# Efficient Tsunami Modeling on Adaptive Grids with Graphics Processing Units (GPUs)

## TL;DR

This paper presents a GPU-accelerated adaptive grid tsunami modeling method that significantly speeds up simulations, enabling faster and more efficient hazard assessment and early warning systems.

## Contribution

It introduces a CUDA implementation of GeoClaw combining adaptive mesh refinement and GPU acceleration for efficient tsunami simulation.

## Key findings

- GPU implementation is 3.6 to 6.4 times faster than CPU version.
- The method accurately models transoceanic tsunamis with reduced computational time.
- Proposed metrics effectively evaluate hardware resource utilization.

## Abstract

Solving 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 (AMR) 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 AMR and spherical coordinates allows modeling transoceanic tsunami simulation. Numerical experiments on several realistic tsunami modeling problems illustrate the correctness and efficiency of the code, which implements a simplified dimensionally-split version of the algorithms. This implementation is shown to be accurate and faster than the original when using 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.