Global Storm Resolving Models (GSRMs) is crucial for understanding extreme weather events under the climate change background. In this study, we optimize Global-Regional Integrated Forecast System (GRIST), which is a unified weather-climate modeling system designed for research and operation, for the next-generation Sunway supercomputer, incorporating AI-enhanced physics suite, OpenMP-based parallelization, and mixed-precision optimizations to enhance both efficiency and performance portability, as well as the unified modeling capability. Our experiments successfully capture significant events during the "23.7" extreme rainfall over northern China influenced by super Typhoon Doksuri, at 1km resolution. Notably, our work scales to 34 million cores, enabling simulation speeds at 491 SDPD (3km) and 181 SDPD (1km).

MPI neighborhood communication with sparse and imbalanced patterns is common in process-level parallel programs. However, these programs often encounter significant performance slowdowns in today’s many-core clusters that feature dozens of cores per node. There are two key causes for this slowdown. First, there is substantial competition for memory and network ports when a large number of processes simultaneously access the MPI library. Second, many neighborhood communications do not align well with the many-core architecture, resulting in performance bottlenecks that could have been mitigated.

Half-precision hardware support is now almost ubiquitous. In contrast to its active use in AI, half-precision is less commonly employed in scientific and engineering computing. The valuable proposition of accelerating scientific computing applications using half-precision prompted this study. Focusing on solving sparse linear systems in scientific computing, we explore the technique of utilizing FP16 in multigrid preconditioners. Based on observations of sparse matrix formats, numerical features of scientific applications, and the performance characteristics of multigrid, this study formulates four guidelines for FP16 utilization in multigrid. The proposed algorithm demonstrates how to avoid FP16 overflow through scaling. A setup-then-scale strategy prevents FP16’s limited accuracy and narrow range from interfering with the multigrid’s numerical properties. Another strategy, recover-and-rescale on the fly, reduces the memory footprint of hotspot kernels. The extra precision-conversion overhead in mix-precision kernels is addressed by the transformation of storage formats and SIMD implementation. Two ablation experiments validate the effectiveness of our algorithm and parallel kernel implementation on ARM and X86 architectures. We further evaluate three idealized and five real-world problems to demonstrate the advantage of utilizing FP16 in a multigrid preconditioner. The average speedups are approximately 2.75x and 1.95x in preconditioner and end-to-end workflow, respectively.

Parallel multigrid is widely used as preconditioners in solving large-scale sparse linear systems. However, the current multigrid library still needs more satisfactory performance for structured grid problems regarding speed and scalability. To this end, we design and implement StructMG, a fast and scalable multigrid that constructs hierarchical grids automatically based on the original matrix. As a preconditioner, StructMG can achieve both low cost per iteration and good convergence. Two idealized and five real-world problems from four application fields, including radiation hydrodynamics, petroleum reservoir simulation, numerical weather prediction, and solid mechanics, are evaluated on ARM and X86 platforms. In comparison to hypre's multigrid preconditioners, StructMG achieves the fastest time-to-solutions in all cases with average speedups of 17.6x, 5.7x, 4.6x, 8.5x over SMG, PFMG, SysPFMG, and BoomerAMG, respectively. Additionally, StructMG significantly improves strong and weak scaling efficiencies in most tests.