2025/02/11 by Xun Wang, Xiangyu Meng, Zhuoqiang Guo +8 · 2 citations
Physics and Astronomy · Computer Science · #Radiation Detection and Scintillator Technologies #Parallel Computing and Optimization Techniques #Nuclear physics research studies
paper · doi:10.1109/tc.2025.3540646
Physical phenomena such as bond breaking and phase transitions require molecular dynamics (MD) with <i>ab initio</i> accuracy, 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 with <i>ab initio</i> accuracy 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 <inline-formula><tex-math notation="LaTeX">\boldsymbol×</tex-math></inline-formula> 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<inline-formula><tex-math notation="LaTeX">\boldsymbol×</tex-math></inline-formula> bigger and 1.2<inline-formula><tex-math notation="LaTeX">\boldsymbol×</tex-math></inline-formula> 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.