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(2020) : Hybrid-Parallel Simulations and Visualisations of Real Flood and Tsunami Events Using Unstructured Meshes on High-Performance Cluster Systems In: Gourbesville, Philippe; Caignaert, Guy (Hg.): Advances in Hydroinformatics: Models for Extreme Situations and Crisis Management: Proceedings: SymHydro: Nizza, 12. - 14. Juni 2019: Singapore: Springer (Springer Water), S. 867-888
Abstract: We present simulations of real flood and tsunami events using a hybrid OpenMP-MPI model on high-performance cluster systems. The two-dimensional shallow water equations were solved by means of the in-house code NUFSAW2D, using an edge-based cell-centred finite volume method with the central-upwind scheme for millions of unstructured cells, thus ensuring spatial accuracy, especially near buildings or hydraulic structures. Each node of a cluster system performed simulations using OpenMP and communicated with other nodes using MPI. We explain strategies on reordering the meshes to support contiguous memory access patterns and to minimise communication cost; to this end, a simple criterion was proposed to decide the strategy used. Despite employing static domain decompositions for such unstructured meshes, the computation loads were distributed dynamically based on the complexity level, to each core and node during runtime to ensure computational efficiency. Our model was tested by simulating two real-life cases: the 2011 flood event in Kulmbach (Germany) and the Japan 2011 tsunami recorded in Hilo Harbour, Hawaii (USA). The numerical results show that our model is robust and accurate when simulating such complex flood phenomena, while the hybrid parallelisation concept proposed proves to be quite efficient. We also provide an outlook for an advanced visualisation method employing the Sliding Window technique with an HDF5 data structure. With such a combination of high-performance computing and interactive visualisation, users have a comprehensive predictive tool to take immediate measures and to support decision makers in developing a well-integrated early warning system.
(2020): Numerische Simulation: von der Formel zum bunten Bild. Oder wie Computer helfen, physikalische Phänomene besser zu verstehen und vorherzusagen. Numerical Simulation: from formulas to colourful pictures. Or how computers can help to understand and predict physical phenomena. In: Information. Wissenschaft & Praxis 71 (5-6), S. 331-335. Available online at https://doi.org/10.1515/iwp-2020-2121, last checked on 30.10.2020
Abstract: Numerische Simulation dient der Vorhersage und Analyse komplexer physikalischer Zusammenhänge, die im Gegensatz zum meist (deutlich) teureren Experiment am Rechner durchgeführt wird und damit beliebig oft wiederholt werden kann. Auf Basis mathematischer Modelle wird die Lösung eines Problems mithilfe numerischer Verfahren berechnet und zum besseren visuellen Verständnis in graphischer Form als Bild oder Film dargestellt. Im vorliegenden Beitrag soll hierzu die gesamte Prozesskette – von der Formel zum bunten Bild – am Beispiel der Hochwassersimulation aufgezeigt werden.
(2020): Advanced Visualisation, Analysis, and Parallelisation Concepts for Multi-Scale CFD Simulations in Science and Engineering. Mini-Symposium. SIAM Conference on Parallel Processing for Scientific Computing. Society for Industrial and Applied Mathematics. Seattle; 12. - 15. Februar, 2020
Abstract: Due to recent advances in supercomputing, more and more scientific questions - especially from the so-called emerging sciences such as medicine, sociology, biology, virology, chemistry, climate or geo-sciences - can be answered today using high-performance computing (HPC). Such questions could cover the structural analysis of buildings and constructions in a global context (e.g. earth quakes), the prediction of floods and flooding damages due to heavy rainfall, the thread of tsunamis on coastal regions, the risk analysis of pollutant diffusion in populated regions, the simulation of evacuation scenarios on the facility, urban quarter, and city scale, or the optimisation of traffic flow within entire cities during rush hour; just to name a few. On the other side, “high-performance computing must now assume a broader meaning, encompassing not only flops, but also the ability, for example, to efficiently manipulate vast and rapidly increasing quantities of both numerical and non-numerical data” (Kalil, Miller: Advancing U.S. Leadership in High-Performance Computing. The White House, 2015.). In this minisymposium, different aspects of multi-scale, multi-level, multi-physics applications from science and engineering should be addressed, dealing with topics “but not limited to” such as parallelisation strategies, advanced numerical algorithms, coupling interfaces, data orchestration, interaction concepts, (big) data exploration, or visual data analytics.