Interactive exploration and analysis of large amounts of data from scientific simulations, in-situ visualization and application control are convincing scenarios for explorative sciences. Based on the open source software Jupyter or JupyterLab, a way has been available for some time now that combines interactive with reproducible computing while at the same time meeting the challenges of support for the wide range of different software workflows.
Even on supercomputers, the method enables the creation of documents that combine live code with narrative text, mathematical equations, visualizations, interactive controls, and other extensive output. However, a number of challenges must be mastered in order to make existing workflows ready for interactive high-performance computing. With so many possibilities, it's easy to lose sight of the big picture. This course provides a detailed introduction to interactive high-performance computing.
The following topics are part of the course:
- Introduction to Jupyter
- Jupyter kernels customization
- JupyterLab for custom services
- Parallel computing using JupyterLab
Prerequisites:
Experience in Python
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 interactive HPC for research.
Language:
This course is given in English.
Duration:
2 half days
Dates:
28-29 April 2026, 09:00-13:00 each day
Venue:
Online
Number of Participants:
Maximum 40
Instructors:
Jens Henrik Göbbert, JSC
Fees
This course is offered free of charge.
Pre-required logistics
Experience in Python