What happens when using asyncio for a CPU-intensive task?

Using asyncio for CPU-intensive tasks is inefficient because asyncio is based on a single-threaded event loop and is designed for asynchronous I/O.

When executing CPU-bound tasks, the event loop gets blocked, leading to delays and reduced performance, as other asynchronous tasks cannot run in parallel.

For such tasks, it is better to use:

• Multithreading — suitable if the task does not heavily load the CPU due to Python's GIL.
• Multiprocessing — allows running multiple processes, bypassing the GIL and effectively utilizing multiple CPU cores.

Example of using multiprocessing for a CPU-intensive task:

import multiprocessing

def cpu_bound_task(x):
    # example computational task
    return sum(i*i for i in range(x))

if __name__ == '__main__':
    with multiprocessing.Pool() as pool:
        results = pool.map(cpu_bound_task, [10**6, 10**7, 10**8])
    print(results)

Thus, asyncio is better suited for I/O-bound tasks, while for CPU-bound tasks, multiprocessing or other approaches are recommended.