Engineering-Based Thermal CFD Simulations on Massive Parallel Systems
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ArticleKAUST Grant Number
UK-c0020Date
2015-05-22Permanent link to this record
http://hdl.handle.net/10754/596994
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The development of parallel Computational Fluid Dynamics (CFD) codes is a challenging task that entails efficient parallelization concepts and strategies in order to achieve good scalability values when running those codes on modern supercomputers with several thousands to millions of cores. In this paper, we present a hierarchical data structure for massive parallel computations that supports the coupling of a Navier–Stokes-based fluid flow code with the Boussinesq approximation in order to address complex thermal scenarios for energy-related assessments. The newly designed data structure is specifically designed with the idea of interactive data exploration and visualization during runtime of the simulation code; a major shortcoming of traditional high-performance computing (HPC) simulation codes. We further show and discuss speed-up values obtained on one of Germany’s top-ranked supercomputers with up to 140,000 processes and present simulation results for different engineering-based thermal problems.Citation
Frisch J, Mundani R-P, Rank E, van Treeck C (2015) Engineering-Based Thermal CFD Simulations on Massive Parallel Systems. Computation 3: 235–261. Available: http://dx.doi.org/10.3390/computation3020235.Sponsors
This publication is partially based on work supported by Award No. UK-c0020, made by KAUST. Furthermore, the authors would like to thank LRZ in Germany for the support and usage of SuperMUC during their ‘Extreme Scaling Workshop’, held in June 2014, and UVT in Romania for the support and usage of their BlueGene/P.Publisher
MDPI AGJournal
Computationae974a485f413a2113503eed53cd6c53
10.3390/computation3020235
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Except where otherwise noted, this item's license is described as This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.