A GPU-accelerated shallow flow model for tsunami simulations

Amouzgar, Reza; Liang, Qiuhua; Smith, Luke

doi:10.1680/eacm.13.00024

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…In particular, the porting of tsunami models to GPU devices has been a priority of the international tsunami research community in the wake of the devastating 2011 Tohoku Tsunami. Currently, the majority of mainstream tsunami models, mostly based on linear or nonlinear shallow water equations discretized with different orders of accuracy (e.g., Tsunami‐HySEA, MOST, GeoClaw, TUNAMI‐N1, EASYWAVE, and COMCOT), have been accelerated by GPU devices for real‐time tsunami warning purpose at a lower cost (Amouzgar et al, ; Castro et al, ; de La Asunción et al, ; Gidra et al, ; Harig et al, ; Qin et al, ; Satria et al, ). Typically, the computational time for basin‐scale tsunami propagation modeling can be reduced to several minutes, or even tens of seconds in some simplified cases.…”

Section: Introductionmentioning

confidence: 99%

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

Yuan

Shi

Kirby

et al. 2020

J Adv Model Earth Syst

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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.Plain Language Summary Numerical modeling of surface wave dynamics is necessary for coastal infrastructure design. FUNWAVE-Total Variation Diminishing is a widely accepted open-source wave model for simulating surface wave propagation and wave-driven processes in the nearshore region, as well as tsunami wave propagation at oceanic scales. Due to the complexity of governing equations and corresponding numerical methods, the modeling of wave dynamics usually depends on the use of High Performance Clusters, which are both expensive and power consuming. To address this problem, Graphics Processing Unit (GPU)-accelerated computing is introduced in the FUNWAVE-Total Variation Diminishing for wave dynamics modeling in this study. GPUs were originally used for image processing and visualization purpose in personal computers. Because GPUs have thousands of "Cores" that can implement arithmetic computations simultaneously, they are now widely employed to facilitate computing-intensive tasks such as deep learning and engineering computations. We find that by porting wave model to GPU devices, the modeling of surface wave dynamics over a large domain can be achieved by an affordable stand-alone PC with GPU cards installed. Key Points:• The fully nonlinear Boussinesq wave model FUNWAVE-TVD is ported to multiple-GPU for acceleration • The GPU version is ideal for solving wave problems over large computational domains in a stand-alone machineAs an efficient, portable yet commercially available substitute for HPC clusters, GPUs have played a central role in implementing massive computation across a wide range of areas, among which GPU acceleration of CFD algorithms was one of the main areas in the past few years. The key to the success of GPU computing is partly attributed to its capability of massive computation charac...

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Section: Introductionmentioning

confidence: 99%

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

Yuan

Shi

Kirby

et al. 2020

J Adv Model Earth Syst

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“…In this work a GPU-accelerated second-order accurate hydrodynamic model is presented for tsunami simulations, which is an extension of the first-order accurate model previously reported by the authors (Amouzgar et al, 2014) and better suited for practical tsunami simulations. The model solves the 2D SWEs using a finite volume Godunov-type scheme incorporated with an HLLC approximate Riemann solver.…”

Section: Introductionmentioning

confidence: 99%

Computationally Efficient Tsunami Modelling on Graphics Processing Units (GPU)

Amouzgar¹,

Liang²,

Clarke³

et al. 2017

Preprint

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Tsunamis generated by earthquakes commonly propagate as long waves in the deep ocean and develop into sharp-fronted surges moving rapidly towards the coast in shallow water, which may be effectively simulated by hydrodynamic models solving the nonlinear shallow water equations (SWEs). However, most of the existing tsunami models suffer from long simulation time for large-scale real-world applications. In this work, a graphics processing unit (GPU) accelerated finite volume shock-capturing hydrodynamic model is presented for computationally efficient tsunami simulations. The improved performance of the GPU-accelerated tsunami model is demonstrated through a laboratory benchmark test and a field-scale simulation.

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“…In this work, a GPU-accelerated second-order accurate hydrodynamic model is presented for tsunami simulations, which is an extension of the first-order accurate model previously reported by the authors (Amouzgar et al, 2014) and better suited for practical tsunami simulations. The model solves the 2-D SWEs using a finite volume Godunov-type scheme incorporated with an HLLC approximate Riemann solver.…”

Section: Introductionmentioning

confidence: 99%

Computationally Efficient Tsunami Modeling on Graphics Processing Units (GPUs)

Amouzgar

Liang

Clarke

et al. 2016

IJOPE

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Tsunamis generated by earthquakes commonly propagate as long waves in the deep ocean and develop into sharp-fronted surges moving rapidly toward the coast in shallow water, which may be effectively simulated by hydrodynamic models solving the nonlinear shallow water equations (SWEs). However, most of the existing tsunami models suffer from long simulation time for large-scale real-world applications. In this work, a graphics processing unit (GPU)-accelerated finite volume shockcapturing hydrodynamic model is presented for computationally efficient tsunami simulations. The improved performance of the GPU-accelerated tsunami model is demonstrated through a laboratory benchmark test and a field-scale simulation.

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A GPU-accelerated shallow flow model for tsunami simulations

Cited by 5 publications

References 20 publications

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

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

Computationally Efficient Tsunami Modelling on Graphics Processing Units (GPU)

Computationally Efficient Tsunami Modeling on Graphics Processing Units (GPUs)

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