08-29, 10:30–11:00 (Europe/Berlin), Room 6
Parallel computing is essential for many performance-critical applications. Python provides many solutions for this problem. New versions of Python will support sub-interpreters and a, currently experimental, free-threading version without the Global Interpreter Lock (GIL).
This talk starts with a short overview over this topic, clarifying terms such parallel, concurrent, and distribute computing as well as CPU-bound, memory-bound, and IO-bound problems. The presentation explains how Python and its standard library support parallel programming tasks. In addition, many Python libraries provide very useful approaches and tools for parallel computing. An overview of important libraries provides guidance which library can be used for what type of parallel problem.
How do Python's new features such as sub-interpreters and free-threading without the Global Interpreter Lock (GIL) impact parallel Programming in Python? This talk address this question by providing examples where these features might help to make programs simpler and/or faster.
Parallel computing is essential for many performance-critical applications. Python provides many solutions for this problem. New versions of Python will support sub-interpreters and a, currently experimental, free-threading version without the Global Interpreter Lock (GIL).
This talk starts with a short overview over this topic, clarifying terms such parallel, concurrent, and distribute computing as well as CPU-bound, memory-bound, and IO-bound problems. The presentation explains how Python and its standard library support parallel programming tasks. In addition, many Python libraries provide very useful approaches and tools for parallel computing. An overview of important libraries provides guidance which library can be used for what type of parallel problem.
How do Python's new features such as sub-interpreters and free-threading without the Global Interpreter Lock (GIL) impact parallel Programming in Python? This talk address this question by providing examples where these features might help to make programs simpler and/or faster.
Parallel Python Computing with Python version 3.13 and beyond -Use sub-interpreters and free-threading (no GIL)
Category [High Performance Computing] –Parallel Computing
Expected audience expertise: Domain –some
Expected audience expertise: Python –some
Python user since 1999. Python trainer since 2004. CEO and trainer at Python Academy. Serial Python conference organizer. Chair of Python Software Verband. PSF community award winner.