We offer HPC training at beginning and advanced levels at all Hessian universities.
Our annual workshop series HiPerCH (High Performance Computing in Hessen) is complemented by tutorials about different HPC-topics.
Special cluster introduction courses take place at the hosting sites.
Our offers are free of charge.
| Date | Event | Topics | Location | Link |
|---|---|---|---|---|
| Proficiency Training High Performance Computing: HPC-Workflows | Online (Zoom) | Link | ||
| Digital HPC Café | Online (Big Blue Button) | Link | ||
| Data analysis in Python with Pandas | Python | Universität Kassel | Link | |
| Proficiency Training High Performance Computing: Version Control with GIT | Online (Zoom) | Link | ||
| Digital HPC Café | Online (Big Blue Button) | Link | ||
| Digital HPC Café | Online (Big Blue Button) | Link | ||
| Deep Learning on HPC Systems | Deep Learning | HRZ, TU Darmstadt, Building S1I22, 4th floor, Room "New York" | Link | |
| Proficiency Training High Performance Computing: Linux and Shell Scripting | Tools, Linux | Online (Zoom) | Link | |
| Introduction to Deep Learning | Deep Learning | Online (Zoom) | Link | |
| R on HPC Systems | R | Online (Zoom) | Link |
| Date | Event | Topics | Location | Link |
|---|---|---|---|---|
| - | Introduction to the Lichtenberg High Performance Computer | Hybrid (Alexanderstraße 2, 64283 Darmstadt, Room 203 "Berlin"/ Zoom) | Link | |
| - | Introduction to the Lichtenberg High Performance Computer | Hybrid (Alexanderstraße 2, 64283 Darmstadt, Room 203 "Berlin"/ Zoom) | Link | |
| - | Introduction to the Lichtenberg High Performance Computer | Hybrid (Alexanderstraße 2, 64283 Darmstadt, Room 203 "Berlin"/ Zoom) | Link |
In academia, software has become a crucial component of modern research and education. The role of software as a research output is gaining recognition. Research software is increasingly being published alongside traditional research papers, enabling other scientists to replicate experiments, build on existing work, and contribute to the development of shared tools.
However, sustainability, maintainablilty, testability and ease of use of the written software oftentimes fall short in scientific practice. Applying principles of software engineering and design can greatly improve software quality, thereby enabling others to readily use it, to understand and reproduce results. Ultimately, this enhances the overall scientific quality of the published results.
In this course, we dive into the practical aspects of software engineering and design specifically tailored for scientific software in order to make it more extensible, maintainable and testable. The principles we discuss can be applied to various types of codes such as software for numerical simulation as well as scripts for data processing.
We take the viewpoint of an academic software developer that is exposed to an existing code base with the task to add further functionality. Approaches to dealing with common obstacles like missing tests, interposed functional aspects and inherited technical dept will be discussed. Based on the lessons learned, we also explore how to structure and deploy software used for e.g. data processing.
During practical hands-on sessions, participants will interactively learn how to utilize software development techniques to tackle the aforementioned issues in practice in order to enhance the quality and reproducibility of their software.
Prerequisites
No items available.
