How can I optimize parallel programs in Python?

How can I optimize parallel programs in Python?

You can optimize parallel programs in Python by using libraries like multiprocessing, concurrent.futures, and joblib. What specific aspect are you looking to improve?

I'm interested in reducing execution time for data processing tasks.

For data processing, consider using the multiprocessing library to distribute tasks across multiple CPU cores. Have you tried using it?

Not yet, can you provide a simple example?

Sure! Here's a basic example using multiprocessing:

from multiprocessing import Pool

def square(n):
    return n * n

if __name__ == '__main__':
    with Pool(4) as p:
        results = p.map(square, range(10))
    print(results)

This will compute the square of numbers from 0 to 9 in parallel.

That looks great! How do I handle exceptions in parallel tasks?

You can handle exceptions by using a try-except block within the worker function. If an exception occurs, you can return a custom error message.