Processes are essential for parallel computing in Python. Here's a concise guide to mastering multiprocessing:

🧠 What is Multiprocessing?

Multiprocessing allows programs to execute multiple tasks simultaneously by utilizing multiple CPU cores.

multiprocessing_concepts

Key Benefits:

  • Parallel Execution 🚀
  • Improved Performance 📈
  • Utilizes System Resources 💡

🧪 Using multiprocessing Module

Python’s multiprocessing module provides tools for creating and managing processes.

Example:

import multiprocessing  

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

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

Processes can communicate via:

  1. multiprocessing.Queue 📤
  2. multiprocessing.Pipe 🧩
  3. Shared Memory 🧱

For deeper insights, check our Python Concurrency Guide 📚.

⚠️ Common Pitfalls

  • Global Interpreter Lock (GIL) 🚫
    • Limits true parallelism in CPython
  • Resource Management 🧹
    • Avoid memory leaks and deadlocks
  • Security Risks 🔒
    • Be cautious with untrusted input

📌 Further Reading

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