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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.

  • Poster: Automatic Instrumentation and PGO Optimiziation of HPC Compute Dwarfs (Anja Gerbes, Panagiotis Adamidis, Julian Kunkel), 2025-06-11 BibTeX PDF
  • State-of-the-art artificial intelligence techniques in healthcare publications, and their correlation with disease and data: A data driven analysis (Sadegh Keshtkar, Dagmar Krefting, Anne-Christin Hauschild, Zully Maritza Ritter, Narges Lux, Aasish Kumar Sharma, Pavan Kumar Siligam, Julian Kunkel), 2024-11-26 BibTeX URL DOI
  • Scalable Software Distribution for HPC-Systems Using MPI-Based File Systems in User Space (Jakob Dieterle, Hendrik Nolte, Julian Kunkel), 2024-11-17 BibTeX URL
  • A Quantitative and Qualitative Comparison of Machine Learning Inference Frameworks (Egi Brako, Jonathan Decker, Julian Kunkel), 2024-11-17 BibTeX URL
  • KI in der Projektwirtschaft 2: Automatisierte Analysen von MRT-Bildern (Hendrik Nolte, Philip Langer, Julian Kunkel, Christian Bernert, Steffen Scheurer, Harald Wehnes), 2024-09-23 BibTeX URL

  • 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