Tuning of Vectorization Parameters for Molecular Dynamics Simulations in AutoPas

TL;DR

This paper investigates SIMD vectorization techniques and dynamic tuning in AutoPas to optimize molecular dynamics simulations, focusing on particle interaction order and runtime parameter adaptation for improved performance.

Contribution

It introduces a runtime tuning mechanism for vectorization order in AutoPas, considering simulation-specific parameters like particle density and neighbor algorithms.

Findings

Performance improvements in force calculation through dynamic vectorization order selection

Significant reduction in execution time and energy consumption

Enhanced adaptability of AutoPas to changing simulation conditions

Abstract

Molecular Dynamics simulations can help scientists to gather valuable insights for physical processes on an atomic scale. This work explores various techniques for SIMD vectorization to improve the pairwise force calculation between molecules in the scope of the particle simulation library AutoPas. The focus lies on the order in which particle values are loaded into vector registers to achieve the most optimal performance regarding execution time or energy consumption. As previous work indicates that the optimal MD algorithm can change during runtime, this paper investigates simulation-specific parameters like particle density and the impact of the neighbor identification algorithms, which distinguishes this work from related projects. Furthermore, AutoPas' dynamic tuning mechanism is extended to choose the optimal vectorization order during runtime. The benchmarks show that considering different particle interaction orders during runtime can lead to a considerable performance improvement for the force calculation compared to AutoPas' previous approach.