What types of tasks can be implemented using the multiprocessing module in Python?

The multiprocessing module in Python allows creating processes for parallel execution of tasks, which is especially useful for bypassing the GIL (Global Interpreter Lock) limitation.

Main types of tasks that can be implemented using multiprocessing:

• Parallel data processing — for example, processing large data arrays, where each process works with a part of the data.

• Performing CPU-intensive computations — for example, numerical calculations, where multiprocessing speeds up execution.

• Parallel execution of independent tasks — for example, running multiple functions simultaneously.

• Creating process pools (Pool) for convenient task distribution and result collection.

Example of usage:

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]

Thus, multiprocessing helps efficiently utilize multiple CPU cores to accelerate program execution.