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#!/usr/bin/env python
from __future__ import print_function
import common as com
import multiprocessing
import os
import parse.ft as ft
import parse.sched as st
import pickle
import shutil as sh
import sys
import traceback
from collections import namedtuple
from config.config import DEFAULTS,PARAMS
from optparse import OptionParser
from parse.point import ExpPoint
from parse.tuple_table import TupleTable
from parse.col_map import ColMapBuilder
def parse_args():
parser = OptionParser("usage: %prog [options] [data_dir]...")
parser.add_option('-o', '--out', dest='out',
help='file or directory for data output',
default=DEFAULTS['out-parse'])
parser.add_option('-i', '--ignore', metavar='[PARAM...]', default="",
help='ignore changing parameter values')
parser.add_option('-f', '--force', action='store_true', default=False,
dest='force', help='overwrite existing data')
parser.add_option('-v', '--verbose', action='store_true', default=False,
dest='verbose', help='print out data points')
parser.add_option('-m', '--write-map', action='store_true', default=False,
dest='write_map',
help='Output map of values instead of csv tree')
parser.add_option('-p', '--processors',
default=max(multiprocessing.cpu_count() - 1, 1),
type='int', dest='processors',
help='number of threads for processing')
return parser.parse_args()
ExpData = namedtuple('ExpData', ['path', 'params', 'work_dir'])
def parse_exp(exp_force):
# Tupled for multiprocessing
exp, force = exp_force
result_file = exp.work_dir + "/exp_point.pkl"
should_load = not force and os.path.exists(result_file)
result = None
if should_load:
with open(result_file, 'rb') as f:
try:
# No need to go through this work twice
result = pickle.load(f)
except:
pass
if not result:
try:
# Create a readable name
name = os.path.relpath(exp.path)
name = name if name != "." else os.path.split(os.getcwd())[1]
result = ExpPoint(name)
# Write overheads into result
cycles = exp.params[PARAMS['cycles']]
ft.extract_ft_data(result, exp.path, exp.work_dir, cycles)
# Write scheduling statistics into result
st.extract_sched_data(result, exp.path, exp.work_dir)
with open(result_file, 'wb') as f:
pickle.dump(result, f)
except:
traceback.print_exc()
return (exp, result)
def get_exp_params(data_dir, cm_builder):
param_file = "%s/%s" % (data_dir, DEFAULTS['params_file'])
if os.path.isfile(param_file):
params = com.load_params(param_file)
# Store parameters in cm_builder, which will track which parameters change
# across experiments
for key, value in params.iteritems():
cm_builder.try_add(key, value)
else:
params = {}
# Cycles must be present for feather-trace measurement parsing
if PARAMS['cycles'] not in params:
params[PARAMS['cycles']] = DEFAULTS['cycles']
return params
def load_exps(exp_dirs, cm_builder, force):
exps = []
sys.stderr.write("Loading experiments...\n")
for data_dir in exp_dirs:
if not os.path.isdir(data_dir):
raise IOError("Invalid experiment '%s'" % os.path.abspath(data_dir))
# Used to store error output and debugging info
work_dir = data_dir + "/tmp"
if os.path.exists(work_dir) and force:
sh.rmtree(work_dir)
if not os.path.exists(work_dir):
os.mkdir(work_dir)
params = get_exp_params(data_dir, cm_builder)
exps += [ ExpData(data_dir, params, work_dir) ]
return exps
def get_dirs(args):
if args:
return args
elif os.path.exists(DEFAULTS['out-run']):
sys.stderr.write("Reading data from %s/*\n" % DEFAULTS['out-run'])
sched_dirs = os.listdir(DEFAULTS['out-run'])
return ['%s/%s' % (DEFAULTS['out-run'], d) for d in sched_dirs]
else:
sys.stderr.write("Reading data from current directory.\n")
return [os.getcwd()]
def fill_table(table, exps, opts):
sys.stderr.write("Parsing data...\n")
procs = min(len(exps), opts.processors)
logged = multiprocessing.Manager().list()
pool = multiprocessing.Pool(processes=procs,
# Share a list of previously logged messages amongst processes
# This is for the com.log_once method to use
initializer=com.set_logged_list, initargs=(logged,))
pool_args = zip(exps, [opts.force]*len(exps))
enum = pool.imap_unordered(parse_exp, pool_args, 1)
try:
for i, (exp, result) in enumerate(enum):
if not result:
continue
if opts.verbose:
print(result)
else:
sys.stderr.write('\r {0:.2%}'.format(float(i)/len(exps)))
table[exp.params] += [result]
pool.close()
except:
pool.terminate()
traceback.print_exc()
raise Exception("Failed parsing!")
finally:
pool.join()
sys.stderr.write('\n')
def write_output(table, opts):
reduced_table = table.reduce()
if opts.write_map:
sys.stderr.write("Writing python map into %s...\n" % opts.out)
reduced_table.write_map(opts.out)
else:
if opts.force and os.path.exists(opts.out):
sh.rmtree(opts.out)
# Write out csv directories for all variable params
dir_map = reduced_table.to_dir_map()
# No csvs to write, assume user meant to print out data
if dir_map.is_empty():
if not opts.verbose:
sys.stderr.write("Too little data to make csv files, " +
"printing results.\n")
for key, exp in table:
for e in exp:
print(e)
else:
sys.stderr.write("Writing csvs into %s...\n" % opts.out)
dir_map.write(opts.out)
def main():
opts, args = parse_args()
exp_dirs = get_dirs(args)
# Load experiment parameters into a ColMap
builder = ColMapBuilder()
exps = load_exps(exp_dirs, builder, opts.force)
# Don't track changes in ignored parameters
if opts.ignore:
for param in opts.ignore.split(","):
builder.try_remove(param)
# Always average multiple trials
builder.try_remove(PARAMS['trial'])
# Only need this for feather-trace parsing
builder.try_remove(PARAMS['cycles'])
col_map = builder.build()
table = TupleTable(col_map)
fill_table(table, exps, opts)
if not table:
sys.stderr.write("Found no data to parse!")
sys.exit(1)
write_output(table, opts)
if __name__ == '__main__':
main()
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