Python lets you parallelize workloads using threads, subprocesses, or both. Here's what you need to know about Python's thread and process pools and Python threads after Python 3.13.

Learn how to use Python’s async functions, threads, and multiprocessing capabilities to juggle tasks and improve the responsiveness of your applications. If you program in Python, you have most ...

Ruby and Python's standard implementations make use of a Global Interpreter Lock. Justin James explains the major advantages and downsides of the GIL mechanism. Multithreading and parallel ...

Python knows that I/O can take a long time, and so whenever a Python thread engages in I/O (that is, the screen, disk or network), it gives up control and hands use of the GIL over to a different ...

The GIL is controversial because it only allows one thread at a time to access the Python interpreter. This means that it’s often not possible for threads to take advantage of multi-core systems.

Python can’t thread across cores. Python apps can do a multithreading, but those threads can’t run across cores. It all happens on a single, solitary CPU, no matter how many CPUs exist in the system.

Python's "multiprocessing" module feels like threads, but actually launches processes. Many people, when they start to work with Python, are excited to hear that the language supports threading. And, ...

I have worker thread(s) that use the logger. In the main thread, I occasionally need to ask the user to take some action. Which means I need to suppress the ...

Simultaneous multi-threading is a useful performance tool for processor designers, but it doesn't have the same benefit that adding a full core does.

Standard Modes vs. Hyper-Threading Applications must be multithreaded in order to take advantage of Hyper-Threading whether in single-core or dual-core machines. See dual core.