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#!/usr/bin/env python
from os.path import splitext, basename
from optparse import make_option as o
from tempfile import NamedTemporaryFile as Tmp
from collections import defaultdict
import numpy as np
from util import load_csv_file, select
import stats
import defapp
from plot import decode
from gnuplot import gnuplot, FORMATS
MACHINE_TOPOLOGY = {
'jupiter-cs' : (4, [('preempt', lambda x, y: x == y),
('mem', lambda x, y: x != y)]),
# Socket0 Socket1 Socket2 Socket3
# ------ ------- ------- -------
# | 0, 4| | 1, 5| | 2, 6| | 3, 7|
# | 8,12| | 9,13| |10,14| |11,15|
# |16,20| |17,21| |18,22| |19,23|
# ------- ------- ------- -------
'ludwig.cs.unc.edu' : (24, [('preempt', lambda x, y: x == y),
('l2',
lambda x, y: abs(y - x) == 4),
('l3',
lambda x, y:
abs(y - x) > 4 and \
abs(y - x) % 4 == 0),
('mem', lambda x, y: abs(y - x) % 4 != 0)])
}
PMO_PARAM = {
'wss' : 'WSS',
'host' : 'host',
'wcycle' : 'write-cycle'
}
PMO_MEM = {
'mem' : 'a migration through main memory',
'l3' : 'a migration through a shared L3 cache',
'l2' : 'a migration through a shared L2 cache',
'preempt' : 'a preemption',
'all' : 'either a migration or preemption',
}
PMO_SUBPLOTS = [
# x, y, y-delta, split according to mem-hierarchy?
(0, 6, None, False),
(0, 7, None, False),
(0, 8, None, False),
(0, 9, None, False),
(0, 10, None, True),
(3, 10, None, True),
(0, 10, 9, True),
(3, 10, 9, True),
]
PMO_AGGR_SUBPLOTS = [
# x, y, y-delta, split according to mem-hierarchy?
(0, 6, None, False),
(0, 7, None, False),
(0, 8, None, False),
(0, 9, None, False),
(0, 10, None, True),
(0, 10, 6, True),
(0, 10, 7, True),
(0, 10, 9, True),
(0, 10, 8, True),
]
PMO_COL_LABEL = [('measurement', 'sample', 'index'),
('write cycles', 'wcycle', 'every nth access'),
('WSS', 'wcc', 'kilobytes'),
('suspension length', 'delay', 'microseconds'),
('CPU (preempted on)', 'from', 'processor'),
('CPU (resumed on)', 'to', 'processor'),
('cold access', 'cold', 'cycles'),
('first hot access', 'hot1', 'cycles'),
('second hot access', 'hot2', 'cycles'),
('third hot access', 'hot3', 'cycles'),
('access after resuming', 'after', 'cycles')
]
PMO_FROM_CPU = 4
PMO_TO_CPU = 5
options = [
o('-f', '--format', action='store', dest='format', type='choice',
choices=FORMATS, help='output format'),
o(None, '--paper', action='store_true', dest='paper'),
o(None, '--wide', action='store_true', dest='wide'),
o(None, '--split', action='store_true', dest='split'),
o(None, '--extend', action='store', type='float', dest='extend'),
o(None, '--aggregate', action='store_true', dest='aggregate'),
]
defaults = {
'format' : 'show',
'paper' : False,
'split' : False,
'wide' : False,
'aggregate' : False,
'extend' : 1.5,
}
def extract_cols(data, xcol, ycol1, ycol2, cast=int, cpu_filter=lambda x, y: True):
def matching_cpus(row):
return cpu_filter(row[PMO_FROM_CPU], row[PMO_TO_CPU])
rows = select(matching_cpus, data)
if not (ycol2 is None):
rows[:,ycol1] -= rows[:,ycol2]
return rows[:,(xcol, ycol1)]
class CyclePlotter(defapp.App):
def __init__(self):
defapp.App.__init__(self, options, defaults, no_std_opts=True)
self.aggregate_data = []
def setup_pmo_graphs(self, datafile, conf, subplots=PMO_SUBPLOTS):
host = conf['host']
if host in MACHINE_TOPOLOGY:
(cpus, hier) = MACHINE_TOPOLOGY[host]
plots = []
data = load_csv_file(datafile, dtype=int)
for (xcol, ycol, yminus, by_mem_hierarchy) in subplots:
sub = [('all', lambda x, y: True)]
if by_mem_hierarchy:
sub += hier
for tag, test in sub:
rows = extract_cols(data,
xcol, ycol, yminus,
cpu_filter=test)
plots.append((rows, xcol, ycol, yminus, tag))
return plots
else:
self.err('Unkown host: %s' % host)
return None
def write_aggregate(self, datafiles):
# (wss, avg, wc, #avg, #wc)
# by tag -> by wcycle -> list of data points)
by_tag = defaultdict(lambda: defaultdict(list))
for i, datafile in enumerate(datafiles):
print '[%d/%d] Processing %s...' % (i + 1, len(datafiles), datafile)
bname = basename(datafile)
name, ext = splitext(bname)
if ext != '.csv':
self.err("Warning: '%s' doesn't look like a CSV file."
