- Robustness (fault tolerance)
- Verification and validation
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)
- 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 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)
- 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)
PROVA! A tool to conduct reproducible research in computational science (URL)