LMFF: efficient and scalable layered materials force field on heterogeneous many-core processors
Ping Gao, Xiaohui Duan, Jiaxu Guo, Jin Wang, Zhenya Song, Lizhen Cui, Xiangxu Meng, Xin Liu, Wusheng Zhang, Ming Ma, Guohui Li, Dexun Chen
摘要
LAMMPS is one of the most popular Molecular Dynamic (MD) packages and is widely used in the field of physics, chemistry and materials simulation. Layered Materials Force Field (LMFF) is our expansion of the LAMMPS potential function based on the Tersoff potential and inter-layer potential (ILP) in LAMMPS. LMFF is designed to study layered materials such as graphene and boron hexanitride. It is universal and does not depend on any platform. We have also carried out a series of optimizations on LMFF and the optimization work is carried out on the new generation of Sunway supercomputer, called SWLMFF. Experiments show that our implementation is efficient, scalable and portable. When generic LMFF is ported to Intel Xeon Gold 6278C, 2X performance improvement is achieved. For the optimized SWLMFF, the overall performance improvement is nearly 200--330X compared to the original ILP and Tersoff potentials. And SWLMFF has good parallel efficiency of 95%-100% under weak scaling with 2.7 million atoms on a single process. The maximum atomic system simulated by SWLMFF is close to 231 atoms. And nanosecond simulations in one day can be realized.
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
相关 Paper
- Enhance the Strong Scaling of LAMMPS on FugakuJianxiong Li, Tong Zhao, Zhuoqiang Guo, Shunchen Shi 等SC 2023 · 被引用 3 次
- Enabling Real World Scale Structural Superlubricity All-Atom Simulation on the Next-Generation Sunway SupercomputerXiaohui Duan, Jin Wang, Ping Gao, Ming Ma 等SC 2023 · 被引用 3 次
- TENSORMD: Accelerating Molecular Dynamics with a High-Performance Machine Learning Interatomic PotentialYucheng Ouyang, Xin Chen, Ying Liu, Xin Chen 等SC 2025 · 被引用 1 次
- DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic PotentialsKevin Han, Bowen Deng, Amir Barati Farimani, Gerbrand CederICLR 2026 · 被引用 10 次
- MD-pipe: A Strong Scaling Enhanced Pipeline Architecture for Ab Initio Accuracy Molecular DynamicsNing Kang, Guojun Yuan, Zihan Yan, Beining Zhang 等ISCA 2025 · 被引用 2 次
