Adaptive Distributed Data Structure Management for Parallel CFD Applications
KAUST Grant NumberUK-c0020
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AbstractComputational fluid dynamics (CFD) simulations require a lot of computing resources in terms of CPU time and memory in order to compute with a reasonable physical accuracy. If only uniformly refined domains are applied, the amount of computing cells is growing rather fast if a certain small resolution is physically required. This can be remedied by applying adaptively refined grids. Unfortunately, due to the adaptive refinement procedures, errors are introduced which have to be taken into account. This paper is focussing on implementation details of the applied adaptive data structure management and a qualitative analysis of the introduced errors by analysing a Poisson problem on the given data structure, which has to be solved in every time step of a CFD analysis. Furthermore an adaptive CFD benchmark example is computed, showing the benefits of an adaptive refinement as well as measurements of parallel data distribution and performance. © 2013 IEEE.
CitationFrisch J, Mundani R-P, Rank E (2013) Adaptive Distributed Data Structure Management for Parallel CFD Applications. 2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. Available: http://dx.doi.org/10.1109/SYNASC.2013.73.
SponsorsThis publication is partially based on work supported byAward No. UK-c0020, made by King Abdullah University ofScience and Technology (KAUST). Furthermore, the authorswould like to cordially thank for the support and usage of theBlue Gene/P at Universitatea de Vest din Timi ̧soara (UVT)in Romania.
Journal2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing