多处理要么永远不会完成，要么比串行处理花费更长的时间

Question

For some reason, Example 1's multiprocessing portion prints out a time far greater than the serial portion, and Example 2 just runs forever... I'm wondering if this is an issue with my using VSCode/Jupyter Notebook rather than the code itself, which I got from a couple tutorials I found online.

Example 1:

import multiprocessing as mp
import time
import math

results_a = []
results_b = []

def calc_one(numbers):
    for number in numbers:
        results_a.append(math.sqrt(number **4))

def calc_two(numbers):
    for number in numbers:
        results_b.append(math.sqrt(number**5))

if __name__ == '__main__':
    number_list = list(range(20))
    start = time.time()
    calc_one(number_list)
    calc_two(number_list)
    end = time.time()
    print("Time for process one: " + str(end - start))

    start = time.time()
    p1 = mp.Process(target = calc_one, args = (number_list,))
    p2 = mp.Process(target = calc_two, args = (number_list,))
    p1.start()
    p2.start()
    end = time.time()
    print("Time for process two: " + str(end - start))

Example 2:

def f(n):
    sum = 0
    for x in range(100):
        sum += x*x
    return sum

if __name__ == "__main__":

    array = [1, 2, 3, 4, 5]

    start = time.time()
    
    result = []
    for n in array:
        result.append(f(n))
    print(result)
    end = time.time()
    print("Time for process one: " + str(end - start))

    start = time.time()
    result = []
    p = Pool()
    result = p.map(f, array)
    p.close()
    p.join()
    print(result)
    end = time.time()
    print("Time for process one: " + str(end - start))

Answer 1

The solution that ended up working for me was:

func(num):
    print(num * num)

import multiprocessing.dummy as mp
p = mp.Pool(5)
list = p.starmap(func, y for y in range(3, 100))
p.close()
p.join()

The process I ran contained a function that was quite a bit more intensive than the one shown above as func(), but it still worked. For my function, it took in multiple arguments, so I organized my arguments into a list of tuples.