General-purpose molecular dynamics simulations on GPU-based clusters

TL;DR

This paper introduces a GPU-accelerated version of LAMMPS, significantly boosting molecular dynamics simulation speeds on single nodes and clusters, and demonstrating scalability across diverse materials and hardware configurations.

Contribution

The paper presents a GPU implementation of LAMMPS that achieves 5x to 13x speedups, enabling efficient multi-GPU simulations on heterogeneous clusters with detailed performance analysis.

Findings

GPU implementation achieves 5x-13x speedup over CPU version

Effective multi-GPU scaling on up to 128 dual GPU nodes

Performance varies with neighbor list strategies and material types

Abstract

We present a GPU implementation of LAMMPS, a widely-used parallel molecular dynamics (MD) software package, and show 5x to 13x single node speedups versus the CPU-only version of LAMMPS. This new CUDA package for LAMMPS also enables multi-GPU simulation on hybrid heterogeneous clusters, using MPI for inter-node communication, CUDA kernels on the GPU for all methods working with particle data, and standard LAMMPS C++ code for CPU execution. Cell and neighbor list approaches are compared for best performance on GPUs, with thread-per-atom and block-per-atom neighbor list variants showing best performance at low and high neighbor counts, respectively. Computational performance results of GPU-enabled LAMMPS are presented for a variety of materials classes (e.g. biomolecules, polymers, metals, semiconductors), along with a speed comparison versus other available GPU-enabled MD software. Finally, we show strong and weak scaling performance on a CPU/GPU cluster using up to 128 dual GPU nodes.