How does parallelism in Python compare to other programming languages?

How does parallelism in Python compare to other programming languages?

Python offers several parallelism options like threading, multiprocessing, and async. However, due to the Global Interpreter Lock (GIL), true parallel execution is limited in CPython. In contrast, languages like Java or C++ can achieve better performance with native threads.

What are the main types of parallelism available in Python?

The main types of parallelism in Python are:

1. Threading: Useful for I/O-bound tasks.
2. Multiprocessing: Suitable for CPU-bound tasks, bypassing the GIL.
3. Asyncio: Ideal for handling asynchronous I/O operations.

Can you give an example of using multiprocessing in Python?

Sure! Here's a simple example:

from multiprocessing import Process

def square(n):
    print(f'Square of {n} is {n * n}')

if __name__ == '__main__':
    processes = []
    for i in range(5):
        p = Process(target=square, args=(i,))
        processes.append(p)
        p.start()

    for p in processes:
        p.join()

This code creates multiple processes to compute the square of numbers.

What are the performance implications of using threading vs multiprocessing?

Threading is generally more lightweight and suitable for I/O-bound tasks, but it can be limited by the GIL. Multiprocessing, on the other hand, can utilize multiple CPU cores effectively, making it better for CPU-bound tasks, but it has higher overhead due to process creation.