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Interactive High-Performance Computing with Jupyter (PRACE training course, online)

This course will take place as an online event. The link to the streaming platform will be provided to the registrants only.


20 Apr 2021 09:00
22 Apr 2021 13:00


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

This course is a PRACE training course.


Contents levelin hoursin %
Beginner's contents:2.420 %
Intermediate contents:9.680 %
Advanced contents:00 %
Community-targeted contents:00 %


Experience in Python

Target audience:

Scientists who want to use interactive HPC for research.


This course is given in English.

Learning outcome:

After this course participants will have a general understanding how to approach data analysis problems in a systematic way. In particular this course will provide insights into key benefits of parallelization such as during the n-fold cross-validation process where significant speed-ups can be obtained compared to serial methods. Participants will also get a detailed understanding why and how parallelization provides benefits to a scalable data analyzing process using machine learning methods for big data and a general understanding for which problems deep learning algorithms are useful and how parallel and scalable computing is facilitating the learning process when facing big datasets. Participants will learn that deep learning can actually perform ‘feature learning’ that bears the potential to significantly speed-up data analysis processes that previously required much feature engineering.


This course is given in Englisch.


3 half days


20-22 April 2021, 09:00-13:00



Number of Participants:

maximum 25


Jens Henrik Göbbert, Alice Grosch, JSC


Photo Jens Henrik Göbbert
Jens Henrik Göbbert
Phone: +49 2461 61-96498


Please register until 1 April 2021 via the form at the PRACE web site (will be available in January)