Accelerating an Adaptive Mesh Refinement Code for Depth‐Averaged Flows Using GPUs

Abstract

AbstractSolving the shallow water equations efficiently is critical to the study of natural hazards induced by tsunami and storm surge, since it provides more response time in an early warning system and allows more runs to be done for probabilistic assessment where thousands of runs may be required. Using adaptive mesh refinement speeds up the process by greatly reducing computational demands while accelerating the code using the graphics processing unit (GPU) does so through using faster hardware. Combining both, we present an efficient CUDA implementation of GeoClaw, an open source Godunov‐type high‐resolution finite volume numerical scheme on adaptive grids for shallow water system with varying topography. The use of adaptive mesh refinement and spherical coordinates allows modeling transoceanic tsunami simulation. Numerical experiments on the 2011 Japan tsunami and a local tsunami triggered by a hypothetical Mw 7.3 earthquake on the Seattle Fault illustrate the correctness and efficiency of the code, which implements a simplified dimensionally split version of the algorithms. Both numerical simulations are conducted on subregions on a sphere with adaptive grids that adequately resolve the propagating waves. The implementation is shown to be accurate and faster than the original when using Central Processing Units (CPUs) alone. The GPU implementation, when running on a single GPU, is observed to be 3.6 to 6.4 times faster than the original model running in parallel on a 16‐core CPU. Three metrics are proposed to evaluate relative performance of the model, which shows efficient usage of hardware resources.