Python Multiprocessing

Python multiprocessing is a powerful module that enables parallel processing in Python applications. It allows you to leverage multiple CPU cores, significantly improving performance for CPU-bound tasks.

What is Multiprocessing?

Multiprocessing is the ability to run multiple processes concurrently, each with its own memory space. This is different from Python Multithreading, which runs multiple threads within a single process.

Why Use Multiprocessing?

• Bypass the Global Interpreter Lock (GIL)
• Utilize multiple CPU cores
• Improve performance for CPU-intensive tasks
• Achieve true parallelism

Basic Usage

To use multiprocessing, you need to import the module and create Process objects:

import multiprocessing

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

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

Pool of Workers

For multiple tasks, you can create a pool of worker processes:

from multiprocessing import Pool

def f(x):
    return x*x

if __name__ == '__main__':
    with Pool(5) as p:
        print(p.map(f, [1, 2, 3]))
    

Sharing Data Between Processes

Multiprocessing provides various ways to share data between processes:

• Queue: Thread and process safe
• Pipe: High performance for connecting two processes
• Value and Array: Shared memory for simple objects
• Manager: For more complex objects

Best Practices

1. Use if __name__ == '__main__': to prevent recursive spawning of processes
2. Be cautious with shared resources to avoid race conditions
3. Consider using Pool for managing multiple worker processes
4. Use Python Profiling to identify bottlenecks before implementing multiprocessing

Conclusion

Python multiprocessing is a powerful tool for improving performance in CPU-bound applications. By understanding its capabilities and best practices, you can effectively parallelize your Python code and take full advantage of modern multi-core processors.