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    Adaptive Distributed Data Structure Management for Parallel CFD Applications

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    Type
    Conference Paper
    Authors
    Frisch, Jerome
    Mundani, Ralf-Peter
    Rank, Ernst
    KAUST Grant Number
    UK-c0020
    Date
    2013-09
    Permanent link to this record
    http://hdl.handle.net/10754/597452
    
    Metadata
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    Abstract
    Computational 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.
    Citation
    Frisch 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.
    Sponsors
    This 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.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
    DOI
    10.1109/SYNASC.2013.73
    ae974a485f413a2113503eed53cd6c53
    10.1109/SYNASC.2013.73
    Scopus Count
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