RBMD 2.0: Random batch molecular dynamics package for large-scale simulations on multi-GPU architectures
Abstract
Large-scale molecular dynamics simulations of particle systems on multi-GPU architectures are often constrained by the computational and communication costs of nonbonded force evaluation. We present RBMD 2.0, a major new release of the random batch molecular dynamics package designed for cross-node multi-GPU simulations of large-scale systems. It combines the improved random batch Ewald method with three-dimensional domain decomposition and ghost-particle communication to accelerate multi-GPU nonbonded force evaluation, while the DTK CUDA framework facilitates portability across heterogeneous accelerator architectures. Numerical experiments on multiple benchmark systems demonstrate both the accuracy and efficiency of simulations with RBMD 2.0. For simulations involving up to hundreds of millions of particles across multiple accelerator devices, one achieves speedups ranging from severalfold to approximately two orders of magnitude in nonbonded force evaluation while exhibiting over 97.5% weak-scaling behavior. These results demonstrate the promising nature of RBMD 2.0 as a computational engine for future exascale molecular dynamics simulations.
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BibTeX
@misc{zhou2026rbmd,
title = {{RBMD 2.0: Random batch molecular dynamics package for large-scale simulations on multi-GPU architectures}},
author = {Qi Zhou and Yongfa Guo and Jincheng Zhong and Shou-Hang Bo and Teng Zhao and Zhenli Xu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.02694},
eprint = {2609.02694},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.02694},
}
RIS
TY - UNPB
TI - RBMD 2.0: Random batch molecular dynamics package for large-scale simulations on multi-GPU architectures
AU - Qi Zhou
AU - Yongfa Guo
AU - Jincheng Zhong
AU - Shou-Hang Bo
AU - Teng Zhao
AU - Zhenli Xu
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2609.02694
UR - https://arxiv.org/abs/2609.02694
AB - Large-scale molecular dynamics simulations of particle systems on multi-GPU architectures are often constrained by the computational and communication costs of nonbonded force evaluation. We present RBMD 2.0, a major new release of the random batch molecular dynamics package designed for cross-node multi-GPU simulations of large-scale systems. It combines the improved random batch Ewald method with three-dimensional domain decomposition and ghost-particle communication to accelerate multi-GPU nonbonded force evaluation, while the DTK CUDA framework facilitates portability across heterogeneous accelerator architectures. Numerical experiments on multiple benchmark systems demonstrate both the accuracy and efficiency of simulations with RBMD 2.0. For simulations involving up to hundreds of millions of particles across multiple accelerator devices, one achieves speedups ranging from severalfold to approximately two orders of magnitude in nonbonded force evaluation while exhibiting over 97.5% weak-scaling behavior. These results demonstrate the promising nature of RBMD 2.0 as a computational engine for future exascale molecular dynamics simulations.
ER -