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.

• 2021-06-01: Julian is now Professor in High-Performance Computing at the Georg-August-Universität Göttingen and the deputy head High-Performance Computing at the GWDG.
• 2020-09-30: Julian is guest editor of the special issue “High-Performance Computing Algorithms and their Applications 2021” in MDPI's journal Algorithms
• 2019-09-08: Listen to the Radio Free HPC podcast with the IO-500 team
• 2019-08-25: BCS interview: Inside the Open Source Community
• 2019-07-23: Today our new wordcloud with search functionality is live, check it out below!
• 2019-07-19: Apply today for a 3-year PostDoc position for developing next generation of storage solutions for earth-science
• 2019-06-20: NVIDIA announces our collaboration as part of the NVIDIA AI Technology Center
• 2019-05-27: The HPC Vision document to which we contributed is online
• 2019-01-10: Apply today for a short term PostDoc position for developing next generation of storage solutions for earth-science

• HPC-IODC: HPC I/O in the Data Center Workshop
• BoF: Large-Scale Data Management with Data Lakes
• BoF: International HPC Certification Program
• BoF: The IO500 and the Virtual Institute of I/O
• LLVM-CTH: The Third Workshop on LLVM Compiler and Tools for HPC
• Minisymposium: Leveraging Data Lakes to Manage and Process Scientific Data
• BoF: Analyzing Parallel I/O

• Email security with Postfix/DKIM/DMARC on Ubuntu 20.04 (2020-07-20 11:56)
• Green screen remover (2018-05-01 20:57)
• Impact of KPTI on the IO-500 for DKRZ (2018-01-23 10:58)
• Fine grained Impact of KPTI on HPC nodes I/O performance (2018-01-23 10:40)
• Impact of KPTI on HPC storage performance (2018-01-19 18:06)

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

• ESiWACE2
• Bull I/O cooperation

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• 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
• A Workflow for Identifying Jobs with Similar I/O Behavior Utilizing Time Series Analysis (Dr. Julian Kunkel), HPC IODC Workshop, Virtual, 2021-07-02 Presentation

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• Poster: Optimizing Workload in Heterogeneous HPC Workflows with Constraints (Aasish Kumar Sharma, Christian Boehme, Patrick Gelß, Julian Kunkel), 2025-06-11 BibTeX PDF
• Poster: Automatic Instrumentation and PGO Optimiziation of HPC Compute Dwarfs (Anja Gerbes, Panagiotis Adamidis, Julian Kunkel), 2025-06-11 BibTeX PDF
• Workflow-Driven Modeling for the Compute Continuum: An Optimization Approach to Automated System and Workload Scheduling (Aasish Kumar Sharma, Christian Boehme, Patrick Gelß, Ramin Yahyapour, Julian Kunkel), 2025-05-18 BibTeX URL DOI
• A Review of Tools and Techniques for Optimization of Workload Mapping and Scheduling in Heterogeneous HPC System (Aasish Kumar Sharma, Julian Kunkel), 2025-05-16 BibTeX URL DOI
• Performance Analysis of Convolutional Neural Network By Applying Unconstrained Binary Quadratic Programming (Aasish Kumar Sharma and Sanjeeb Prashad Pandey and Julian M. Kunkel), 2025 BibTeX URL

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• 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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