Multiprocessing vs Threading in Python: Pros & Cons

Python offers two parallelism techniques: multiprocessing and threading. Both methods aim to optimize application performance by running tasks concurrently. In this article, we'll explore the differences between multiprocessing and threading, their pros and cons, and ideal use cases for each technique.

Table of Contents

  1. Understanding Multiprocessing
  2. Understanding Threading
  3. Multiprocessing: Pros and Cons
  4. Threading: Pros and Cons
  5. When to Use Multiprocessing or Threading
  6. Conclusion

Understanding Multiprocessing

Multiprocessing is a technique that allows a program to run multiple processes concurrently. In Python, the multiprocessing module enables the programmer to create multiple processes, each with its own Python interpreter and memory space. This makes multiprocessing ideal for CPU-bound tasks, as it takes full advantage of multiple CPU cores.

import multiprocessing

def worker(num):
    print(f'Worker {num}')

if __name__ == '__main__':
    processes = []
    for i in range(5):
        p = multiprocessing.Process(target=worker, args=(i,))

    for p in processes:

Understanding Threading

Threading is another parallelism technique that runs multiple threads within a single process. In Python, the threading module enables programmers to create multiple threads, each with its own stack and local variables, but sharing the same global variables and memory space. Threading is more suited for I/O-bound tasks, where waiting for I/O operations to complete is the main bottleneck.

import threading

def worker(num):
    print(f'Worker {num}')

if __name__ == '__main__':
    threads = []
    for i in range(5):
        t = threading.Thread(target=worker, args=(i,))

    for t in threads:

Multiprocessing: Pros and Cons


  1. Avoids GIL limitations: The Global Interpreter Lock (GIL) is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes simultaneously. Multiprocessing bypasses the GIL, allowing multiple processes to run concurrently and utilize multiple CPU cores.
  2. Isolation: Each process runs in its own memory space, so a bug or memory leak in one process won't affect other processes.


  1. Higher memory usage: Each process requires its own memory space, leading to higher memory consumption compared to threads.
  2. Communication overhead: Inter-process communication (IPC) can be slower than inter-thread communication, as processes don't share memory space.

Threading: Pros and Cons


  1. Lower memory usage: Threads share the same memory space, consuming less memory compared to multiple processes.
  2. Faster communication: Inter-thread communication can be faster than IPC, as threads share memory space.


  1. GIL limitations: The GIL can limit the performance of CPU-bound tasks in multi-threaded programs.
  2. Concurrency issues: Sharing memory space can lead to data corruption and race conditions if not handled properly.

When to Use Multiprocessing or Threading

  • Multiprocessing: Use multiprocessing for CPU-bound tasks, where parallel execution can significantly improve performance. Also, consider using multiprocessing when isolation between tasks is important.
  • Threading: Use threading for I/O-bound tasks, where waiting for I/O operations to complete is the main bottleneck. Threading is also suitable for tasks that require frequent communication and data sharing between threads.


Understanding the differences between multiprocessing and threading is crucial for optimizing Python application performance. Multiprocessing is well-suited for CPU-bound tasks, while threading is ideal for I/O-bound tasks. By choosing the right technique for your specific use case, you can improve your application's performance and efficiency.

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