RBMD: A molecular dynamics package enabling to simulate 10 million all-atom particles in a single graphics processing unit
School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
arXiv:2407.09315 [physics.comp-ph], (12 Jul 2024)
@misc{gao2024rbmdmoleculardynamicspackage,
title={RBMD: A molecular dynamics package enabling to simulate 10 million all-atom particles in a single graphics processing unit},
author={Weihang Gao and Teng Zhao and Yongfa Guo and Jiuyang Liang and Huan Liu and Maoying Luo and Zedong Luo and Wei Qin and Yichao Wang and Qi Zhou and Shi Jin and Zhenli Xu},
year={2024},
eprint={2407.09315},
archivePrefix={arXiv},
primaryClass={physics.comp-ph},
url={https://arxiv.org/abs/2407.09315}
}
This paper introduces a random-batch molecular dynamics (RBMD) package for fast simulations of particle systems at the nano/micro scale. Different from existing packages, the RBMD uses random batch methods for nonbonded interactions of particle systems. The long-range part of Coulomb interactions is calculated in Fourier space by the random batch Ewald algorithm, which achieves linear complexity and superscalability, surpassing classical lattice-based Ewald methods. For the short-range part, the random batch list algorithm is used to construct neighbor lists, significantly reducing both computational and memory costs. The RBMD is implemented on GPU-CPU heterogeneous architectures, with classical force fields for all-atom systems. Benchmark systems are used to validate accuracy and performance of the package. Comparison with the particle-particle particle-mesh method and the Verlet list method in the LAMMPS package is performed on three different NVIDIA GPUs, demonstrating high efficiency of the RBMD on heterogeneous architectures. Our results also show that the RBMD enables simulations on a single GPU with a CPU core up to 10 million particles. Typically, for systems of one million particles, the RBMD allows simulating all-atom systems with a high efficiency of 8.20 ms per step, demonstrating the attractive feature for running large-scale simulations of practical applications on a desktop machine.
July 28, 2024 by hgpu