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