% bname)
conf = decode(name)
if 'pmo' in conf:
plots = self.setup_pmo_graphs(datafile, conf, PMO_AGGR_SUBPLOTS)
if plots is None:
print "Skipping %s..." % datafile
return
wss = int(conf['wss'])
wcycle = int(conf['wcycle'])
host = conf['host']
for (rows, xcol, ycol, yminus, tag) in plots:
clean = stats.iqr_remove_outliers(rows, extend=self.options.extend)
vals = clean[:,1]
avg = np.mean(vals)
std = np.std(vals, ddof=1)
wc = np.max(vals)
n = len(vals)
xtag = PMO_COL_LABEL[xcol][1]
ytag = PMO_COL_LABEL[ycol][1]
dtag = "-delta-%s" % PMO_COL_LABEL[yminus][1] if not yminus is None else ""
code = "code=%s-%s-%s-%s" % \
(xcol, ycol, yminus, tag)
figname = "host=%s_%s%s-vs-%s_%s_%s" % \
(host, ytag, dtag, xtag, tag, code)
by_tag[figname][wcycle].append((wss, avg, std, wc, n, len(rows) - n))
del plots
else:
self.err("Warning: '%s' is not a PMO experiment; skipping." % bname)
for figname in by_tag:
for wcycle in by_tag[figname]:
data = by_tag[figname][wcycle]
# sort by increasing WSS
data.sort(key=lambda row: row[0])
f = open('pmo-aggr_wcycle=%d_%s.csv' % (wcycle, figname), 'w')
for row in data:
f.write(", ".join([str(x) for x in row]))
f.write('\n')
f.close()
def plot_preempt_migrate(self, datafile, name, conf):
plots = self.setup_pmo_graphs(datafile, conf)
if plots is None:
print "Skipping %s..." % datafile
return
else:
print 'Plotting %s...' % datafile
for (rows, xcol, ycol, yminus, tag) in plots:
# Write it to a temp file.
tmp = Tmp()
for row in rows:
tmp.write("%s, %s\n" % (row[0], row[1]))
tmp.flush()
xtag = PMO_COL_LABEL[xcol][1]
ytag = PMO_COL_LABEL[ycol][1]
dtag = "-delta-%s" % PMO_COL_LABEL[yminus][1] if not yminus is None else ""
figname = "%s_%s%s-vs-%s_%s" % (name, ytag, dtag, xtag, tag)
xunit = PMO_COL_LABEL[xcol][2]
yunit = PMO_COL_LABEL[ycol][2]
ylabel = PMO_COL_LABEL[ycol][0]
xlabel = PMO_COL_LABEL[xcol][0]
title = "%s" % ylabel
if ycol == 10:
title += " from %s" % PMO_MEM[tag]
for key in conf:
if key in PMO_PARAM:
title += " %s=%s" % (PMO_PARAM[key], conf[key])
graphs = [(tmp.name, 1, 2, ylabel)]
# plot cutoff
(s, lo, hi) = stats.iqr(rows[:,1])
lo -= s * self.options.extend
hi += s * self.options.extend
m99 = stats.cutoff_max(rows[:, 1])
graphs += [(lo, 'IQR cutoff (%d)' % lo, 'line'),
(hi, 'IQR cutoff (%d)' % hi, 'line'),
(m99,'99%% cutoff (%d)' % m99, 'line lw 2')]
gnuplot(graphs,
xlabel="%s (%s)" % (xlabel, xunit),
ylabel="%s (%s)" % ("access cost" if yminus is None
else "delta to %s" % PMO_COL_LABEL[yminus][0],
yunit),
title=title,
style='points',
format=self.options.format,
fname=figname)
del tmp # delete temporary file
def plot_pmo_aggr(self, datafile, name, conf):
fname = datafile
code = conf['code']
(xcol, ycol, yminus, tag) = code.split('-')
xcol = int(xcol)
ycol = int(ycol)
if yminus != "None":
yminus = int(ycol)
else:
yminus = None
xtag = PMO_COL_LABEL[xcol][1]
ytag = PMO_COL_LABEL[ycol][1]
dtag = "-delta-%s" % PMO_COL_LABEL[yminus][1] if not yminus is None else ""
figname = "%s_%s%s-vs-%s_%s" % (name, ytag, dtag, xtag, tag)
xunit = PMO_COL_LABEL[xcol][2]
yunit = PMO_COL_LABEL[ycol][2]
ylabel = PMO_COL_LABEL[ycol][0]
xlabel = PMO_COL_LABEL[xcol][0]
title = "%s" % ylabel
ylabel="%s (%s)" % ("access cost" if yminus is None
else "delta to %s" % PMO_COL_LABEL[yminus][0],
yunit),
if ycol == 10:
title += " from %s" % PMO_MEM[tag]
for key in conf:
if key in PMO_PARAM:
title += " %s=%s" % (PMO_PARAM[key], conf[key])
graphs = [
#(fname, 1, 2, "average"),
"'%s' using 1:2:3 title 'average' with errorbars" % (fname),
(fname, 1, 4, "maximum"),
]
xlabel = "working set size (kilobytes)"
gnuplot(graphs, xlabel=xlabel, ylabel=ylabel, title=title, fname=figname,
logscale="xy 2" if yminus is None else "x 2",
format=self.options.format)
def plot_file(self, datafile):
bname = basename(datafile)
name, ext = splitext(bname)
if ext != '.csv':
self.err("Warning: '%s' doesn't look like a CSV file."
% bname)
conf = decode(name)
if 'pmo' in conf:
self.plot_preempt_migrate(datafile, name, conf)
elif 'pmo-aggr' in conf:
self.plot_pmo_aggr(datafile, name, conf)
else:
self.err("Skipped '%s'; unkown experiment type."
% bname)
def default(self, _):
for datafile in self.args:
self.plot_file(datafile)
def do_aggregate(self, _):
self.write_aggregate(self.args[1:])
if __name__ == "__main__":
CyclePlotter().launch()
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