What are effective strategies for achieving true parallelism in CPU-bound Python programs?

True parallelism for CPU-bound tasks in Python is best achieved using the multiprocessing module, which runs multiple processes to bypass the Global Interpreter Lock (GIL). This allows concurrent execution on multiple CPU cores efficiently.

The multiprocessing module enables true CPU parallelism by creating separate processes that run in parallel on multiple CPU cores, avoiding GIL limitations. This approach is suitable for CPU-intensive computations where threading fails to provide parallel speedup.

Code

from multiprocessing import Pool, cpu_count

def cpu_task(x):
    # CPU-intensive task here
    return x * x

if __name__ == "__main__":
    with Pool(cpu_count()) as p:
        results = p.map(cpu_task, range(10))
    print(results)