Highly optimized full GPU-acceleration of non-hydrostatic weather model SCALE-LES

摘要

SCALE-LES is a non-hydrostatic weather model developed at RIKEN, Japan. It is intended to be a global high-resolution model that would be scaled to exascale systems. This paper introduces the full GPU acceleration of all SCALE-LES modules. Moreover, the paper demonstrates the strategies to handle the unique challenges of accelerating SCALE-LES using GPU. The proposed acceleration is important for identifying the expectations and requirements of scaling SCALE-LES, and similar real world applications, into the exascale era. The GPU implementation includes the optimized GPU acceleration of SCALE-LES for a single GPU with both CUDA Fortran and OpenACC. It also includes scaling SCALE-LES for GPU-accelerated clusters. The results and analysis show how the optimization strategies affect the performance gain in SCALE-LES when moving from conventional CPU clusters towards GPU-powered clusters.