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

Qin, Xinsheng; LeVeque, Randall J.; Motley, Michael R.

doi:10.1029/2019ms001635

Cited by 19 publications

(10 citation statements)

References 72 publications

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“…LISFLOOD-FP is a freely available raster-based hydrodynamic model that has been applied in numerous studies from small-scale (Sampson et al, 2012) and reach-scale (Liu et al, 2019;Shustikova et al, 2019;O'Loughlin et al, 2020) to continental and global flood forecasting applications (Wing et al, 2020;Sampson et al, 2015). LISFLOOD-FP has been coupled to several hydrological models (Hoch et al, 2019;Rajib et al, 2020;Li et al, 2020), and it offers simple text file configuration and command-line tools to facilitate DEM preprocessing and sensitivity analyses (Sosa et al, 2020). LISFLOOD-FP includes extension modules to provide efficient rainfall routing (Sampson et al, 2013), modelling of hydraulic structures (Wing et al, 2019;Shustikova et al, 2020), and coupling between two-dimensional flood-plain solvers and a one-dimensional sub-grid channel model (Neal et al, 2012a).…”

Section: Introductionmentioning

confidence: 99%

LISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUs

et al. 2021

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Abstract. LISFLOOD-FP 8.0 includes second-order discontinuous Galerkin (DG2) and first-order finite-volume (FV1) solvers of the two-dimensional shallow-water equations for modelling a wide range of flows, including rapidly propagating, supercritical flows, shock waves or flows over very smooth surfaces. The solvers are parallelised on multi-core CPU and Nvidia GPU architectures and run existing LISFLOOD-FP modelling scenarios without modification. These new, fully two-dimensional solvers are available alongside the existing local inertia solver (called ACC), which is optimised for multi-core CPUs and integrates with the LISFLOOD-FP sub-grid channel model. The predictive capabilities and computational scalability of the new DG2 and FV1 solvers are studied for two Environment Agency benchmark tests and a real-world fluvial flood simulation driven by rainfall across a 2500 km2 catchment. DG2's second-order-accurate, piecewise-planar representation of topography and flow variables enables predictions on coarse grids that are competitive with FV1 and ACC predictions on 2–4 times finer grids, particularly where river channels are wider than half the grid spacing. Despite the simplified formulation of the local inertia solver, ACC is shown to be spatially second-order-accurate and yields predictions that are close to DG2. The DG2-CPU and FV1-CPU solvers achieve near-optimal scalability up to 16 CPU cores and achieve greater efficiency on grids with fewer than 0.1 million elements. The DG2-GPU and FV1-GPU solvers are most efficient on grids with more than 1 million elements, where the GPU solvers are 2.5–4 times faster than the corresponding 16-core CPU solvers. LISFLOOD-FP 8.0 therefore marks a new step towards operational DG2 flood inundation modelling at the catchment scale. LISFLOOD-FP 8.0 is freely available under the GPL v3 license, with additional documentation and case studies at https://www.seamlesswave.com/LISFLOOD8.0 (last access: 2 June 2021).

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Section: Introductionmentioning

confidence: 99%

LISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUs

et al. 2021

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“…However, for ocean models that are usually developed by Fortran, it is costly not only for the developers to rewrite all the code but also for existing users to reacquaint themselves with the model. We notice that a number of studies on GPU‐accelerated hydrodynamic models have been reported which are based on CUDA Fortran (de La Asunción et al, ; Kim et al, ; Qin et al, ; Yamagishi & Matsumura, ; Zhang & Jia, ; Zhu et al, ). We also adopt CUDA Fortran in this study for the aforementioned reasons.…”

Section: Gpu Implementationmentioning

confidence: 99%

“…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%

“…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. By incorporating Tree‐based mesh‐refinement (Acuña & Aoki, ), Adaptive Mesh Refinement techniques (de la Asunción & Castro, ; Qin et al, ; Vacondio et al, ), the efficiency of a tsunami simulation can be further optimized without losing accuracy in the nearshore regions.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Huang et al, 2009;Qi et al, 2014;Dong et al, 2014;. The use of GPUs for shallow water and oceanographic simulations is currently also included in large commercial and academic software packages such as ClawPack (Qin et al, 2019), Telemac (Grasset et al, 2019), TUFLOW (Huxley and Syme, 2016) and MIKE (MIKE Powered by DHI, 2019).…”

Section: Related Workmentioning

confidence: 99%

Coastal ocean forecasting on the GPU using a two-dimensional finite-volume scheme

Brodtkorb

Holm

2021

Tellus A: Dynamic Meteorology and Oceanography

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In this work, we take a modern high-resolution finite-volume scheme for solving the rotational shallowwater equations and extend it with features required to run real-world ocean simulations. Our contributions include a spatially varying north vector and Coriolis term required for large scale domains, moving wet-dry fronts, a static land mask, bottom shear stress, wind forcing, boundary conditions for nesting in a global model, and an efficient model reformulation that makes it well-suited for massively parallel implementations. Our model order is verified using a grid convergence test, and we show numerical experiments using three different sections along the coast of Norway based on data originating from operational forecasts run at the Norwegian Meteorological Institute. Our simulation framework shows perfect weak scaling on a modern P100 GPU, and is capable of providing tidal wave forecasts that are very close to the operational model at a fraction of the cost. All source code and data used in this work are publicly available under open licenses.

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

Cited by 19 publications

References 72 publications

LISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUs

LISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUs

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

Coastal ocean forecasting on the GPU using a two-dimensional finite-volume scheme

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