights(weights, slowdowns)
_, slowdowns1 = pad_weights(weights, slowdowns1)
weights, slowdowns2 = pad_weights(weights, slowdowns2)
weights = np.vstack(weights)
all_data = np.vstack(slowdowns)
all_data1 = np.vstack(slowdowns1)
all_data2 = np.vstack(slowdowns2)
N = all_data.shape[-1]
for i in range(2):
entries = np.sum(weights[:, i])
result = all_data[:, i]
counts, bin_edges = np.histogram(result,
bins=len(result),
weights=weights[:, i])
cdf = np.cumsum(counts) / np.sum(entries) * 100.0
ax.plot(bin_edges[:-1],
cdf,
LINESTYLES[number],
label=LABELS[i],
color=COLORS[i])
# add lines for L=1
result = all_data1[:, i]
counts, bin_edges = np.histogram(result,
bins=len(result),
weights=weights[:, 1])
cdf = np.cumsum(counts) / entries * 100.0
ax.plot(bin_edges[:-1],
cdf,
LINESTYLES[number],
label=LABELS[i],
color=(0, 0, 0))
total_benchmarks = np.sum(weights, axis=0)
avg_slowdown_weighted = np.sum(weights * all_data,
axis=0) / total_benchmarks
avg_slowdown_weighted1 = np.sum(weights * all_data1,
axis=0) / total_benchmarks
s3 = np.sum(weights *
(all_data < 2.0), axis=0) * 100.0 / total_benchmarks
s4 = np.sum(weights *
(all_data1 < 2.0), axis=0) * 100.0 / total_benchmarks
s5 = np.sum(weights *
(all_data < 1.1), axis=0) * 100.0 / total_benchmarks
s6 = np.sum(weights *
(all_data1 < 1.1), axis=0) * 100.0 / total_benchmarks
s7 = np.sum(weights *
(all_data2 < 1.1), axis=0) * 100.0 / total_benchmarks
def rnd(x):
return round(x, 0)
if number != 0:
print "\multicolumn{8}{|c|}{%s} \\\\" % SUFFIXES[number]
print "\\hline"
for i in range(len(avg_slowdown_weighted)):
s1 = round(avg_slowdown_weighted[i], 2)
s2 = round(avg_slowdown_weighted1[i], 2)
print "%s & $%0.2f\\times$ & $%0.2f\\times$ & $%0.0f$ & $%0.0f$ & $%0.0f$ & $%0.0f$ & $%0.0f$ \\\\" % (
(LABELS[i].capitalize(), s1, s2) +
tuple(map(rnd, (s3[i], s4[i], s5[i], s6[i], s7[i]))))
print "\\hline"
plt.axvline(3, color=COLORS[-1])
plt.xlim((1, 10))
ax.set_xlabel("slowdown factor")
ax.set_ylabel("% of benchmarks")
ax.set_xticklabels(["%dx" % (i + 1) for i in range(10)])
plt.ylim((0, 100))
plt.savefig("figs/aggregate-cdf.pdf")
for number, data in enumerate(datas):
plt.cla()
weights = [np.ones(d.means.shape[0]) / d.means.shape[0] for d in data]
slowdowns = [d.means / d.means[0, 0] for d in data]
weights, slowdowns = pad_weights(weights, slowdowns)
weights = np.vstack(weights)
all_data = np.vstack(slowdowns)
for i in range(0, 2):
ax.scatter(all_data[:, 0],
all_data[:, i],
label=LABELS[i],
color=COLORS[i],
marker='.')
