Multiprocessing in Python

1. Introduction

Multiprocessing in Python allows the execution of multiple processes simultaneously. This is particularly useful for CPU-bound tasks that can benefit from parallelism.

2. Key Concepts

• Process: An instance of a program in execution.
• Thread: A lightweight process that shares the same memory space.
• Concurrency: Running multiple tasks at the same time (not necessarily simultaneously).
• Parallelism: Performing multiple operations simultaneously.

3. Getting Started

To use the multiprocessing module, you first need to import it. Below is a basic setup:

import multiprocessing

def worker():
    print("Worker Function")

if __name__ == "__main__":
    p = multiprocessing.Process(target=worker)
    p.start()
    p.join()

4. Code Example

Here is a more detailed example that demonstrates the use of multiple processes.

import multiprocessing
import time

def square(n):
    time.sleep(1)
    print(f'Square of {n} is {n * n}')

if __name__ == "__main__":
    processes = []
    numbers = [1, 2, 3, 4, 5]

    for number in numbers:
        p = multiprocessing.Process(target=square, args=(number,))
        processes.append(p)
        p.start()

    for p in processes:
        p.join()

5. Best Practices

• Always protect the entry point of the program by using if __name__ == "__main__".
• Use a Pool for managing multiple processes efficiently.
• Be cautious of shared data; use Manager or Lock when necessary.
• Keep track of processes and ensure they are properly joined.

6. FAQ