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.

  • \myPub{2021}{Input/Output and Middleware}{Summer School on Effective HPC for Climate and Weathe}{Virtual}
  • \myPub{}{Lifting the user I/O abstraction to workflow level a possibility or in vain?}{Dagstuhl Seminar}{Schloß Dagstuhl}
  • \myPub{}{Data Systems at Scale in Climate and Weather}{Hidalgo Workshop}{Virtual}
  • \myPub{}{A Workflow for Identifying Jobs with Similar I/O Behavior Utilizing Time Series Analysis}{HPC IODC Workshop}{Virtual}

  • \myPub{2024}{HOSHMAND: Accelerated AI-Driven Scheduler Emulating Conventional Task Distribution Techniques for Cloud Workloads}{Michael Bidollahkhani, Aasish Kumar Sharma, Julian Kunkel}{IEEE Computers, Software, and Applications Conference, pp. 1-8, IEEE, COMPSAC 2024}
  • \myPub{}{Deployment of an HPC-Accelerated Research Data Management System: Exemplary Workflow in HeartAndBrain Study}{V. Telzki, H. tom Wörden, F. Spreckelsen, Hendrik Nolte, Julian Kunkel, U. Parlitz, S. Luther, M. Uecker, M. Bähr}{Poster, Biosignals Workshop, Göttingen}
  • \myPub{}{Distracted AI: Integrating Neuroscience-Inspired Attention and Distraction Learning in ANN}{Michael Bidollahkhani, M. Raahemi, P. Haskul}{2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing, pp. 1-8, IEEE, AISP}
  • \myPub{}{Revolutionizing system reliability: The role of AI in predictive maintenance strategies}{Michael Bidollahkhani, Julian Kunkel}{IARIA CloudComputing 2024 Conference, pp. 1-9, Venice, Italy, ISSN: 2308-4294. ISBN: 978-1-68558-156-5}
  • \myPub{2023}{Transfer Learning Workflow for High-Quality I/O Bandwidth Prediction with Limited Data}{Dmytro Povaliaiev, Radita Liem, Julian Kunkel, Jay Lofstead, Philip Carns}{Poster, SC 2023, Denver, Colorado, USA}

  • 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
  • Enhancing Tree Segmentation in Large Forest Point Clouds with Synthetic Data, Ali Doosthosseini (Master's Thesis), Advisors: Prof. Dr. Julian Kunkel, Prof. Dr. Alexander Ecker, 2023-09, BibTeX
  • Implementation of a Liquid Neural Network Control System for Multi-Join Cyber Physical ARM, Michael Bidollahkhani (Master's Thesis), Advisors: Ferhat Atasoy, Abdellatef Hamdan, 2023-06, BibTeX
  • Interactive Data Center Digital Twin using Virtual Reality, Lars Quentin (Bachelor's Thesis), Advisors: Dr. Julian Kunkel, 2022-12-15, Thesis BibTeX