Algorithm Selection in Short-Range Molecular Dynamics Simulations

TL;DR

This paper explores advanced algorithm selection strategies for short-range molecular dynamics simulations, significantly improving performance over naive methods by using predictive and data-driven approaches.

Contribution

It introduces three novel algorithm selection strategies—performance prediction, fuzzy logic, and random forest—for molecular dynamics, outperforming prior naive methods.

Findings

Achieved up to 4.05x speedup over previous approaches

Surpassed a perfect static configuration by 1.25x with dynamic selection

Demonstrated practicality and efficiency of proposed strategies

Abstract

Numerous algorithms and parallelisations have been developed for short-range particle simulations; however, none are optimally performant for all scenarios. Such a concept led to the prior development of the particle simulation library AutoPas, which implemented many of these algorithms and parallelisations and could select and tune these over the course of the simulation as the scenario changed. Prior works have, however, used only naive approaches to the algorithm selection problem, which can lead to significant overhead from trialling poorly performing algorithmic configurations. In this work, we investigate this problem in the case of Molecular Dynamics simulations. We present three algorithm selection strategies: an approach which makes performance predictions from past data, an expert-knowledge fuzzy logic-based approach, and a data-driven random forest-based approach. We demonstrate that these approaches can achieve speedups of up to 4.05 compared to prior approaches and 1.25 compared to a perfect configuration selection without dynamic algorithm selection. In addition, we discuss the practicality of the strategies in comparison to their performance, to highlight the tractability of such solutions.