Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.08250 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908949484142592 |
|---|---|
| author | Liu, Xianglin Yang, Kai Zhou, Fanli Liu, Yongxiang Chen, Hao Zhang, Yijia Fan, Dengdong Li, Wenbo Wang, Bingqiang Zhang, Shixun Xu, Pengxiang Tian, Yonghong |
| author_facet | Liu, Xianglin Yang, Kai Zhou, Fanli Liu, Yongxiang Chen, Hao Zhang, Yijia Fan, Dengdong Li, Wenbo Wang, Bingqiang Zhang, Shixun Xu, Pengxiang Tian, Yonghong |
| contents | The rapid advancement of deep learning is reshaping the hardware design landscape toward AI tasks, posing fundamental challenges for HPC workloads such as atomistic simulation. Here we present SMC-AI, a general algorithmic framework that extends the SMC-X method for efficient canonical Monte Carlo simulation on AI accelerators, including GPUs and NPUs, while maintaining extreme scalability. The implementation of SMC-AI on an NPU cluster reaches unprecedented performance, achieving MC simulation of 4 trillion atoms on 4096 NPU dies. This represents the largest ML-accelerated atomistic simulation reported, delivering 32X system size and 1.3X throughput than previous records, with a relatively small computational budget. Excellent strong and weak scaling efficiency are reached for both the NPU and GPU implementation. By decoupling ML models from simulation, SMC-AI creates an abstraction that facilitates integration and porting of diverse ML models, laying a foundation for the future development of scalable scientific software. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08250 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SMC-AI: Scaling Monte Carlo Simulation to Four Trillion Atoms with AI Accelerators Liu, Xianglin Yang, Kai Zhou, Fanli Liu, Yongxiang Chen, Hao Zhang, Yijia Fan, Dengdong Li, Wenbo Wang, Bingqiang Zhang, Shixun Xu, Pengxiang Tian, Yonghong Computational Physics The rapid advancement of deep learning is reshaping the hardware design landscape toward AI tasks, posing fundamental challenges for HPC workloads such as atomistic simulation. Here we present SMC-AI, a general algorithmic framework that extends the SMC-X method for efficient canonical Monte Carlo simulation on AI accelerators, including GPUs and NPUs, while maintaining extreme scalability. The implementation of SMC-AI on an NPU cluster reaches unprecedented performance, achieving MC simulation of 4 trillion atoms on 4096 NPU dies. This represents the largest ML-accelerated atomistic simulation reported, delivering 32X system size and 1.3X throughput than previous records, with a relatively small computational budget. Excellent strong and weak scaling efficiency are reached for both the NPU and GPU implementation. By decoupling ML models from simulation, SMC-AI creates an abstraction that facilitates integration and porting of diverse ML models, laying a foundation for the future development of scalable scientific software. |
| title | SMC-AI: Scaling Monte Carlo Simulation to Four Trillion Atoms with AI Accelerators |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2604.08250 |