The research of the HPC group at the Department of Mathematics and Computer Science of University of Basel, Switzerland concentrates on the following topics:

  • Parallelization
  • Heterogeneity
  • Mapping
  • Scheduling
  • Robustness (fault tolerance)
  • Scalability
  • Verification and validation
  • Reproducibility

of parallel applications executing on small (multi-many cores) to large scale (multi-node) high performance computers.

Other topics of interest include:

  • Data mining
  • Process mining
  • Information security
    (protection and privacy)
  • System and application monitoring
  • Modeling and simulation of parallel applications and systems.


Ongoing 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 programme (URL)

  • The DIALOGUE Study: Using digital health to improve care for families with predisposition to hereditary cancer (01.11.2019-31.10.2022), funded by the Swiss National Science Foundation and the National Research Foundation (NRF) of South Korea (URL)
  • MLS: Multilevel Scheduling in Large Scale High Performance Computers (01.09.2017-31.08.2020), funded by the Swiss National Science Foundation (URL)

  • SPH-EXA: Optimizing Smooth Particle Hydrodynamics for Exascale Computing (01.07.2017-30.06.2020), funded by the Swiss Platform for Advanced Scientific Computing (External URL, UniBas 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 simulations

  • 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

  • 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)

Other projects

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