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 ...

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 ...

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 ...

Free-threaded Python is now officially supported, though using it remains optional. Here are four tips for developers getting started with true parallelism in Python. Until recently, Python threads ...

At work, I use a Python tool that automates browser operations. It launches several browsers in parallel to process hundreds ...

One of the biggest changes to come to the Python world is the addition of the free-threading interpreter, which eliminates the global interpreter lock (GIL) that kept the interpreter thread-safe, but ...

Peyton McCullough illustrates Python threads in a Dev Shed article. "If you want your application to perform several tasks at once, you can use threads. Python can handle threads, but many developers ...

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 ...

Think it's complex to connect your Python program to the UNIX shell? Think again! In past articles, I've looked into concurrency in Python via threads (see "Thinking Concurrently: How Modern Network ...