Explain the difference between threading and multiprocessing in Python?

Threading vs. Multiprocessing in Python

The difference between threading and multiprocessing in Python lies in how they achieve concurrency and utilize system resources:

Threading:

• In threading, multiple threads are run within a single process. Threads share the same memory space, making it easier to share data between them.
• Python's threading module (threading) allows you to create and manage threads. However, due to the Global Interpreter Lock (GIL) in CPython, only one thread can execute Python bytecode at a time, limiting performance improvement for CPU-bound tasks.
• Generally, threading is more suitable for I/O-bound tasks, such as reading from files, making network requests, or waiting for user input.
• Due to the GIL, multiple threads cannot fully utilize multiple CPU cores for CPU-bound tasks.

Multiprocessing:

• In multiprocessing, each process has its own memory space and Python interpreter. Each process operates independently, and multiple processes can utilize several CPU cores simultaneously.
• Python's multiprocessing module (multiprocessing) allows you to create and manage processes. With individual GILs for each process, Python bytecode can be executed concurrently and CPU cores can be fully utilized.
• It works well for CPU-bound tasks, where computations dominate and waiting for I/O operations is minimal.
• Since processes have separate memory spaces and inter-process communication can be complicated, creating and managing processes incurs higher overhead than threads.

In summary, threading in Python differs from multiprocessing in the following ways:

• Multiprocessing runs multiple processes in separate memory spaces, whereas threading runs multiple threads within a single process.
• When it comes to I/O-bound tasks, threading is better, while multiprocessing is better when it comes to CPU-bound tasks.
• In CPython, threading can be limited by the Global Interpreter Lock (GIL), whereas multiprocessing can fully utilize multiple CPU cores since each process has its own GIL.