Real Python: Speed Up Your Python Program With Concurrency

Concurrency in programming refers to the ability of a program to manage multiple tasks at once, improving performance and responsiveness. It encompasses different models like threading, asynchronous tasks, and multiprocessing, each offering unique benefits and trade-offs. In Python, threads and asynchronous tasks facilitate concurrency on a single processor, while multiprocessing allows for true parallelism by utilizing multiple CPU cores. Understanding concurrency is crucial for optimizing programs, especially those that are I/O-bound or CPU-bound. Efficient concurrency management can significantly enhance a program’s performance by reducing wait times and better utilizing system resources. Concurrency can take different forms depending on the problem it aims to solve, and in Python, it can be achieved through threads, tasks, or processes. Threads and asynchronous tasks always run on a single processor, taking turns to speed up the overall process, while processes can run on different CPU cores, enabling true parallelism. The way threads, tasks, or processes take turns differs, with multi-threaded approaches using preemptive multitasking and asynchronous tasks using cooperative multitasking. In a multi-threaded approach, the operating system can interrupt a thread at any time to start running a different thread, while asynchronous tasks must cooperate with each other by announcing when they’re ready to be switched out. Multiprocessing, on the other hand, allows each process to run in its own Python interpreter, enabling true parallelism by running on different CPU cores. Python's concurrency models have different implications for program design and execution, and understanding these differences is essential for choosing the appropriate concurrency model based on a program's needs. By leveraging concurrency effectively, developers can significantly improve the performance and responsiveness of their programs.