THIS COURSE IS POSTPONED TO AN YET UNDETERMINED DATE.
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 covered:
- Introduction to Jupyter
- Parallel computing using Jupyter
- Coupling and control of simulations
- Interactive & in-situ visualization
- Simulation dashboards
Prerequisites: Experience in Python
Registrations are only considered until 20 March 2020 due to available space, the maximal number of participants is limited. Applicants will be notified, whether they are accepted for participitation.
Instructors: Jens Henrik Göbbert, Alice Grosch, JSC
For any questions concerning the course please send an e-mail to firstname.lastname@example.org