RESEARCH ACTIVITIES

The research of the HPC group at the Department of Mathematics and Computer Science of University of Basel , Switzerland conducts advanced and cutting-edge research in performance optimization of parallel and distributed applications and computing systems . Our work spans from small-scale multi/many-core systems to large-scale heterogeneous and multi-node architectures.

Our research addresses the complex interpla y between high performance computing (HPC) applications, computing and data center systems and their operations. We focus on optimizing performance , reliability , energy-efficiency , and usability guided by real-world user behavior, system usage patterns and daily operations. Key research areas include: 

  • Parallelization : Designing efficient algorithms and tools to maximize the performance of concurrent and distributed computing environments.
  • Heterogeneity : Addressing challenges in systems that integrate diverse processing units or resources that operate under variable capacities.
  • Mapping & Scheduling : Developing intelligent and self-adaptive strategies for task distribution, workload placement, and resource allocation.
  • Robustness ( Fault Tolerance ): Enhancing system reliability under hardware and/or software faults, including transient and permanent errors and failures. 
  • Scalability : Enabling scalable performance as applications and systems grow in size and complexity. 
  • Energy Efficiency & Sustainability : Minimizing the environmental footprint of HPC through energy-aware system design, workload optimization, and sustainable computing practices.
  • Reproducibility : Promoting transparent and repeatable computational research processes and workflows. 
  • Data Mining & Process Mining : Extracting actionable insights from comprehensive amounts of application and system data.
  • Confidential Computing : Protecting sensitive data during processing using high-performance, privacy-preserving technologies.
  • Application & System Monitoring : Real-time tracking, analysis, and optimization of application and infrastructure behavior.
  • Verification & Validation : Ensuring correctness and reliability of HPC systems and applications, especially following optimizations.
  • Modeling & Simulation : Building predictive models to understand, test, and improve parallel application behavior and system performance.

 

Our interdisciplinary approach combines system-level engineering with data-driven methodologies to generate both foundational knowledge and insights and practical solutions in HPC. We actively collaborate with academic, industrial, and international partners to ensure our research has real-world impact. Learn more on our Collaborations page .

This principle reflects our commitment to impactful, responsible, and reproducible research.

DMI-HPC group research vision 2022

DMI-HPC group research vision: Improve the theory, broaden the practice, and expand the application of High Performance Computing and High Performance Data Analysis methods to accelerate scientific discovery.

RESEARCH PROJECTS

Ongoing scientific research projects

  • BRIDGE-FGT: Family Gene Toolkit: a digital service to support genetic care in Europe, funded by SNSF-Innosuisse BRIDGE Discovery, 01/01/2026–12/31/2029 (External URL, UniBas URL )
  • SKACH: Exascale Sky Simulations for Square Kilometer Array Observatory, funded by The State Secretariat for Education, Research and Innovation (SERI), September 1, 2021-December 31, 2028 (External URL , UniBas URL ) 
  • SPH-EXA2:  Smoothed Particle Hydrodynamics at Exascale, funded by the Swiss Platform for Advanced Scientific Computing, July 1, 2021-June 30, 2024 (External URL , UniBas URL )
  • 3BEARS:  Broad Bundle of BEnchmARks for Scheduling in HPC, Big Data, and ML, funded by the Swiss Academy of Engineering Sciences (Switzerland) via the Germaine de Staël program, January 1st, 2021-December 31st, 2022 ( URL )
  • MODA : Improving HPC operations and research through Monitoring and Operational Data Analytics, funded by the University of Basel, August 1st, 2022 – present ( URL )
  • miniHPC : A modern high-performance computing cluster (15.12.2016-present), a teaching and research platform, funded by the University of Basel ( URL )
  • µ-Cluster : Everything Parallel (01.03.2017-present), a research and demonstration project, funded by the University of Basel ( URL )

Active software projects and tools

  • SPH-EXA : We develop the SPH-EXA mini-app (more details on our Scientific Output page) as a means to enable Exascale-ready hydrodynamic simulations for cosmology and astrophysics

  • LB4OMP: We develop  an OpenMP runtime library that extends the LLVM OpenMP runtime library with additional dynamic loop scheduling techniques for OpenMP loops (more details on our Scientific Output page) 

  • SimGrid: We extend SimGrid ( URL ), a scientific instrument to study the behavior of large-scale distributed systems (Grids, Clouds, HPC, or P2P systems) with dynamic load balancing methods to optimize application performance (more details on our Scientific Output page)

  • HAEC-SIM: We use HAEC-SIM ( URL ), a simulator for highly adaptive energy-efficient computing originally developed at TU Dresden, to study the impact of parallel application placement on performance.

Completed scientific research projects

  • DAPHNE:  Integrated Data Analysis Pipelines for Large-Scale Data Management, HPC, and Machine Learning, funded by the European Union’s Horizon 2020 research and innovation program, 12/01/2020-11/30/2024 ( URL )
  • The DIALOGUE Study : Using digital health to improve care for families with predisposition to hereditary cancer, funded by the Swiss National Science Foundation and the National Research Foundation (NRF) of South Korea, November 1st, 2019-October 31st, 2022 ( URL )
  • SPH-EXA: Optimizing Smooth Particle Hydrodynamics for Exascale Computing (July 1, 2017-June 30, 2020), funded by the Swiss Platform for Advanced Scientific Computing (External URL , UniBas URL )

  • MLS: Multilevel Scheduling in Large Scale High Performance Computers (09/01/2017-04/30/2021), funded by the Swiss National Science Foundation ( URL )

  • DA-HPC-OR: Data Analysis for Improving High Performance Computing Operations and Research (01.09.2018-29.02.2020), Seed Money grant funded by the Eucor – The European Campus (External URL , UniBas URL )

Former projects (no longer supported)

  • PROVA! A tool to conduct reproducible research in computational science ( URL )