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

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:\Users\hughdbrown>\Python26\scripts\easy_install.exe psyco
Searching for psyco
Best match: psyco 1.6
Adding psyco 1.6 to easy-install.pth file

Using c:\python26\lib\site-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:\Users\hughdbrown\Documents\python\StackOverflow>python  1289813.py
serialrun took 57.000 ms
parallelrun took 3716.000 ms
parallelpoolrun took 128.000 ms
threadedrun took 58.000 ms