What is the role of multiprocessor processing and in what scenarios is it used?

Multiprocessor processing in Python allows multiple processes to run in parallel, which is especially useful for tasks that require intensive computations or parallel resource handling.

Role of multiprocessing:

• Overcoming the limitations of GIL (Global Interpreter Lock), which prevents multiple Python threads from executing simultaneously.
• Increasing performance by utilizing multiple CPU cores.

Application scenarios:

• Computationally intensive tasks (e.g., processing large data arrays, scientific calculations).
• Parallel processing of independent tasks (e.g., handling multiple files or requests).
• Running isolated processes to enhance stability and security.

Example of using the multiprocessing module:

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, 5])
    print(results)  # [1, 4, 9, 16, 25]

Here, a pool of 4 processes is created, which compute the squares of numbers in parallel.