Efficient Parallelization of Short-Range Molecular Dynamics Simulations on Many-Core Systems

TL;DR

This paper presents a highly parallel algorithm for short-range molecular dynamics simulations on multi-core systems, achieving high efficiency and scalability, especially for inhomogeneous systems like nanostructures.

Contribution

The paper introduces a novel task-based parallel algorithm that improves efficiency and scalability for molecular dynamics simulations on many-core architectures.

Findings

Achieves over 80% parallel efficiency on 12-core systems.

Outperforms spatial decomposition methods for inhomogeneous systems.

Scales well on Intel Xeon Phi coprocessors.

Abstract

This article introduces a highly parallel algorithm for molecular dynamics simulations with short-range forces on single node multi- and many-core systems. The algorithm is designed to achieve high parallel speedups for strongly inhomogeneous systems like nanodevices or nanostructured materials. In the proposed scheme the calculation of the forces and the generation of neighbor lists is divided into small tasks. The tasks are then executed by a thread pool according to a dependent task schedule. This schedule is constructed in such a way that a particle is never accessed by two threads at the same time.Benchmark simulations on a typical 12 core machine show that the described algorithm achieves excellent parallel efficiencies above 80 % for different kinds of systems and all numbers of cores. For inhomogeneous systems the speedups are strongly superior to those obtained with spatial decomposition. Further benchmarks were performed on an Intel Xeon Phi coprocessor. These simulations demonstrate that the algorithm scales well to large numbers of cores.