An MPI-CUDA implementation of an improved Roe method for two-layer shallow water systems

The numerical solution of two-layer shallow water systems is required to simulate accurately stratified fluids, which are ubiquitous in nature: they appear in atmospheric flows, ocean currents, oil spills, . . . Moreover, the implementation of the numerical schemes to solve these models in realistic scenarios imposes huge demands of computing power. In this paper, we tackle the acceleration of these simulations in triangular meshes by exploiting the combined power of several CUDA-enabled GPUs in a GPU cluster. For that purpose, an improvement of a path conservative Roe type finite volume scheme which is specially suitable for GPU implementation is presented, and a distributed implementation of this scheme which uses CUDA and MPI to exploit the potential of a GPU cluster is developed. This implementation overlaps MPI communication with CPU-GPU memory transfers and GPU computation to increase efficiency. Several numerical experiments performed on a cluster of modern CUDA-enabled GPUs show the efficiency of the distributed solver

Pseudo-Probabilistic Design for High-Resolution Tsunami Simulations in the Southwestern Spanish Coast

The application of simulation software has proven to be a crucial tool for tsunami hazard assessment studies. Understanding the potentially devastating effects of tsunamis leads to the development of safety and resilience measures, such as the design of evacuation plans or the planning of the economic investment necessary to quickly mitigate their consequences. This article introduces a pseudo-probabilistic seismic-triggered tsunami simulation approach to investigate the potential impact of tsunamis in the southwestern coast of Spain, in the provinces of Huelva and Cádiz. Selected faults, probabilistic distributions and sampling methods are presented as well as some results for the nearly 900 Atlantic-origin tsunamis computed along the 250 km-long coast.This work has being carried out under a project funded by a public mutual agreement of understanding between the CN-IGME (CSIC) and the CCS (Law reference: BOE 103, 30/04/2019). This project is supported by an agreement of understanding between CN-IGME and UMA, creating a cooperative entity INGEA (Law reference: BOE 332, 22/12/2020). The numerical results presented in this work have been performed with the computational resources allocated by the Spanish Network for Supercomputing (RES) grants AECT-2020-3-0023 and AECT-2021-2-0018. Further support has also been received from the Spanish Government research project MEGAFLOW (RTI2018-096064-B-C21) and ChEESE project (EU Horizon 2020, grant agreement No. 823844, https://cheese-coe.eu/) due to the synergies found between the projects. Partial funding for open access charge: Universidad de Málag