FUNWAVE-GPU: Multiple-GPU Acceleration of a Boussinesq-Type Wave Model

Abstract

This paper documents development of a multiple-Graphics Processing Unit (GPU) version of FUNWAVE-Total Variation Diminishing (TVD), an open-source model for solving the fully nonlinear Boussinesq wave equations using a high-order TVD solver. The numerical schemes of FUNWAVE-TVD, including Cartesian and spherical coordinates, are rewritten using CUDA Fortran, with inter-GPU communication facilitated by the Message Passing Interface. Since FUNWAVE-TVD involves the discretization of high-order dispersive derivatives, the on-chip shared memory is utilized to reduce global memory access. To further optimize performance, the batched tridiagonal solver is scheduled simultaneously in multiple-GPU streams, which can reduce the GPU execution time by 20–30%. The GPU version is validated through a benchmark test for wave runup on a complex shoreline geometry, as well as a basin-scale tsunami simulation of the 2011 Tohoku-oki event. Efficiency evaluation shows that, in comparison with the CPU version running at a 36-core HPC node, speedup ratios of 4–7 and above 10 can be observed for single- and double-GPU runs, respectively. The performance metrics of multiple-GPU implementation needs to be further evaluated when appropriate.