How does the Python multiprocessing library improve performance?

How does the Python multiprocessing library improve performance?

The Python multiprocessing library allows you to create multiple processes, enabling parallel execution of tasks. This can significantly improve performance, especially for CPU-bound tasks, by utilizing multiple cores.

Can you give me an example of how to use it?

Sure! Here's a simple example:

import multiprocessing

def square(n):
    return n * n

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

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

What are the limitations of using multiprocessing?

Some limitations include increased memory usage, complexity in sharing data between processes, and potential overhead from process creation. It's also not suitable for I/O-bound tasks where threading might be more efficient.

When should I prefer multiprocessing over threading?

You should prefer multiprocessing for CPU-bound tasks that require heavy computation, as it can take full advantage of multiple CPU cores. For I/O-bound tasks, threading might be more efficient due to lower overhead.