Ravedutti Lucio Machado R, Eitzinger J, Köstler H, Wellein G (2023)
Publication Language: English
Publication Type: Book chapter / Article in edited volumes
Publication year: 2023
Publisher: Springer, Cham
Edited Volumes: Parallel Processing and Applied Mathematics. PPAM 2022.
Series: Lecture Notes in Computer Science (LNCS)
Book Volume: 13826
Pages Range: 321-332
ISBN: 978-3-031-30441-5
DOI: 10.1007/978-3-031-30442-2_24
Proxy-apps, or mini-apps, are simple self-contained benchmark codes with performance-relevant kernels extracted from real applications. Initially used to facilitate software-hardware co-design, they are a crucial ingredient for serious performance engineering, especially when dealing with large-scale production codes. MD-Bench is a new proxy-app in the area of classical short-range molecular dynamics. In contrast to existing proxy-apps in MD (e.g. miniMD and coMD) it does not resemble a single application code, but implements state-of-the art algorithms from multiple applications (currently LAMMPS and GROMACS). The MD-Bench source code is understandable, extensible and suited for teaching, benchmarking and researching MD algorithms. Primary design goals are transparency and simplicity, a developer is able to tinker with the source code down to the assembly level. This paper introduces MD-Bench, explains its design and structure, covers implemented optimization variants, and illustrates its usage on three examples.
APA:
Ravedutti Lucio Machado, R., Eitzinger, J., Köstler, H., & Wellein, G. (2023). MD-Bench: A Generic Proxy-App Toolbox for State-of-the-Art Molecular Dynamics Algorithms. In Parallel Processing and Applied Mathematics. PPAM 2022. (pp. 321-332). Springer, Cham.
MLA:
Ravedutti Lucio Machado, Rafael, et al. "MD-Bench: A Generic Proxy-App Toolbox for State-of-the-Art Molecular Dynamics Algorithms." Parallel Processing and Applied Mathematics. PPAM 2022. Springer, Cham, 2023. 321-332.
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