A continuous benchmarking infrastructure for high-performance computing applications

Abstract

For scientific software, especially those used for large-scale simulations, achieving good performance and efficiently using the available hardware resources is essential. It is important to regularly perform benchmarks to ensure the efficient use of hardware and software when systems are changing and the software evolves. However, this can become quickly very tedious when many options for parameters, solvers, and hardware architectures are available. We present a continuous benchmarking strategy that automates benchmarking new code changes on high-performance computing clusters. This makes it possible to track how each code change affects the performance and how it evolves.

GRAPHICAL ABSTRACT

Keywords:

• Continuous integration
• continuous Benchmarking
• finite elements method
• computational homogenization
• lattice Boltzmann method

Acknowledgments

The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this project by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC). The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project ‘HEISSRISSE – Massively Parallel Simulation of the Melt Pool Area during Laser Beam Welding using the Lattice Boltzmann Method’. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the German Research Foundation (DFG) – 440719683.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://i10git.cs.fau.de/ob28imeq/cb-util.

2 https://www10.cs.fau.de/grafana/d/a3583809-6009-457c-b4d1-71dad2c5b230/fe2ti-benchmarks?orgId=1&refresh=10s.

3 https://www10.cs.fau.de/grafana/d/7HOrefnVk/overview?orgId=1&refresh=10s.

4 https://i10git.cs.fau.de/walberla/walberla.

Additional information

Funding

This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the project Numbers 433735254 and 434946896. This work has also received funding from the European High Performance Computing Joint Undertaking (JU) and Sweden, Germany, Spain, Greece, and Denmark under grant agreement No. 101093393.