[Python] Why does my Python multiprocessing code run slower than the single-threaded version?

I'm working on a CPU-bound task in Python and attempted to speed it up using the multiprocessing module. I expected performance improvements due to parallel execution, but instead, the multiprocessing version runs noticeably slower than the single-threaded version. I'm using multiprocessing.Pool with map() to distribute tasks across processes.

Here's a simplified version of the code:

from multiprocessing import Pool
import time

def compute(x):
return sum(i * i for i in range(10_000))

if __name__ == '__main__':
data = range(100)

start = time.time()

with Pool() as pool:
results = pool.map(compute, data)

print("Time with multiprocessing:", time.time() - start)


The same logic in a simple for loop without multiprocessing is significantly faster. I'm running this on a machine with 8 CPU cores. What could be causing this slowdown? Is it related to overhead in process creation or data serialization?

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