What is the difference between threading and multiprocessing in Python and what tasks are they used for?

In Python, threading and multiprocessing are two approaches to parallelism, but they differ in mechanism and application.

• threading — is multithreading within a single process. Threads share memory, which makes data exchange easier, but due to GIL (Global Interpreter Lock), only one thread executes Python bytecode at a time. Therefore, threading is effective for I/O-bound tasks, such as network requests, file reading.

• multiprocessing creates multiple processes, each with its own memory and Python interpreter. This allows bypassing GIL and using multiple CPU cores for compute-heavy tasks (CPU-bound), such as numerical calculations, processing large data.

Example of using multiprocessing for parallel computations:

from multiprocessing import Pool

def f(x):
    return x*x

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

In summary:

• Use threading for I/O-bound tasks.
• Use multiprocessing for CPU-bound tasks requiring parallel computation.