I have been trying to learn multiprocessing. I wanted to test my understanding by making a little program that would generate a list of random numbers. Then make two different methods to find the largest number in the list. The first method not using multiprocessing and the second using multiprocessing. However when I run the code the runtime is always hundreds of times slower when I use the multiprocessing code regardless of the length of the list.

import random
import multiprocessing
import time

def random_num_list(length=100000):
     return [random.randint(-13443,435234) for i in range(length)]

rando_list = random_num_list()

def method1(lst):
    biggest_num = -13443
    for i in lst:
        if i > biggest_num:
            biggest_num = i

    return biggest_num

lst1, lst2 = rando_list[:int(len(rando_list)/3)], rando_list[int(len(rando_list)/3):]

def partial_process(lst1, lst2):
    biggest_num = -13443
    for i in lst1:
        if i > biggest_num:
            biggest_num = i

    lst2.append(biggest_num)
    #print("New record added!n")

def full_process(lst2):
    biggest_num = -13443
    for i in lst2:
        if i > biggest_num:
            biggest_num = i

    print(biggest_num)

def method2(lst1,lst2):
    with multiprocessing.Manager() as manager:
        lst2 = manager.list(lst2)

        p1 = multiprocessing.Process(target=partial_process,args=(lst1, lst2))
        p2 = multiprocessing.Process(target=full_process,args=(lst2,))

        p1.start()
        p1.join()

        p2.start()
        p2.join()

if __name__ == '__main__':
    method2(lst1,lst2)
    print(method1(rando_list))

I also tried writing method2 like this as well:

def method2(lst):
    num_processes = multiprocessing.cpu_count()  # Number of processes to match the number of CPU cores
    chunk_size = len(lst) // num_processes

    with multiprocessing.Pool(processes=num_processes) as pool:
        # Split the list into chunks and process in parallel
        results = pool.map(find_max, [lst[i * chunk_size:(i + 1) * chunk_size] for i in range(num_processes)])

    return max(results)