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 likely ...

Your Python cache can run the same work twice, even with the GIL. See the check-then-act race, the lru_cache gotcha, and how ...

The ability to execute code in parallel is crucial in a wide variety of scenarios. Concurrent programming is a key asset for web servers, producer/consumer models, batch number-crunching and pretty ...

Threads can provide concurrency, even if they're not truly parallel. In my last article, I took a short tour through the ways you can add concurrency to your programs. In this article, I focus on one ...

When running multiple processes in parallel, one thing to be careful about is the handling of shared data.For example, ...

LWN.net is a subscriber-supported publication; we rely on subscribers to keep the entire operation going. Please help out by buying a subscription and keeping LWN on the net. Parallelism comes down to ...

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 processing ...

It's been in the works for a while.

It is obvious that global variable are always shared and in most cases this isn’t a problem. However, suppose you have some existing code that makes use of a global variable to store its state and we ...