lang/python/ MultiprocessingVsThreading1

• asyncio is useful for I/O bound tasks, not for CPU bound tasks.
• threading suffers from the Global Interpreter Lock aka GIL preventing more than one thread executing python at the same time (multiple threads can execute C code in parallel).
• multiprocessing allows using separate processes.

Multiprocessing Module

See docs here:

The multiprocessing module also introduces APIs which do not have analogs in the threading module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,

from multiprocessing import Pool

def f(x):
    return x*x

if __name__ == '__main__':
    with Pool(5) as p:
        print(p.map(f, [1, 2, 3]))