python multiprocessing vs threading for cpu bound work on windows and linux

Question

So I knocked up some test code to see how the multiprocessing module would scale on cpu bound work compared to threading. On linux I get the performance increase that I'd expect:

linux (dual quad core xeon):
serialrun took 1192.319 ms
parallelrun took 346.727 ms
threadedrun took 2108.172 ms

My dual core macbook pro shows the same behavior:

osx (dual core macbook pro)
serialrun took 2026.995 ms
parallelrun took 1288.723 ms
threadedrun took 5314.822 ms

I then went and tried it on a windows machine and got some very different results.

windows (i7 920):
serialrun took 1043.000 ms
parallelrun took 3237.000 ms
threadedrun took 2343.000 ms

Why oh why, is the multiprocessing approach so much slower on windows?

Here's the test code:

#!/usr/bin/env python

import multiprocessing
import threading
import time

def print_timing(func):
    def wrapper(*arg):
        t1 = time.time()
        res = func(*arg)
        t2 = time.time()
        print '%s took %0.3f ms' % (func.func_name, (t2-t1)*1000.0)
        return res
    return wrapper


def counter():
    for i in xrange(1000000):
        pass

@print_timing
def serialrun(x):
    for i in xrange(x):
        counter()

@print_timing
def parallelrun(x):
    proclist = []
    for i in xrange(x):
        p = multiprocessing.Process(target=counter)
        proclist.append(p)
        p.start()

    for i in proclist:
        i.join()

@print_timing
def threadedrun(x):
    threadlist = []
    for i in xrange(x):
        t = threading.Thread(target=counter)
        threadlist.append(t)
        t.start()

    for i in threadlist:
        i.join()

def main():
    serialrun(50)
    parallelrun(50)
    threadedrun(50)

if __name__ == '__main__':
    main()

Answer 1

The python documentation for multiprocessing blames the lack of os.fork() for the problems in Windows. It may be applicable here.

See what happens when you import psyco. First, easy_install it:

C:Usershughdbrown>Python26scriptseasy_install.exe psyco
Searching for psyco
Best match: psyco 1.6
Adding psyco 1.6 to easy-install.pth file

Using c:python26libsite-packages
Processing dependencies for psyco
Finished processing dependencies for psyco

Add this to the top of your python script:

import psyco
psyco.full()

I get these results without:

serialrun took 1191.000 ms
parallelrun took 3738.000 ms
threadedrun took 2728.000 ms

I get these results with:

serialrun took 43.000 ms
parallelrun took 3650.000 ms
threadedrun took 265.000 ms

Parallel is still slow, but the others burn rubber.

Edit: also, try it with the multiprocessing pool. (This is my first time trying this and it is so fast, I figure I must be missing something.)

@print_timing
def parallelpoolrun(reps):
    pool = multiprocessing.Pool(processes=4)
    result = pool.apply_async(counter, (reps,))

Results:

C:UsershughdbrownDocumentspythonStackOverflow>python  1289813.py
serialrun took 57.000 ms
parallelrun took 3716.000 ms
parallelpoolrun took 128.000 ms
threadedrun took 58.000 ms