How is parallel execution of tasks achieved using multiprocessing?

In Python, concurrency can be organized using several methods:

1. Threads (threading) — suitable for I/O-bound tasks, such as network or file operations. I used threading.Thread to run multiple tasks simultaneously.

2. Processes (multiprocessing) — for CPU-bound tasks, to bypass GIL limitations. Used the multiprocessing module for parallel data processing or computations.

3. Async programming (asyncio) — for managing many I/O operations efficiently without creating many threads. Used async def and await for non-blocking code.

4. Locks and synchronization — used threading.Lock, RLock to protect shared resources.

Example with threads:

import threading

def worker():
    print("Work in thread")

threads = []
for i in range(5):
    t = threading.Thread(target=worker)
    t.start()
    threads.append(t)

for t in threads:
    t.join()

Thus, the choice depends on the task: I/O-bound — threads or asyncio, CPU-bound — processes.