The Community Atmosphere Model (CAM) is ported, redesigned, and scaled to the full system of the Sunway TaihuLight, and provides peta-scale climate modeling performance. We refactored and optimized the complete code using OpenACC directives at the first stage. A more aggressive and finer-grained redesign is then applied on the CAM, to achieve finer memory control and usage, more efficient vectorization and compute and communication overlapping. We further improve the CAM performance of a 260-core Sunway processor to the range of 28 to 184 Intel CPU cores, and achieve a sustainable double-precision performance of 3.3 PFlops for a 750 m global simulation when using 10,075,000 cores. CAM on Sunway achieves the simulation speed of 3.4 and 21.5 simulation-year-per-day (SYPD) for global 25-km and 100-km resolution respectively; and enables us to perform, to our knowledge, the first simulation of the complete lifecycle of hurricane Katrina, and achieve close-to-observation simulation results for both track and intensity.

This paper reports our efforts on refactoring and optimizing the Community Atmosphere Model (CAM) on the Sunway TaihuLight supercomputer, which uses a many-core processor that consists of management processing elements (MPEs) and clusters of computing processing elements (CPEs). To map the large code base of CAM to the millions of cores on the Sunway system, we take OpenACC-based refactoring as the major approach, and apply source-to-source translator tools to exploit the most suitable parallelism for the CPE cluster, and to fit the intermediate variable into the limited on-chip fast buffer. For individual kernels, when comparing the original ported version using only MPEs and the refactored version using both the MPE and CPE clusters, we achieve up to 22× speedup for the compute-intensive kernels. For the 25km resolution CAM global model, we manage to scale to 24,000 MPEs, and 1,536,000 CPEs, and achieve a simulation speed of 2.81 model years per day.

An ultra-scalable fully-implicit solver is developed for stiff time-dependent problems arising from the hyperbolic conservation laws in nonhydrostatic atmospheric dynamics. In the solver, we propose a highly efficient hybrid domain-decomposed multigrid preconditioner that can greatly accelerate the convergence rate at the extreme scale. For solving the overlapped subdomain problems, a geometry-based pipelined incomplete LU factorization method is designed to further exploit the on-chip fine-grained concurrency. We perform systematic optimizations on different hardware levels to achieve best utilization of the heterogeneous computing units and substantial reduction of data movement cost. The fully-implicit solver successfully scales to the entire system of the Sunway TaihuLight supercomputer with over 10.5M heterogeneous cores, sustaining an aggregate performance of 7.95 PFLOPS in double-precision, and enables fast and accurate atmospheric simulations at the 488-m horizontal resolution (over 770 billion unknowns) with 0.07 simulated-years-per-day. This is, to our knowledge, the largest fully-implicit simulation to date.

This paper is concerned with output feedback control for two-dimensional (2-D) T-S fuzzy systems described by the Fornasini-Marchesini (FM) second model. A 2-D T-S fuzzy model is established first by taking its structural feature into consideration. This implication format can generally reduce the long and tedious fuzzy implications, resulting in a comparatively less computational demand when premise variables increase. Then by using matrix's singular value decomposition, sufficient conditions for the 2-D fuzzy systems to be asymptotically stable are given in terms of linear matrix inequalities (LMIs), which can be easily obtained by Matlab's LMI toolbox. Finally, a numerical example demonstrates the validity of this approach.