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.

The Weather Research and Forecasting (WRF) Model is one of the widely-used mesoscale numerical weather prediction system and is designed for both atmospheric research and operational forecasting applications. However, it is an extremely time-consuming application: running a single simulation takes researchers days to weeks as the simulation size scales up and computing demands grow. In this paper, we port and optimize the whole WRF model to the Sunway TaihuLight supercomputer at a large scale. For the dynamic core in WRF, we present a domain-specific tool, namely, SWSLL, which is a directive-based compiler tool for the Sunway many-core architecture to convert the stencil computation into optimized parallel code. We also apply a decomposition strategy for SWSLL to improve the memory locality and decrease the number of off-chip memory accesses. For physical parameterizations, we explore the thread-level parallelization using OpenACC directives via reorganizations of data layouts and loops to achieve high performance. We present the algorithms and implementations and demonstrate the optimizations of a real-world complicated atmospheric modeling on the Sunway TaihuLight supercomputer. Evaluation results reveal that for the widely used benchmark with a horizontal resolution of 2.5 km, the speedup of 4.7 can be achieved by using the proposed algorithm and optimization strategies for the whole WRF model. In terms of strong scalability, our implementation scales well to hundreds of thousands of heterogeneous cores on Sunway TaihuLight.

Surface wave is the most energetic form of motions in the ocean and is crucially important to navigation safety and climate change. High-resolution global wave model plays a key role in accurate surface wave forecasting. However, operational forecasting systems are still not in high-resolution due to entailed high demand for large computation, as well as low parallel efficiency barrier. Here breakthroughs encompassing the design and application of irregular quasi-rectangular domain decomposition, master-slave cooperative computing workflow and pipelining scheme were applied to a global wave model, which has been used in several operational forecasting systems and earth system models. Our realistic surface wave simulations on Sunway TaihuLight Supercomputer demonstrated that our model had outstanding scalability and achieved 45.43 PFlops in ultra-high resolution of (1/100)°, using full-scale supercomputer with 10,649,600 cores. That provides a highly effective solution for accurate surface wave forecasting and climate change prediction.