hps-header.jpg

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 25 people.

Explore our research using the power of machine learning, click on a keyword to see relevant documents.

  • Emulation of Heterogeneous Kubernetes Clusters using QEMU, Vincent Florens Hasse (Master's Thesis), Advisors: Prof. Dr. Julian Kunkel, Sven Bingert, 2024-09-30, BibTeX URL
  • Analyse und Optimierung von Ein-/Ausgabe von DeepLearning Piplines für Hochleistungsrechnersysteme, Katrena Shihada (Bachelor's Thesis), Advisors: Prof. Dr. Julian Kunkel, Sven Bingert, 2024-09, BibTeX
  • A qualitative and quantitative comparison of Machine Learning Inference Runtimes, Egi Brako (Bachelor's Thesis), Advisors: Prof. Dr. Julian Kunkel, Sven Bingert, 2024-07-05, Thesis BibTeX
  • Running Kubernetes Workloads on Rootless HPC Systems using Slurm, Sören Metje (Master's Thesis), Advisors: Prof. Dr. Julian Kunkel, Stefanie Mühlhausen, 2023-12, BibTeX URL
  • An HPC FaaS Runtime based on HPX and Modern Lightweight Isolation, Jakob Hördt (Master's Thesis), Advisors: Prof. Dr. Julian Kunkel, Sven Bingert, 2023-09, BibTeX URL