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

James Shaw*, Georges Kesserwani*, Jeffrey Neal, Paul Bates, Mohammad Kazem Sharifian

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

32 Citations (Scopus)
64 Downloads (Pure)


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. Alongside the existing local inertia solver (called ACC), the new solvers are parallelised on multi-core CPU and Nvidia GPU architectures and run existing LISFLOOD-FP modelling scenarios without modification. The predictive capabilities and computational scalability of the new 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 × 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 × 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.
Original languageEnglish
Pages (from-to)3577–3602
Number of pages25
JournalGeoscientific Model Development
Issue number6
Early online date11 Jun 2021
Publication statusE-pub ahead of print - 11 Jun 2021


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