Neben der persönlichen Beratung bieten wir Ihnen HPC-Schulungen auf allen Niveaus an wechselnden hessischen Standorten.
Zusätzlich zu unserer jährlich stattfindenden Workshop-Serie HiPerCH (High Performance Computing in Hessen) werden auch Tutorien zu Spezialthemen angeboten.
An den Standorten finden spezielle Clustereinführungen statt.
Unsere Angebote sind kostenfrei.
| Date | Event | Topics | Location | Link |
|---|---|---|---|---|
| Digital HPC Café | Open consultation | Online (Big Blue Button) | Link | |
| R on HPC Systems | R | Online (Zoom) | Link | |
| HPC Bytes: Introduction to Performance Profiling | Online (Big Blue Button) | Link | ||
| Digital HPC Café | Open consultation | Online (Big Blue Button) | Link | |
| Data analysis in Python with Pandas | Python | Hochschulrechenzentrum, University of Marburg | Link | |
| HPC Bytes: Basics of Parallelization with OpenMP | Parallelisation | Online (Big Blue Button) | Link | |
| Proficiency Training High Performance Computing: Batch Job Scheduling | ProTHPC | Online (Zoom) | Link | |
| Digital HPC Café | Open consultation | Online (Big Blue Button) | Link | |
| Proficiency Training High Performance Computing: HPC-Workflows | ProTHPC | Online (Zoom) | Link | |
| HPC Bytes: Scaling DL Models Across Compute Nodes | Parallelisation | Online (Big Blue Button) | Link |
| Date | Event | Topics | Location | Link |
|---|---|---|---|---|
| - | Introduction to the Lichtenberg High Performance Computer | Hybrid (Alexanderstraße 2, 64283 Darmstadt, Room 202 "Darmstadt"/ Zoom) | Link | |
| - | Introduction to the Lichtenberg High Performance Computer | Hybrid (Alexanderstraße 2, 64283 Darmstadt, Room 202 "Darmstadt"/ Zoom) | Link | |
| - | Introduction to the Lichtenberg High Performance Computer | Hybrid (Alexanderstraße 2, 64283 Darmstadt, Room 202 "Darmstadt"/ Zoom) | Link |
HKHLR and Hessian Research Data Infrastructures (HeFDI) conduct the HKHLR/HeFDI Code School in collaboration.
Sustainability, maintainablilty, testability and ease of use of research software, while gaining importance, often 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.
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