pooling_python.py

from multiprocessing import Pool

# parallelize function
def product(a,b):
	print a*b

# auxiliary funciton to make it work
def product_helper(args):
	return product(*args)

def parallel_product(list_a, list_b):
	# spark given number of processes
	p = Pool(5) 
	# set each matching item into a tuple
	job_args = [(item_a, list_b[i]) for i, item_a in enumerate(list_a)] 
	# map to pool
	p.map(product_helper, job_args)

exp_a = range(1000)
exp_b = range(1000)
parallel_product(exp_a, exp_b)