Here's a further modified version.  In this case, when using the pool for the first time, it uses an n of 10, instead of 100 million.  Even with such a low precision, the first execution takes 1.3 seconds.  It seems some significant warm up time is needed the first time a multiprocessing.Pool object is used.

See the attachment or this link for the code: http://pastie.org/2614925



On Thu, Sep 29, 2011 at 8:24 PM, Josh Ayers <josh.ayers@gmail.com> wrote:
I think the slowdown you're seeing is due to the time it takes to create new processes.  This seems to be quite a bit slower in PyPy than in CPython.  However, once the process pool is created and has been used once, the execution time vs. process count behaves as expected.

I attached a modified version of your code to demonstrate the behavior.  It calculates Pi once without using multiprocessing as a baseline for comparison.  Then a multiprocessing.Pool object is created with 8 processes, and the same pool is used multiple times.  On my machine, creating the 8 new processes takes 0.60 seconds in PyPy and only 0.20 seconds in CPython.

The pool is first used two times in a row with only a single process active.  For some reason, the second run is a factor of 2 faster than the first.  Is this just warmup of the JIT, or some other behavior?

Next, it repeats using 2, 4, and 8 processes.  This was run on a 4 core machine, and as expected there was an improvement in run time with 2 and 4 processes.  Using 8 processes gives approximately the same run time as 4.

The output is pasted below.  I also pasted the modified code here in case the attached file doesn't come through: http://pastie.org/2614751.  For reference, I'm running PyPy 1.6 on Windows 7.

Sincerely,
Josh


C:\Users\jayers\Documents\SVN\randomStuff\pypy_comparisons>pypy-c pi_python2_multiprocessing_pool.py

3.14159265359
non parallel execution time: 1.52899980545
pool creation time: 0.559000015259
==== Python Multiprocessing Pool pi = 3.14159265359
==== Python Multiprocessing Pool iteration count = 100000000
==== Python Multiprocessing Pool elapse = 3.1930000782
==== Python Multiprocessing Pool process count = 1
==== Python Multiprocessing Pool processor count = 4


==== Python Multiprocessing Pool pi = 3.14159265359
==== Python Multiprocessing Pool iteration count = 100000000
==== Python Multiprocessing Pool elapse = 1.53900003433
==== Python Multiprocessing Pool process count = 1
==== Python Multiprocessing Pool processor count = 4


==== Python Multiprocessing Pool pi = 3.14159265359
==== Python Multiprocessing Pool iteration count = 100000000
==== Python Multiprocessing Pool elapse = 0.802000045776
==== Python Multiprocessing Pool process count = 2
==== Python Multiprocessing Pool processor count = 4


==== Python Multiprocessing Pool pi = 3.14159265359
==== Python Multiprocessing Pool iteration count = 100000000
==== Python Multiprocessing Pool elapse = 0.441999912262
==== Python Multiprocessing Pool process count = 4
==== Python Multiprocessing Pool processor count = 4


==== Python Multiprocessing Pool pi = 3.14159265359
==== Python Multiprocessing Pool iteration count = 100000000
==== Python Multiprocessing Pool elapse = 0.457000017166
==== Python Multiprocessing Pool process count = 8
==== Python Multiprocessing Pool processor count = 4