RBMD 2.0：面向多GPU架构大规模模拟的随机批分子动力学软件包
RBMD 2.0: Random batch molecular dynamics package for large-scale simulations on multi-GPU architectures
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中文总结 AI 辅助
RBMD 2.0是面向多GPU架构的随机批分子动力学软件包，结合改进的随机批Ewald方法等技术，实现了非键作用力计算的高效加速，在大规模粒子模拟中展现出优异的准确性与扩展性，可作为未来百亿亿次级模拟的计算引擎。
中文摘要 AI 辅助
在多GPU架构上开展粒子系统的大规模分子动力学模拟，常受限于非键作用力计算的计算成本与通信成本。我们推出RBMD 2.0，这是随机批分子动力学软件包的重大新版本，专为大规模系统的跨节点多GPU模拟设计。它将改进的随机批Ewald方法与三维域分解、幽灵粒子通信相结合，以加速多GPU非键作用力计算；DTK CUDA框架则提升了其在异构加速器架构间的可移植性。对多个基准系统的数值实验验证了RBMD 2.0模拟的准确性与效率：在涉及数亿粒子、跨多个加速器设备的模拟中，非键作用力计算可获得数倍至约两个数量级的加速，同时展现出超过97.5%的弱扩展效率。这些结果表明，RBMD 2.0有望成为未来百亿亿次级分子动力学模拟的计算引擎。
英文摘要
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.
发表机构
- School of Mathematical Sciences, Shanghai Jiao Tong University（上海交通大学数学科学学院）
- SOG AI-Technology Co. Ltd.（索格人工智能科技有限公司）
- Future Battery Research Center, Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来技术学院未来电池研究中心）
机构由 AI 辅助整理，请以论文原文为准。
