Python's Multiprocessing and Threading

As programs grow, performance can become an issue. Python offers threading and multiprocessing to help with concurrent execution. Threading is suitable for I/O-bound tasks, like waiting for network responses or file reads. The threading module lets you create and manage threads. However, due to the Global Interpreter Lock (GIL), threads in Python do not execute Python bytecode in parallel for CPU-bound tasks. For CPU-bound tasks, you should use multiprocessing. The multiprocessing module spawns separate processes, each with its own Python interpreter and GIL. This allows true parallel execution across CPU cores. You can use a Pool to distribute tasks across processes. For example, you might use multiprocessing to process a large list of numbers and apply a computationally expensive function in parallel. Asynchronous programming with asyncio is another approach for concurrent I/O. It uses a single thread but can handle many tasks concurrently using coroutines. Understanding these concurrency models is important for building efficient applications. A practical project is to write a web scraper that downloads multiple pages simultaneously. Compare the performance of sequential, threaded, and multiprocess approaches to see the differences.