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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.

  • Protein Ai “A Platform for Predicting Protein Structures based on the Molecule's Sequence with ChatBot Interface for Analysis” (Hasan Marwan Mahmood Aldhahi), Industry Internship (M.Inf.2802) at GWDG on Applied Data Science, M.Sc., GWDG, 2025-03-31 Presentation
  • Data at Scale in ESiWACE: Progress of WP4 (Bryan Lawrence, Dr. Julian Kunkel), ESiWACE Annual General Assembly, Virtual, 2021-09-27 Presentation
  • Input/Output and Middleware (Dr. Julian Kunkel), Summer School on Effective HPC for Climate and Weathe, Virtual, 2021-08-23 Presentation Video
  • Lifting the user I/O abstraction to workflow level a possibility or in vain? (Dr. Julian Kunkel), Dagstuhl Seminar, Schloß Dagstuhl, 2021-08-16 Presentation
  • Data Systems at Scale in Climate and Weather (Dr. Julian Kunkel), Hidalgo Workshop, Virtual, 2021-07-09 Presentation

  • Exploring transfer learning for predicting I/O time across systems, Voß, Adrian (Master Thesis), Advisors: Müller, Matthias S., Kunkel, Julian, Liem, Radita Tapaning Hesti, 2024, BibTeX URL
  • 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
  • Investigation of the influence of cuttings transport on drill string dynamics, Patrick Höhn (PhD Thesis), Advisors: Joachim Oppelt, 2024-08-09, Thesis BibTeX URL
  • 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

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  • Last modified: 2025-08-16 11:15
  • by Julian Kunkel