MD-Bench: Engineering the in-core performance of short-range molecular dynamics kernels from state-of-the-art simulation packages

TL;DR

MD-Bench is a versatile, transparent benchmarking tool that models state-of-the-art short-range molecular dynamics kernels, aiding performance analysis and optimization in MD simulations.

Contribution

We developed MD-Bench, an extensible and understandable proxy-app for MD kernels, facilitating benchmarking, teaching, and research on performance bottlenecks.

Findings

MD-Bench accurately models algorithms from LAMMPS and GROMACS.

It reveals key performance bottlenecks in MD kernels.

Provides insights for optimizing MD simulation performance.

Abstract

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 also crucially limited by the computing power they can make use of. Performance engineering of MD kernels is therefore important to understand their bottlenecks and point out possible improvements. For that reason, we developed MD-Bench, a proxy-app for short-range MD kernels that implements state-of-the-art algorithms from multiple production applications such as LAMMPS and GROMACS. MD-Bench is intended to have simpler, understandable and extensible source code, as well as to be transparent and suitable for teaching, benchmarking and researching MD algorithms. In this paper we introduce MD-Bench, describe its design and structure and implemented algorithms. Finally, we show five usage examples of MD-Bench and describe how these are useful to have a deeper understanding of MD kernels from a performance point of view, also exposing some interesting performance insights.