Multiprocessing is a powerful feature in Python that allows you to run multiple processes concurrently. This can significantly speed up your program's execution time, especially when dealing with CPU-bound tasks.

Key Concepts

  • Process: A process is an instance of a program that is being executed.
  • Multiprocessing: The technique of executing multiple processes at the same time.
  • GIL (Global Interpreter Lock): A mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes at once.

Why Use Multiprocessing?

  • Speed: Multiprocessing can provide a significant speedup for CPU-bound tasks.
  • Concurrency: You can run multiple processes concurrently, which can improve the responsiveness of your application.
  • Scalability: Multiprocessing allows you to scale your application to take advantage of multiple CPU cores.

Getting Started

To use multiprocessing in Python, you can use the multiprocessing module. Here's a simple example:

import multiprocessing

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

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

In this example, we create a new process using the Process class, passing in the worker function as the target. We then start the process and wait for it to finish using join().

Best Practices

  • Avoid sharing mutable data: Since processes do not share memory, you cannot directly share mutable data between processes. Instead, you can use communication mechanisms like pipes or queues.
  • Use process pools: The Pool class in the multiprocessing module allows you to create a pool of worker processes and distribute tasks among them.
  • Be cautious with I/O operations: I/O operations are not always safe to perform in parallel, as they may block the entire process.

Learn More

For more information on multiprocessing in Python, check out the following resources:

multiprocessing