2025/02/11 by Xun Wang, Xiangyu Meng, Zhuoqiang Guo +8 · 6 citations
Chemistry · Computer Science · Physics and Astronomy · #Chemistry #Computational chemistry #Computational science #Computer science #Molecular dynamics #Nuclear physics research studies #Parallel Computing and Optimization Techniques #Parallel computing #Radiation Detection and Scintillator Technologies #Supercomputer
paper · doi:10.1109/tc.2025.3540646
published in IEEE Transactions on Computers 74(5), 1634-1648 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2025/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Physical phenomena such as bond breaking and phase transitions require molecular dynamics (MD) withab initioaccuracy, involving up to billions of atoms and over nanosecond timescales. Previous state-of-the-art work has demonstrated that neural network molecular dynamics (NNMD) like deep potential molecular dynamics (DeePMD), can successfully extend the temporal and spatial scales of MD withab initioaccuracy on both ARM and GPU platforms. However, the DeePMD-kit package is currently unable to fully exploit the computational potential of the new Sunway supercomputer due to its unique many-core architecture, memory hierarchy, and low precision capability. In this paper, we re-design the DeePMD-kit to harness the massive computing power of the new Sunway, enabling the MD with over ten billion atoms. We first design a large-scale parallelization scheme to exploit the massive parallelism of the new Sunway. Then we devise specialized optimizations for the time-consuming operators. Finally, we design a novel mixed precision method for DeePMD-kit customized operators to leverage the low precision computing power of the new Sunway. The optimized DeePMD-kit achieves 67.6 / 56.5\boldsymbol×speedup for water / copper systems on the new Sunway. Meanwhile, it can perform 29 billion atoms simulation for the water system on 35 million cores (i.e., 90,000 computing nodes, around 84% of the whole supercomputer) with a peak performance of 57.1 PFLOPs, which is 7.9\boldsymbol×bigger and 1.2\boldsymbol×faster than state-of-the-art results. This paves the way for investigating more realistic scenarios, such as studying the mechanical properties of metals, semiconductor devices, batteries, and other materials and physical systems.