This course will take place as an online event. The link to the streaming platform will be provided to the registrants only. The course will be held in English.
Course Contents:
Python is increasingly used in high-performance computing projects. It can be used directly or as a high-level interface to existing HPC applications and libraries.
This course combines lectures and hands-on sessions. We will show how Python can be used for simulation on parallel architectures and how to optimise critical parts of the code using various tools.
The following topics will be covered:
- Short review of vectorised programming with NumPy
- Interactive parallel programming with IPython
- Profiling and optimisation
- High-performance NumPy
- Just-in-time compilation with Numba
- Distributed-memory parallel programming with Python and MPI
- Bindings to other programming languages and HPC libraries
- Interfaces to GPUs
This course does not cover any AI frameworks nor high performance data analysis.
This course is aimed at scientists who wish to explore the productivity gains made possible by Python for HPC.
Contents level
in hours
in %
Beginner's contents:
0 h
0 %
Intermediate contents:
11 h
62 %
Advanced contents:
7 h
38 %
Community-targeted contents:
0 h
0 %
Prerequisites:
Good working knowledge of Python and NumPy
A personal institutional email address (university/research institution, government agency, organisation, or company) is required to register for JSC training courses. If you don't have an institutional email address, please get in touch with the contact person for this course.
Target Audience:
Scientists who want to use Python on supercomputers
Language:
This course is given in English.
Duration:
5 half days
Dates:
15-19 June 2026, 09:00-13:00 each day
Venue:
Online
Number of Participants:
Minimum 5
Instructors:
Jan Meinke, Olav Zimmermann (JSC)
Fees
This course is offered free of charge.
Pre-required logistics
Good working knowledge of Python and NumPy