What is the difference between threading and multiprocessing

Keywords: threading, multiprocessing, Python, parallelism, concurrency
Description: This article explains the differences between threading and multiprocessing in Python, highlighting their use cases, advantages, and examples.

Threading and multiprocessing are two ways to achieve concurrent execution in programming. Here’s a brief comparison:

  • Threading: Involves creating multiple threads within a single process. Threads share the same memory space, which makes them lightweight and efficient for I/O-bound tasks. However, Python's Global Interpreter Lock (GIL) limits true parallel execution of threads in CPU-bound tasks.
  • Multiprocessing: Launches separate processes, each with its own memory space. This allows for true parallelism and is suitable for CPU-bound tasks. It avoids GIL limitations but has higher overhead due to inter-process communication.

Here’s a simple example of both threading and multiprocessing in Python:

<?php // Threading example class MyThread extends Thread { public function run() { // Your thread code here echo "Running in thread\n"; } } $thread = new MyThread(); $thread->start(); // Multiprocessing example $process = popen('php -r "echo \"Running in process\";"', 'r'); fclose($process); ?>

Keywords: threading multiprocessing Python parallelism concurrency

Related Questions

How do I avoid rehashing overhead with std::set in multithreaded code?

How do I find elements with custom comparators with std::set for embedded targets?

How do I erase elements while iterating with std::set for embedded targets?

How do I provide stable iteration order with std::unordered_map for large datasets?

How do I reserve capacity ahead of time with std::unordered_map for large datasets?

How do I erase elements while iterating with std::unordered_map in multithreaded code?

How do I provide stable iteration order with std::map for embedded targets?

How do I provide stable iteration order with std::map in multithreaded code?

How do I avoid rehashing overhead with std::map in performance-sensitive code?

How do I merge two containers efficiently with std::map for embedded targets?