Speakers
Ariel Lozano
(ULB)
Nicolas Potvin
(ULB département informatique)
Description
Python has become one of the most widely used languages in scientific computing thanks to libraries such as NumPy, SciPy, and Matplotlib. However, writing efficient Python applications on HPC systems often requires going beyond basic usage of these libraries. This session focuses on performance-oriented Python programming, showing how to deploy scientific Python environments on CÉCI clusters, exploit parallelism, and accelerate applications through compilation and optimization techniques.
| Contents | Information |
|---|---|
| • Managing Python environments and installing libraries • Efficient numerical computing with NumPy • Scientific computing with SciPy • Performance analysis and optimization techniques • Multithreading and multiprocessing in Python • Understanding the Global Interpreter Lock (GIL) • Accelerating Python with compiled extensions • JIT compilation with Numba and related tools • Best practices for running Python applications on HPC systems |
Prerequisite: • Being able to use SSH with private keys • Being familiar with a text editor • Mastering the Linux command line and GNU utilities (mkdir, cp, scp, etc.) • Working knowledge of Python **Type |