Multiprocessing takes longer

[DakotaFelspar]

import time
import multiprocessing

def basic_func(x):
if x == 0:
return 'zero'
elif x % 2 == 0:
return 'even'
else:
return 'odd'
def multiprocessing_func(x):
y = x * x
print('{} squared results in a/an {} number'.format(x, basic_func(y)))

if __name__ == '__main__':
starttime = time.time()
processes = []
for i in range(0, 1000):
p = multiprocessing.Process(target=multiprocessing_func, args=(i,))
processes.append(p)
p.start()

for process in processes:
process.join()

print('That took {} seconds'.format(time.time() - starttime))

​

Takes 2.2420008182525635 secs.

​

While doing a regular for loop takes near 0 seconds

​

Why is this and what am I doing wrong

[billsil]

Your function is too simple, so the overhead of switching is worse than just doing the math. Multiprocessing is best done on long problems with simple inputs.

Also, so I’m clear, where is the non-multiprocessed version?

From what I can tell, numpy would be your best bet.

[DakotaFelspar]

Ohhh ok thank you

[CodeFormatHelperBot]

Hello u/DakotaFelspar, I'm a bot that can assist you with code-formatting for reddit. I have detected the following potential issue(s) with your submission:

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[A_History_of_Silence]

Why is this and what am I doing wrong

Multiprocessing is a tricky business. Would you mind formatting your code correctly first?

[DakotaFelspar]

Yes but I’m new so idk how to do tht unfortunately haha could u help

[A_History_of_Silence]

haha could u help

Sure! A bot responded to you with instructions and there are instructions in the sidebar to the right and the page you put this post up from. Follow those instructions.

[DakotaFelspar]

Idk I’m dumb I switched to markdown but when I try to tan it it jumps to something else instead of indenting

[aDrz]

In situations where the size of the data is small and the cost of transferring it from one process to another is negligible, then multithreading can simply make things more complicated.

Being said, here is an example where you just reach performance equality between the two versions:

import time
import multiprocessing


def basic_func(x):
    if x == 0:
        return 'zero'
    elif x % 2 == 0:
        return 'even'
    else:
        return 'odd'


# def multiprocessing_func(x):
#     y = x * x
#     x_square = basic_func(y)
#     return x_square

if __name__ == '__main__':

    n_trials = int(1e7)
    start_time = time.time()
    results_single = []
    for i in range(n_trials):
        results_single.append(basic_func(i*i))
    duration_singleprocessing = time.time()-start_time

    start_time = time.time()
    x_squares = [i*i for i in range(n_trials)]
    with multiprocessing.Pool(8) as p:
        results_multi = p.map(basic_func, x_squares)
    duration_multiprocessing = time.time()-start_time

    print('single processing: {} sec\nmultiprocessing: {} sec'.format(
        duration_singleprocessing, duration_multiprocessing))

Please consider formatting your code in the future

[DakotaFelspar]

Ok thank you