multi_process_map.py

from multiprocessing import Pool, cpu_count

def multi_process_map(fn, args):
  '''Applyes function fn to each element in args running in a seperate process.

	This will block until all function calls are finished.
	This uses a process pool, which size equals the number of available cpu's.

	fn:				a funtion that takes one argument
	args:			a list of arguments to that function
	returns:	a listof results'''
	pool = Pool(cpu_count())
	return pool.map(fn, args)

This uses multi process because of this:

CPython implementation detail: In CPython, due to the Global Interpreter Lock, only one thread can execute Python code at once (even though certain performance-oriented libraries might overcome this limitation). If you want your application to make better use of the computational resources of multi-core machines, you are advised to use multiprocessing. However, threading is still an appropriate model if you want to run multiple I/O-bound tasks simultaneously.

• http://docs.python.org/2/library/threading.html#module-threading