Infinite Storage
Infinite Possibilities
High-Performance Storage
The research group High-Performance Storage improves the capabilities of storage landscapes applying smart concepts. We speak big data analytics and high-performance computing and apply our knowledge to meet the needs of environmental modeling.
Further information about our mission.
The HPS group is tightly integrated into the GWDG AG Computing group with more than 50 people.
Recent talks
- Knowledge-Based HPC Decision Intelligence: Executable Governance for Trustworthy AI-Driven Decision Workflows (Aasish Kumar Sharma), Research Work Progress on Trustworthy AI Framework, Online: HammerHAI AIF Working Group on Ethics and Complaince, 2026-11-10 Presentation
- GöTRE: HPC-basiertes SPE für Big Data (Sascha Safenreider), 3. KIMed Netzwerk Partner Forum - KI trifft Medizin, Paulinum, Neues Augusteum, Leipzig, Germany, 2026-10-27
- Knowledge-Guided HPC Decision Intelligence: Executable Governance for Trustworthy AI-Driven Decision Workflows (Aasish Kumar Sharma), Contributed Talk, NHR Conference 2026, Göttingen, 2026-09-15 Presentation
- DAYA: Data-Aware Agentic AI Architecture for Decision-Oriented Analytics on HPC Systems (Michael Bidollahkhani, Orr Shomroni), NHR Conference 2026 - HPC-Driven Decision Science: Optimization, Simulation, and AI for Economics and Management, Heinz Nixdorf MuseumsForum, Paderborn, Germany, 2026-09-15
- GWDG AI Services and Requirements for a Scientific AI Service (Michael Bidollahkhani), Bremen AI School, Bremen, Germany, 2026-08-19
Recent publications
- When GPUs Fail Quietly: Separating GPU Hardware Degradation from Monitoring Failure under Degraded Observability (Michael Bidollahkhani, Mohsen Seyedkazemi Ardebili, Andrea Bartolini, Rute C. Sofia, Julian Kunkel), 2026-10-31 BibTeX URL DOI
- Poster: Resilient HPC Operations through Observability-Aware Predictive Maintenance (Michael Bidollahkhani), 2026-09-14 BibTeX URL
- Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems (Aasish Kumar Sharma, Julian Kunkel), 2026-07 BibTeX DOI
- Global AI Regulations for FAIR and Ethics in High-Risk Use Cases: A Comparative Review (Aasish Kumar Sharma, Dimitar Koysev, Christopher Anich, Roshni Kumari Ojha, Julian M. Kunkel), 2026-07 BibTeX DOI
Recent theses
- Dynamically Negotiating Resources Between Kubernetes and Slurm Environments, Shrinath Madde (Master's Thesis), Advisors: Prof. Dr. Julian Kunkel, Dr. Jonathan Decker, 2026-09-26, Thesis BibTeX
- Scalable GPU-Accelerated Segmentation and Tracking in High-Speed Flow Loop Videos, Serge Muriel Kouom Nankam (Master's Thesis), Advisors: Dr. Patrick Höhn, 2026-08-31, Thesis BibTeX
- Comparative Analysis of On-Demand Distributed Computing Frameworks on Slurm-Based HPC Systems, Yasir Yasir (Master's Thesis), Advisors: Prof. Dr. Julian Kunkel, Dr. Jonathan Decker, 2026-08-30, Thesis BibTeX
- The Impact of Data Preparation Strategies on Retrieval Quality in Educational RAG Systems, Corinna Margarete Liley (Bachelor's Thesis), Advisors: Prof. Dr. Julian Kunkel, Dr. Jonathan Decker, 2026-08-22, Thesis BibTeX
- Performance Evaluation of LLM Inference Engines, Yining Liu (Bachelor's Thesis), Advisors: Prof. Dr. Julian Kunkel, Dr. Jonathan Decker, 2026-08-21, Thesis BibTeX
Research
Explore our research using the power of machine learning, click on a keyword to see relevant documents.