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.

Advantages of Python multiprocessing. With threading and coroutines, the Python runtime forces all operations to run serially, the better to manage access to any Python objects.

We think it’s awesome that Python manages to keep the same syntax between the threading and multiprocessing modules, when the action taking place under the hood is so different.

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, ...

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 ...

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 ...