Ravedutti Lucio Machado R, Eitzinger J, Laukemann J, Hager G, Köstler H, Wellein G (2023)
Publication Type: Journal article
Publication year: 2023
DOI: 10.1016/j.future.2023.06.023
Molecular dynamics (MD) simulations provide considerable benefits for the investigation and experimentation of systems at atomic level. Their usage is widespread into several research fields, but their system size and timescale are crucially limited by the available computing power. Performance engineering of MD kernels is therefore critical to understand their bottlenecks and investigate possible improvements. For that reason, we developed MD-Bench, a performance-focused prototyping harness for short-range MD kernels that implements state-of-the-art algorithms from multiple production applications such as LAMMPS and GROMACS. The MD-Bench source code is simple, understandable, and extensible, and therefore well suited for benchmarking, teaching, and researching MD algorithms. In this paper we introduce MD-Bench, describe its design, structure, and implemented algorithms. Finally, we show five use-cases of MD-Bench and describe how these are useful to gain a deeper understanding of the performance of MD kernels.
APA:
Ravedutti Lucio Machado, R., Eitzinger, J., Laukemann, J., Hager, G., Köstler, H., & Wellein, G. (2023). MD-Bench: A performance-focused prototyping harness for state-of-the-art short-range molecular dynamics algorithms. Future Generation Computer Systems-The International Journal of Grid Computing Theory Methods and Applications. https://dx.doi.org/10.1016/j.future.2023.06.023
MLA:
Ravedutti Lucio Machado, Rafael, et al. "MD-Bench: A performance-focused prototyping harness for state-of-the-art short-range molecular dynamics algorithms." Future Generation Computer Systems-The International Journal of Grid Computing Theory Methods and Applications (2023).
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