Understanding the Scalability of Molecular Simulation Using Empirical Performance Modeling

The final authenticated publication is available online at https://doi.org/10.1007/978-3-030-17872-7_8.Molecular dynamics (MD) simulation allows for the study of static and dynamic properties of molecular ensembles at various molecular scales, from monatomics to macromolecules such as proteins and nucleic acids. It has applications in biology, materials science, biochemistry, and biophysics. Recent developments in simulation techniques spurred the emergence of the computational molecular engineering (CME) field, which focuses specifically on the needs of industrial users in engineering. Within CME, the simulation code ms2 allows users to calculate thermodynamic properties of bulk fluids. It is a parallel code that aims to scale the temporal range of the simulation while keeping the execution time minimal. In this paper, we use empirical performance modeling to study the impact of simulation parameters on the execution time. Our approach is a systematic workflow that can be used as a blue-print in other fields that aim to scale their simulation codes. We show that the generated models can help users better understand how to scale the simulation with minimal increase in execution time.BMBF, 01IH16008D, Verbundprojekt: TaLPas - Task-basierte Lastverteilung und Auto-Tuning in der PartikelsimulationDFG, 323299120, ExtraPeak - Automatische Leistungsmodellierung von HPC-Anwendungen mit multiplen Modellparameter