How to parallel sum a loop using multiprocessing in Python

Question

I am having difficulty understanding how to use Python's multiprocessing module.

I have a sum from 1 to n where n=10^10, which is too large to fit into a list, which seems to be the thrust of many examples online using multiprocessing.

Is there a way to "split up" the range into segments of a certain size and then perform the sum for each segment?

For instance

def sum_nums(low,high):
    result = 0
    for i in range(low,high+1):
        result += i
    return result

And I want to compute sum_nums(1,10**10) by breaking it up into many sum_nums(1,1000) + sum_nums(1001,2000) + sum_nums(2001,3000)... and so on. I know there is a close-form n(n+1)/2 but pretend we don't know that.

Here is what I've tried

import multiprocessing

def sum_nums(low,high):
    result = 0
    for i in range(low,high+1):
        result += i
    return result

if __name__ == "__main__":
    n = 1000 
    procs = 2 

    sizeSegment = n/procs

    jobs = []
    for i in range(0, procs):
        process = multiprocessing.Process(target=sum_nums, args=(i*sizeSegment+1, (i+1)*sizeSegment))
        jobs.append(process)

    for j in jobs:
        j.start()
    for j in jobs:
        j.join()

    #where is the result?

Answer 1

I find the usage of multiprocess.Pool and map() much more simple

Using your code:

from multiprocessing import Pool

def sum_nums(args):
    low = int(args[0])
    high = int(args[1])
    return sum(range(low,high+1))

if __name__ == "__main__":
    n = 1000 
    procs = 2 

    sizeSegment = n/procs

    # Create size segments list
    jobs = []
    for i in range(0, procs):
        jobs.append((i*sizeSegment+1, (i+1)*sizeSegment))

    pool = Pool(procs).map(sum_nums, jobs)
    result = sum(pool)

    >>> print result
    >>> 500500