What techniques for task parallelization are available in Python and how do they use multithreading or multiprocessing?

Python provides several techniques for parallelizing tasks, which use either multithreading or multiprocessing:

1. Multithreading:

   • The threading module allows creating threads within a single process.
   • Due to GIL (Global Interpreter Lock) in the standard CPython implementation, threads cannot execute Python bytecode simultaneously, which limits parallelism in CPU-intensive tasks.
   • Suitable for I/O-bound tasks, such as network operations.

2. Multiprocessing:

   • The multiprocessing module creates separate processes, each with its own memory and interpreter.
   • Bypasses GIL limitations and efficiently utilizes multiple CPU cores.
   • Suitable for CPU-bound tasks.

3. Asynchronous programming (asyncio):

   • Allows writing non-blocking code for I/O operations.
   • Does not create new threads or processes but uses an event loop.

Example of using multiprocessing:

from multiprocessing import Pool

def f(x):
    return x * x

if __name__ == '__main__':
    with Pool(4) as p:
        results = p.map(f, [1, 2, 3, 4])
    print(results)  # [1, 4, 9, 16]

Thus, for CPU-intensive tasks, it is better to use multiprocessing, and for I/O-bound tasks — threading or asyncio.