Multiprocessing is a powerful technique in Python for leveraging multiple CPU cores to execute tasks concurrently. It's particularly useful for CPU-bound operations, such as data processing or scientific computations.

Key Concepts 📚

  • Process: Each independent unit of execution in a multiprocessing program
  • Pool: Manages a pool of worker processes to parallelize tasks
  • Multiprocessing Module: Built-in Python library for creating and managing processes
  • Parallelism vs Concurrency:
    • Parallelism executes tasks simultaneously (e.g., multi-core processing)
    • Concurrency manages tasks to appear simultaneous (e.g., multi-threading)

Common Use Cases 🚀

  • Batch data processing 📊
  • Scientific simulations 🔬
  • Web scraping (with caution) 📌
  • Game development (for physics engines) 🎮

Code Example 🧾

import multiprocessing

def worker(num):
    """Thread worker function"""
    print(f"Worker {num} is running")
    return

if __name__ == "__main__":
    processes = []
    for i in range(4):
        p = multiprocessing.Process(target=worker, args=(i,))
        processes.append(p)
        p.start()
    
    for p in processes:
        p.join()

Further Reading 📚

Multiprocessing Python
Process Pool