What methods are implemented in Python for executing tasks in parallel or concurrently?

In Python, several approaches and libraries are used for parallel or concurrent tasks:

1. threading module — for creating threads. Suitable for I/O-bound tasks, but does not speed up CPU-bound tasks due to GIL.

2. multiprocessing module — for creating processes. Bypasses GIL and efficiently utilizes multiple CPU cores.

3. asyncio module — for asynchronous programming using coroutines. Well-suited for a large number of I/O operations.

4. High-level libraries:

   • concurrent.futures.ThreadPoolExecutor and ProcessPoolExecutor — convenient interfaces for running tasks in threads or processes.

Example of using concurrent.futures:

from concurrent.futures import ThreadPoolExecutor

def task(n):
    print(f"Task {n} is running")

with ThreadPoolExecutor(max_workers=3) as executor:
    for i in range(5):
        executor.submit(task, i)

Thus, the choice of method depends on the type of task: for CPU-bound — multiprocessing, for I/O-bound — threading or asyncio.