if i == 0:
continue
# m, b = np.polyfit(all_data[:,0], all_data[:,i], 1)
m, b, r, _, _ = linregress(all_data[:, 0], all_data[:, i])
x = np.vstack([np.arange(0, 100, 0.01), np.ones(10000)]).T
print "y = %f * x + %f : r^2 = %f" % (m, b, r**2)
y = m * x + b
ax.plot(x, y, color=COLORS[i + 2])
textX = np.max(all_data[:, 0]) / 2.0
textY = np.max(all_data[:, i]) / 1.8
plt.text(textX,
textY,
'$y = %0.3f x + %0.3f$ \n $r^2 = %0.3f$' % (m, b, r**2),
fontsize=20,
color='k',
horizontalalignment='center',
verticalalignment='bottom')
plt.legend(loc='upper left')
plt.ylim((0, 70))
plt.xlim((0, 70))
ax.set_xlabel("Racket gradual typing overhead")
ax.set_ylabel("overhead relative to Racket")
plt.savefig("figs/aggregate-slowdown-%d.pdf" % number)
def slowdown_cdf(datas):
fig, ax = plt.subplots(nrows=1, ncols=1)
for number, data in enumerate(datas):
weights = [np.array([1.0 / float(d.means.shape[0])] * d.means.shape[0]) for d in data]
slowdowns = [d.means / d.means[0,:] for d in data]
graphs = [lnm.fromkeyvals(d.names, slowdown) for d, slowdown in zip(data, slowdowns)]
graphs = [lnm.compute_lnm_times(g, L=1) for g in graphs]
slowdowns1 = [g.ungraph()[1] for g in graphs]
graphs = [lnm.fromkeyvals(d.names, slowdown) for d, slowdown in zip(data, slowdowns)]
graphs = [lnm.compute_lnm_times(g, L=2) for g in graphs]
slowdowns2 = [g.ungraph()[1] for g in graphs]
largest_dims = max(*[s.shape[-1] for s in slowdowns])
_, slowdowns = pad_weights(weights, slowdowns)
_, slowdowns1 = pad_weights(weights, slowdowns1)
weights, slowdowns2 = pad_weights(weights, slowdowns2)
weights = np.vstack(weights)
all_data = np.vstack(slowdowns)
all_data1 = np.vstack(slowdowns1)
all_data2 = np.vstack(slowdowns2)
N = all_data.shape[-1]
for i in range(2):
entries = np.sum(weights[:,i])
result = all_data[:,i]
counts, bin_edges = np.histogram(result, bins=len(result), weights=weights[:,i])
cdf = np.cumsum(counts) / np.sum(entries) * 100.0
ax.plot(bin_edges[:-1], cdf, LINESTYLES[number], label=LABELS[i], color=COLORS[i])
# add lines for L=1
result = all_data1[:,i]
counts, bin_edges = np.histogram(result, bins=len(result), weights=weights[:,1])
cdf = np.cumsum(counts) / entries * 100.0
ax.plot(bin_edges[:-1], cdf, LINESTYLES[number], label=LABELS[i], color=(0,0,0))
total_benchmarks = np.sum(weights, axis=0)
avg_slowdown_weighted = np.sum(weights * all_data, axis=0) / total_benchmarks
avg_slowdown_weighted1 = np.sum(weights * all_data1, axis=0) / total_benchmarks
s3 = np.sum(weights * (all_data < 3.0) , axis=0) * 100.0 / total_benchmarks
s4 = np.sum(weights * (all_data1 < 3.0) , axis=0) * 100.0 / total_benchmarks
s5 = np.sum(weights * (all_data < 1.1) , axis=0) * 100.0 / total_benchmarks
s6 = np.sum(weights * (all_data1 < 1.1) , axis=0) * 100.0 / total_benchmarks
s7 = np.sum(weights * (all_data2 < 1.1) , axis=0) * 100.0 / total_benchmarks
def rnd(x):
return round(x, 0)
if number != 0:
print "\multicolumn{8}{|c|}{%s} \\\\" % SUFFIXES[number]
print "\\hline"
for i in range(len(avg_slowdown_weighted)):
s1 = round(avg_slowdown_weighted[i], 2)
s2 = round(avg_slowdown_weighted1[i], 2)
print "%s & $%0.2f\\times$ & $%0.2f\\times$ & $%0.0f$ & $%0.0f$ & $%0.0f$ & $%0.0f$ & $%0.0f$ \\\\" % ((LABELS[i].capitalize(), s1, s2) + tuple(map(rnd, (s3[i], s4[i], s5[i], s6[i], s7[i]))))
print "\\hline"
plt.axvline(3, color=COLORS[-1])
plt.xlim((1,10))
ax.set_xlabel("slowdown factor")
ax.set_ylabel("% of benchmarks")
ax.set_xticklabels(["%dx" % (i + 1) for i in range(10)])
plt.ylim((0, 100))
plt.savefig("figs/aggregate-cdf.pdf")
for number, data in enumerate(datas):
plt.cla()
weights = [np.ones(d.means.shape[0]) / d.means.shape[0] for d in data]
slowdowns = [d.means / d.means[0,0] for d in data]
weights, slowdowns = pad_weights(weights, slowdowns)
weights = np.vstack(weights)
all_data = np.vstack(slowdowns)
for i in range(0, 2):
ax.scatter(all_data[:,0], all_data[:,i], label=LABELS[i], color=COLORS[i], marker='.')
if i == 0:
continue
# m, b = np.polyfit(all_data[:,0], all_data[:,i], 1)
m, b, r, _, _ = linregress(all_data[:,0], all_data[:,i])
x = np.vstack([np.arange(0, 100, 0.01), np.ones(10000)]).T
print "y = %f * x + %f : r^2 = %f" % (m, b, r ** 2)
y = m * x + b
ax.plot(x, y, color=COLORS[i+2])
textX = np.max(all_data[:,0]) / 2.0
textY = np.max(all_data[:,i]) / 1.8
plt.text(textX, textY, '$y = %0.3f x + %0.3f$ \n $r^2 = %0.3f$' % (m, b, r**2), fontsize=20,
color='k',
horizontalalignment='center',
verticalalignment='bottom')
plt.legend(loc='upper left')
plt.ylim((0, 70))
plt.xlim((0, 70))
ax.set_xlabel("Racket gradual typing overhead")
ax.set_ylabel("overhead relative to Racket")
plt.savefig("figs/aggregate-slowdown-%d.pdf" % number)