What types of concurrency are suitable for optimizing the execution of CPU-intensive tasks?

For tasks requiring intensive CPU computations, the optimal types of concurrency in Python are:

• Multiprocessing: since the Global Interpreter Lock (GIL) limits the concurrent execution of threads in Python, using multiple processes allows for effective parallelization of computations across multiple CPU cores.

• Using libraries with native code: for example, NumPy, which implements computations in C and releases the GIL.

• Parallel execution using concurrent.futures.ProcessPoolExecutor — a convenient interface for running tasks in separate processes.

An example of using multiprocessing to compute factorials in multiple processes:

from multiprocessing import Pool

def factorial(n):
    return 1 if n == 0 else n * factorial(n-1)

if __name__ == '__main__':
    numbers = [5, 7, 10, 12]
    with Pool() as pool:
        results = pool.map(factorial, numbers)
    print(results)

Thus, for CPU-intensive tasks, it is better to use multiprocessing rather than multithreading.