Python provides two ways to work around this issue: threading and multiprocessing. Each approach allows you to break a long-running job into parallel batches, which you can work on side-by-side.

For parallelism, Python offers multiprocessing, which launches multiple instances of the Python interpreter, each one running independently on its own hardware thread.. All three of these ...

We’ll walk through the difference between threads and processes in a Python context, before reviewing some of the different approaches you can take and what they’re best suited for. ( Python 3 ...

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, ...

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 ...