From http://www.praetorian.com/blog/multi-core-and-distributed-programming-in-python pool

poolWorker.py

import random
import time
import sys
from multiprocessing import Pool
random.seed()
def genList (size):
    randomList = []
     
    #initialize random list with values between 0 and 100
    for i in range(size):
        randomList.append(random.randint(0,10))
         
    return randomList
#return the sum of all elements in the list
#This is the same as "return sum(inList)" but in long form for readability and emphasis
def sumList(inList):
    finalSum = 0
     
    #iterate over all values in the list, and calculate the cummulative sum
    for value in inList:
        finalSum = finalSum + value
    return finalSum
def doWork(N):
    #create a random list of N integers
    myList = genList (N)
    finalSum = sumList(myList)
    return finalSum
if __name__ == '__main__':
    if len(sys.argv) == 2 and sys.argv[1].isdigit():
        N = int(sys.argv[1])
        #mark the start time
        startTime = time.time()
         
        #create a process Pool with 4 processes
        pool = Pool(processes=4)
         
        #map doWork to availble Pool processes
        results = pool.map(doWork, (N/4,N/4,N/4,N/4))
         
        #sum the partial results to get the final result
        finalSum = sumList(results)
                 
        #mark the end time
        endTime = time.time()
        #calculate the total time it took to complete the work
        workTime =  endTime - startTime
         
        #print results
        print "The job took " + str(workTime) + " seconds to complete"
        print "The final sum was: " + str(finalSum)
    else:
        exit(-1)