RBMD: A Molecular Dynamics Package Enabling to Simulate 10 Million All-Atom Particles in a Single Graphics Processing Unit

Authors

  • Weihang Gao Shanghai Jiao Tong University image/svg+xml
  • Teng Zhao Shanghai Jiao Tong University image/svg+xml
  • Yongfa Guo Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence
  • Jiuyang Liang Shanghai Jiao Tong University image/svg+xml
  • Huan Liu Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence
  • Maoying Luo Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence
  • Zedong Luo Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence
  • Wei Qin Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence
  • Yichao Wang Shanghai Jiao Tong University image/svg+xml
  • Qi Zhou Shanghai Jiao Tong University image/svg+xml
  • Shi Jin Shanghai Jiao Tong University image/svg+xml
  • Zhenli Xu Shanghai Jiao Tong University image/svg+xml

DOI:

https://doi.org/10.4208/cicp.OA-2024-0156

Keywords:

Molecular dynamics, random batch methods, heterogeneous architectures, Coulomb interactions, large-scale simulations

Abstract

This paper introduces a random-batch molecular dynamics (RBMD) package for 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 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 the accuracy and performance of the package. Comparison with the particle-particle particle-mesh and the Verlet list methods 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.

Author Biographies

  • Weihang Gao

    School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China

    Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence, Chongqing 401329, China

  • Teng Zhao

    Institute of Natural Sciences, MOE-LSC, and Shanghai National Center for Applied Mathematics, Shanghai Jiao Tong University, Shanghai 200240, China

    Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence, Chongqing 401329, China

  • Yongfa Guo

    Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence, Chongqing 401329, China

  • Jiuyang Liang

    School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China

  • Huan Liu

    Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence, Chongqing 401329, China

  • Maoying Luo

    Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence, Chongqing 401329, China

  • Zedong Luo

    Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence, Chongqing 401329, China

  • Wei Qin

    Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence, Chongqing 401329, China

  • Yichao Wang

    Network & Information Center, Shanghai Jiao Tong University, Shanghai 200240, China

  • Qi Zhou

    School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China

  • Shi Jin

    School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China

    Institute of Natural Sciences, MOE-LSC, and Shanghai National Center for Applied Mathematics, Shanghai Jiao Tong University, Shanghai 200240, China

    Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence, Chongqing 401329, China

  • Zhenli Xu

    School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China

    Institute of Natural Sciences, MOE-LSC, and Shanghai National Center for Applied Mathematics, Shanghai Jiao Tong University, Shanghai 200240, China

    CMA-Shanghai, Shanghai Jiao Tong University, Shanghai 200240, China

Published

2025-11-07

Issue

Section

Articles