Massively Parallel Large Scale Inundation Modelling

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dc.contributor.authorRak, Arneen_US
dc.contributor.authorGuthe, Stefanen_US
dc.contributor.authorMewis, Peteren_US
dc.contributor.editorBujack, Roxanaen_US
dc.contributor.editorTierny, Julienen_US
dc.contributor.editorSadlo, Filipen_US
dc.date.accessioned2022-06-02T14:36:50Z
dc.date.available2022-06-02T14:36:50Z
dc.date.issued2022
dc.description.abstractOver the last 20 years, flooding has been the most common natural disaster, accounting for 44.7% of all disasters, affecting about 1.65 billion people worldwide and causing roughly 105 thousand deaths†. In contrast to other natural disasters, the impact of floods is preventable through affordable structures such as dams, dykes and drainage systems. To be most effective, however, these structures have to be planned and evaluated using the highest precision data of the underlying terrain and current weather conditions. Modern laser scanning techniques provide very detailed and reliable terrain information that may be used for flood inundation modelling in planning and hazard warning systems. These warning systems become more important since flood hazards increase in recent years due to ongoing climate change. In contrast to simulations in planning, simulations in hazard warning systems are time critical due to potentially fast changing weather conditions and limited accuracy in forecasts. In this paper we present a highly optimized CUDA implementation of a numerical solver for the hydraulic equations. Our implementation maximizes the GPU's memory throughput, achieving up to 80% utilization. A speedup of a factor of three is observed in comparison to previous work. Furthermore, we present a low-overhead, in-situ visualization of the simulated data running entirely on the GPU. With this, an area of 15 km2 with a resolution of 1 m can be visualized hundreds of times faster than real time on consumer grade hardware. Furthermore, the flow settings can be changed interactively during computation.en_US
dc.description.sectionheadersLarge Scale Visualization
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualization
dc.identifier.doi10.2312/pgv.20221063
dc.identifier.isbn978-3-03868-175-5
dc.identifier.issn1727-348X
dc.identifier.pages31-35
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/pgv.20221063
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pgv20221063
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing --> Scientific visualization; Geographic visualization; Computing methodologies --> Realtime simulation; Massively parallel and high-performance simulations; Massively parallel algorithms
dc.subjectHuman centered computing
dc.subjectScientific visualization
dc.subjectGeographic visualization
dc.subjectComputing methodologies
dc.subjectRealtime simulation
dc.subjectMassively parallel and high
dc.subjectperformance simulations
dc.subjectMassively parallel algorithms
dc.titleMassively Parallel Large Scale Inundation Modellingen_US
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EGPGV22: Eurographics Symposium on Parallel Graphics and Visualization