How can I optimize multiprocessing in Python?

How can I optimize multiprocessing in Python?

To optimize multiprocessing in Python, consider using the multiprocessing module effectively by minimizing inter-process communication and maximizing CPU usage.

What are some common pitfalls to avoid?

Common pitfalls include excessive use of shared resources, which can lead to bottlenecks, and not properly managing the number of processes.

Can you give me an example of using a Pool?

Sure! You can use Pool to parallelize a function across multiple input values. Here's a simple example:

from multiprocessing import Pool

def square(x):
    return x * x

if __name__ == '__main__':
    with Pool(4) as p:
        results = p.map(square, [1, 2, 3, 4])
    print(results)

How do I handle exceptions in multiprocessing?

You can handle exceptions in multiprocessing by using a try-except block within the target function. Additionally, you can use apply_async with a callback to handle results or errors.