Requirements

To exploit the proposed contents of this course, it is recommend to have some experience in Python programming language.

Schedule

Total hours: 15 hours.

• Class 1: Monday 13/3, 14:00hs to 17:00hs
• Class 2: Tuesday 14/3, 14:00hs to 17:00hs
• Class 3: Wednesday 15/3, 14:00hs to 17:00hs
• Class 4: Thursday 16/3, 14:00hs to 17:00hs
• Class 5: Friday 17/3, 14:00hs to 17:00hs

Short Program

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.

Objetives

Topics:

The following topics are covered:

• Introduction to Jupyter
• Parallel computing using Jupyter
• Interactive & in-situ visualization
• From ipywidgets to dashboards