Optimize and Scale Python Code
for High-Performance Computing
Topics covered
Python is making inroads into the HPC landscape. However, writing Python code for efficient scientific computing is not entirely trivial. In this course a variety of techniques and libraries will be discussed that are useful in this context. Subjects covered include profiling of code to discover opportunities for optimization, using Cython, a Python extension that translate critical code sections into efficient C, wrapping C/C++/Fortran libraries in Python, multithreaded/multiprocess Python, distributed programming use mpi4py, and pySpark for data science.
Target audience
This info session is primarily targeted at VSC users, although other interested parties are welcome as well.
Previous knowledge
Participants have programming experience in Python and preferably also in C/C++ or Fortran.
Level: intermediate
Result/Objectives
Participants can make an informed choice on various techniques to improve the performance of Python code, and know how to call C/C++ or Fortran functions from Python.
Dates & Location
You can take the training online or at the training site.
You will be sent the link a few days before the start of the course.
24/11/2026: 9 a.m.-1 p.m.
25/11/2026: 9 a.m.-1 p.m.