Optimizing a Nested Loop Algorithm for Maximum Efficiency

Question

I'm working on a project that involves processing a large dataset with nested loops, and I'm struggling to optimize my code for maximum efficiency. Here's my current approach, which I know is not efficient:

for i in range(N):
    for j in range(M):
        for k in range(L):
            # Perform some computations

In this code, N, M, and L are the sizes of the loops, and within the innermost loop, I'm performing some computations that take a significant amount of time.

I've tried various techniques like memoization and parallelization, but I'm still not achieving the performance I need. Are there any advanced optimization strategies or libraries that I can use to speed up this nested loop processing significantly? I'm open to suggestions in Python or any other language that can provide a substantial performance boost.

Answer 1

using multiprocessing in linux system

from multiprocessing import cpu_count, Pool, Array, Manager
import numpy as np
N = 20
M = 20
L = 20
ret = Array('f', N*M*L)
def calcul(i, j, k, num,):
    global ret
    ret._obj[num] = i+j+k
    kk = 0

def main():
    p = Pool(2)
    num = 0
    for i in range(N):
        for j in range(M):
            for k in range(L):
                # Perform some computations
                # calcul(i, j, k, num)
                p.apply_async(calcul, args=(i, j, k, num,))
                # p.apply(calcul, args=(i, j, k, num,))
                num += 1
    p.close()
    p.join()
    
if __name__=="__main__":
    main()
    kk = []
    for i in range(N*M*L):
        kk.append(ret._obj[i])
    k = 0

in win10, it doesn't work