When performing resource-intensive tasks, which approach is preferable — using shared threads or separate processes?

For resource-intensive tasks in Python, it is preferable to use separate processes rather than shared threads. This is due to the characteristics of the CPython interpreter and its Global Interpreter Lock (GIL), which prevents multiple threads from executing Python bytecode simultaneously.

Using threads (threading) is suitable for I/O-bound tasks, but for compute-intensive tasks, they do not provide a performance boost because of the GIL.

Instead, it is recommended to use the multiprocessing module, which creates separate processes with their own Python interpreter and memory, allowing for effective parallel computation:

from multiprocessing import Pool

def heavy_task(x):
    # resource-intensive operation
    return x * x

with Pool(processes=4) as pool:
    results = pool.map(heavy_task, range(10))

Thus, for CPU-bound tasks, it is better to use processes, and for I/O-bound tasks, threads.