An interpretable DeePMD-kit performance model for emerging supercomputers

Xiangyu Meng, Xun Wang, Mingzhen Li, Guangming Tan, Weile Jia

Abstract: Deep potential (DP) scheme has increased the simulation temporal and spatial scales while maintaining the ab initio accuracy of the molecular dynamics. DeePMD-kit is an outstanding application that implements DP scheme efficiently. However, current performance model cannot accurately measure the resource utilization of DeePMD-kit operators and predict the execution time. We introduce DP-perf, an interpretable performance model for DeePMD-kit. DP-perf can accurately measure the resource utilization of the individual DeePMD-kit operators, communication pattern, and the overall application by exploiting physical system properties and machine configurations. It can be easily applied to mainstream supercomputers including Tianhe-3F, the new Sunway, Fugaku, and Summit. With DP-perf, users can select the optimal machine and decide the corresponding configuration for various purposes (e.g., lower cost, less time) without real runs. Evaluation of four top supercomputers shows that DP-perf can fit overall execution time with a low mean absolute percentage error of 5.7 %/8.1%/14.3%/13.1% on Tianhe-3F/new Sunway/Fugaku/Summit. On the prediction scenario, DP-perf can predict the total execution time with a mean absolute percentage error of less than 20%.