I have a folowing problem. i would like to run a simple foo function on multiple CPUs and save computational time. I am afraid that my code does the opposite – it runs foo multiple times.

Here is my code:

from itertools import product

all_combinations = []

for cas in range(3):

    target_sum = 12

    combinations = product(range(target_sum + 1), repeat=4)

    valid_combinations = [combo for combo in combinations if sum(combo) == target_sum]

    all_combinations.append(valid_combinations)


compute_combo = []


for a in all_combinations[2]:
    for b in all_combinations[1]:
        for c in all_combinations[0]:
            compute_combo.append([a, b, c])


def foo(combo):
    max_combo = 0
    for j in combo:
        for a in j:
            suma = a[0] + a[1] + 3 * a[2] + a[3]
            if suma > max_combo:
                max_combo = suma
    return max_combo



import time


start_time_par = time.time()



import multiprocessing

nb_cpus = 5

with multiprocessing.Pool(processes=nb_cpus) as pool:
    results = pool.map(foo, [compute_combo])



end_time_par = time.time()

execution_time_par = end_time_par - start_time_par

print(f"Multiple CPUs result {max(results)}")

print(f"Multiple CPUs run time: {execution_time_par} s")

Can you help me to find the mistake in my code, please?