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 print_compare_normalized (experiment_id, n_procs):
problem_name=get.problem_name_of_experiment(experiment_id)
print ("Problem: " + problem_name)
print ("Number of Cores: " + str(n_procs))
((binput, bps, bspeedup, mltspeedup, mltlbsovhd, seqpmlcvsmlton, berror, lbs_ovhd, (bavg, bstddev)), norms)=compare_normalized(experiment_id, n_procs)
print ("Best splitting strategy: " + bps + " (Error= " + human_readable.percent(berror) + "%) (Speedup vs. SeqPMLC= " + human_readable.speedup(bspeedup) + ") (Speedup vs. mlton= " + human_readable.speedup(mltspeedup) + ")")
print ("Overhead of LBS vs. NS (PMLC)= " + human_readable.percent(lbs_ovhd)) + '%'
print ("Overhead of LBS vs. NS (mlton)= " + human_readable.percent(mltlbsovhd)) + '%'
print ("Sequential PMLC vs. mlton (both NS)= " + human_readable.percent(seqpmlcvsmlton) + "%")
print("Splitting strategy\t\tPct. slower\t\tError\t\t\tSpeedup vs. SeqPMLC\tSpeedup vs. mlton")
for norm in norms:
print_pt(norm)
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 compare(experiment_id):
speedup_args = []
ssts = (1, 128, 16384)
ks = (1, 2)
sstpars = ([], [], [])
ebsappars = ([], [])
lbspars = []
for n_procs in (1, 8, 16, 24, 32, 42, 48):
(maxtime, bl_avg, mlt_avg, mltlbsovhd, lbs_ovhd,
norms) = r.compare_wall_clock(experiment_id, n_procs)
for i in (0, 1, 2):
sst = ssts[i]
xinput = "-chunking-policy ETS " + str(sst)
for (input, ps, avg, stddev) in norms:
if input == xinput:
sstparsi = sstpars[i]
sstparsi.append((n_procs, avg))
for i in [1]:
k = ks[i]
xinput = "-splitting-strategy ebs-ap " + str(k) + " 4"
for (input, ps, avg, stddev) in norms:
if input == xinput:
ebsapparsi = ebsappars[i]
ebsapparsi.append((n_procs, avg))
for (input, ps, avg, stddev) in norms:
if r.is_lbs1(input):
lbspars.append((n_procs, avg))
speedup_args.append(("LTS", bl_avg, lbspars))
speedup_args.append(("ETS ($SST=2^0$)", bl_avg, sstpars[0]))
speedup_args.append(("ETS ($SST=2^7$)", bl_avg, sstpars[1]))
speedup_args.append(("ETS ($SST=2^{14}$)", bl_avg, sstpars[2]))
# speedup_args.append(("EBS AP (K=1 V=4)", bl_avg, ebsappars[0]))
# speedup_args.append(("EBS AP (K=2 V=4)", bl_avg, ebsappars[1]))
ylab = 'speedup'
bench_name = get.problem_name_of_experiment(experiment_id)
xaxlabs = (0, 8, 16, 24, 32, 40, 48)
yaxvals = (0, 8, 16, 24, 32, 40, 48)
plot(bench_name + '-speedups',
speedup_args,
chart_title='',
yax_label=ylab,
connect_dots=True,
dimensions=(35, 23),
prefix='.',
formats=['r.', 'bx', 'b+', 'b1', 'b,', 'b4', 'b2', 'b<', 'b>', 'b|'],
yaxvals=yaxvals,
xaxlabs=xaxlabs,
marker=(80.0, 5.0))
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))
# Adam Shaw, November 2009
# run this with 'python speedup.py'
# Please see EDIT ME! below for customization.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
import utils
import rope_splitting_experiment as r
import collect_data as get
import human_readable as h
import speedups as spds
for experiment_id in spds.ids:
(maxtime, bl_avg, mlt_avg, mltlbsovhd, lbs_ovhd,
norms) = r.compare_wall_clock(experiment_id, 48)
problem_name = get.problem_name_of_experiment(experiment_id)
problem_name = r.pretty_bench_name(problem_name)
(input, ps, lbs_avg,
lbs_std) = filter(lambda (input, ps, avg, err): r.is_lbs1(input),
norms)[0]
lbs_speedup = bl_avg / lbs_avg
print(problem_name + " & " + h.speedup(mlt_avg, digits=2) + " & " +
h.speedup(bl_avg, digits=2) + " & " + h.speedup(lbs_ovhd, digits=2) +
" & " + h.speedup(lbs_avg, digits=2) + " & " +
h.speedup(lbs_speedup, digits=2) + "\\\\")