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bdiff: don't check border condition in loop...
bdiff: don't check border condition in loop `plast = a + len - 1`. So, this "for" loop iterates from "a" to "plast", inclusive. So, `p == plast` can only be true on the final iteration of the loop. So checking for it on every loop iteration is wasteful. This patch simply decreases the upper bound of the loop by 1 and adds an explicit check after iteration for the `p == plast` case. We can't simply add 1 to the initial value for "i" because that doesn't do the correct thing on empty input strings. `perfbdiff -m 3041e4d59df2` on the Firefox repo becomes significantly faster: ! wall 0.072763 comb 0.070000 user 0.070000 sys 0.000000 (best of 100) ! wall 0.053221 comb 0.060000 user 0.060000 sys 0.000000 (best of 100) For the curious, this code has its origins in 8b067bde6679, which is the changeset that introduced bdiff.c in 2005. Also, GNU diffutils is able to perform a similar line-based diff in under 20ms. So there's likely more perf wins to be found in this code. One of them is the hashing algorithm. But it looks like mpm spent some time testing hash collisions in d0c48891dd4a. I'd like to do the same before switching away from lyhash, just to be on the safe side.

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worker.py
184 lines | 5.7 KiB | text/x-python | PythonLexer
# worker.py - master-slave parallelism support
#
# Copyright 2013 Facebook, Inc.
#
# This software may be used and distributed according to the terms of the
# GNU General Public License version 2 or any later version.
from __future__ import absolute_import
import errno
import os
import signal
import sys
import threading
from .i18n import _
from . import error
def countcpus():
'''try to count the number of CPUs on the system'''
# posix
try:
n = int(os.sysconf('SC_NPROCESSORS_ONLN'))
if n > 0:
return n
except (AttributeError, ValueError):
pass
# windows
try:
n = int(os.environ['NUMBER_OF_PROCESSORS'])
if n > 0:
return n
except (KeyError, ValueError):
pass
return 1
def _numworkers(ui):
s = ui.config('worker', 'numcpus')
if s:
try:
n = int(s)
if n >= 1:
return n
except ValueError:
raise error.Abort(_('number of cpus must be an integer'))
return min(max(countcpus(), 4), 32)
if os.name == 'posix':
_startupcost = 0.01
else:
_startupcost = 1e30
def worthwhile(ui, costperop, nops):
'''try to determine whether the benefit of multiple processes can
outweigh the cost of starting them'''
linear = costperop * nops
workers = _numworkers(ui)
benefit = linear - (_startupcost * workers + linear / workers)
return benefit >= 0.15
def worker(ui, costperarg, func, staticargs, args):
'''run a function, possibly in parallel in multiple worker
processes.
returns a progress iterator
costperarg - cost of a single task
func - function to run
staticargs - arguments to pass to every invocation of the function
args - arguments to split into chunks, to pass to individual
workers
'''
if worthwhile(ui, costperarg, len(args)):
return _platformworker(ui, func, staticargs, args)
return func(*staticargs + (args,))
def _posixworker(ui, func, staticargs, args):
rfd, wfd = os.pipe()
workers = _numworkers(ui)
oldhandler = signal.getsignal(signal.SIGINT)
signal.signal(signal.SIGINT, signal.SIG_IGN)
pids, problem = [], [0]
for pargs in partition(args, workers):
pid = os.fork()
if pid == 0:
signal.signal(signal.SIGINT, oldhandler)
try:
os.close(rfd)
for i, item in func(*(staticargs + (pargs,))):
os.write(wfd, '%d %s\n' % (i, item))
os._exit(0)
except KeyboardInterrupt:
os._exit(255)
# other exceptions are allowed to propagate, we rely
# on lock.py's pid checks to avoid release callbacks
pids.append(pid)
pids.reverse()
os.close(wfd)
fp = os.fdopen(rfd, 'rb', 0)
def killworkers():
# if one worker bails, there's no good reason to wait for the rest
for p in pids:
try:
os.kill(p, signal.SIGTERM)
except OSError as err:
if err.errno != errno.ESRCH:
raise
def waitforworkers():
for _pid in pids:
st = _exitstatus(os.wait()[1])
if st and not problem[0]:
problem[0] = st
killworkers()
t = threading.Thread(target=waitforworkers)
t.start()
def cleanup():
signal.signal(signal.SIGINT, oldhandler)
t.join()
status = problem[0]
if status:
if status < 0:
os.kill(os.getpid(), -status)
sys.exit(status)
try:
for line in fp:
l = line.split(' ', 1)
yield int(l[0]), l[1][:-1]
except: # re-raises
killworkers()
cleanup()
raise
cleanup()
def _posixexitstatus(code):
'''convert a posix exit status into the same form returned by
os.spawnv
returns None if the process was stopped instead of exiting'''
if os.WIFEXITED(code):
return os.WEXITSTATUS(code)
elif os.WIFSIGNALED(code):
return -os.WTERMSIG(code)
if os.name != 'nt':
_platformworker = _posixworker
_exitstatus = _posixexitstatus
def partition(lst, nslices):
'''partition a list into N slices of roughly equal size
The current strategy takes every Nth element from the input. If
we ever write workers that need to preserve grouping in input
we should consider allowing callers to specify a partition strategy.
mpm is not a fan of this partitioning strategy when files are involved.
In his words:
Single-threaded Mercurial makes a point of creating and visiting
files in a fixed order (alphabetical). When creating files in order,
a typical filesystem is likely to allocate them on nearby regions on
disk. Thus, when revisiting in the same order, locality is maximized
and various forms of OS and disk-level caching and read-ahead get a
chance to work.
This effect can be quite significant on spinning disks. I discovered it
circa Mercurial v0.4 when revlogs were named by hashes of filenames.
Tarring a repo and copying it to another disk effectively randomized
the revlog ordering on disk by sorting the revlogs by hash and suddenly
performance of my kernel checkout benchmark dropped by ~10x because the
"working set" of sectors visited no longer fit in the drive's cache and
the workload switched from streaming to random I/O.
What we should really be doing is have workers read filenames from a
ordered queue. This preserves locality and also keeps any worker from
getting more than one file out of balance.
'''
for i in range(nslices):
yield lst[i::nslices]