Should computational tasks be performed using multiple threads or is it better to involve multiple processes?

In Python, the choice between multithreading and multiprocessing depends on the nature of the task:

• Multithreading is suitable for I/O-bound tasks, such as network requests or file reading. This is because Python has a GIL (Global Interpreter Lock) that prevents multiple threads from executing Python bytecode simultaneously, limiting the effectiveness of multithreading for CPU-bound tasks.

• Multiprocessing is better for compute-intensive tasks, as each process has its own interpreter and memory, bypassing the GIL restriction. This allows efficient utilization of multiple CPU cores.

Example of using multiprocessing for a computational task:

from multiprocessing import Pool

def compute(x):
    return x * x

if __name__ == '__main__':
    with Pool(4) as p:
        results = p.map(compute, range(10))
    print(results)

Thus, for computational tasks, it is preferable to use multiple processes.