Parallelize these nested for loops in python

Question

I have a multidimensional array (result) that should be filled by some nested loops. Function fun() is a complex and time-consuming function. I want to fill my array elements in a parallel manner, so I can use all my system's processing power. Here's the code:

import numpy as np


def fun(x, y, z):
    # time-consuming computation...
    # ...

    return output


dim1 = 10
dim2 = 20
dim3 = 30

result = np.zeros([dim1, dim2, dim3])

for i in xrange(dim1):
    for j in xrange(dim2):
        for k in xrange(dim3):
            result[i, j, k] = fun(i, j, k)

My question is that "Can I parallelize this code or not? if yes, How?"

I'm using Windows 10 64-bit and python 2.7.

Please provide your solution by changing my code if you can. Thanks!

Answer 1

Here is a version of code that runs fun(i, j, k) in parallel for differend k indices. This is done by running fun in different processes by using https://docs.python.org/2/library/multiprocessing.html

import numpy as np
from multiprocessing import Pool


def fun(x, y, z):
    # time-consuming computation...
    # ...

    return output


def fun_wrapper(indices):
    fun(*indices)

if __name__ == '__main__':
    dim1 = 10
    dim2 = 20
    dim3 = 30

    result = np.zeros([dim1, dim2, dim3])

    pool = Pool(processes=8)
    for i in xrange(dim1):
        for j in xrange(dim2):
            result[i, j] = pool.map(fun_wrapper, [(i, j, k) for k in xrange(dim3)])

This is not the most elegant solution but you may start with it. And you will get a speed up only if fun contains time-consuming computation