The concurrent.futures module provides a simple, high-level interface for executing tasks concurrently using either threads or processes, making parallel programming accessible without dealing with low-level threading or multiprocessing details.
The multiprocessing module creates separate Python processes that bypass the GIL, enabling true parallel execution for CPU-intensive tasks.
Unlike threading, multiprocessing actually uses multiple CPU cores simultaneously - each process has its own Python interpreter and memory space!
The threading module enables concurrent execution through threads with synchronization primitives like Lock, Event, and Semaphore for safe resource sharing.
Python’s Global Interpreter Lock (GIL) means only one thread executes Python code at once - threading helps with I/O-bound tasks, not CPU-bound ones!