Academic Journal

Query-driven Multiscale Data Postprocessing in Computational Fluid Dynamics

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Query-driven Multiscale Data Postprocessing in Computational Fluid Dynamics
Συγγραφείς: Atanasov, Atanas1 atanasoa@in.tum.de, Weinzierl, Tobias1 weinzier@in.tum.de
Πηγή: Procedia Computer Science. Sep2011, Vol. 4, p332-341. 10p.
Θεματικοί όροι: Electronic data processing, Computational fluid dynamics, Multiscale modeling, Querying (Computer science), Computational steering (Computer science), Numerical solutions to boundary value problems, Parallel computers, Problem solving
Περίληψη: Abstract: Massively parallel computational uid dynamics codes that have to stream solution data to a visualisation or postprocessing component in each time step often are IO-bounded. This is especially cumbersome if the succeeding components require the simulation data only in a coarse resolution or only in specific subregions. We suggest to replace the streaming data approach found in many applications with a query-driven communication paradigm where the postprocessing components explicitly inform the uid solver which data they need in which resolution in which subregions. Two case studies reveal that such a data exchange paradigm reduces the memory footprint of the exchanged data as well as the latency of the data delivery, and that the approach scales. In particular geometric multigrid solvers based upon a non-overlapping domain decomposition can answer such queries efficiently. [Copyright &y& Elsevier]
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  Data: Query-driven Multiscale Data Postprocessing in Computational Fluid Dynamics
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  Data: <searchLink fieldCode="AR" term="%22Atanasov%2C+Atanas%22">Atanasov, Atanas</searchLink><relatesTo>1</relatesTo><i> atanasoa@in.tum.de</i><br /><searchLink fieldCode="AR" term="%22Weinzierl%2C+Tobias%22">Weinzierl, Tobias</searchLink><relatesTo>1</relatesTo><i> weinzier@in.tum.de</i>
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  Data: <searchLink fieldCode="JN" term="%22Procedia+Computer+Science%22">Procedia Computer Science</searchLink>. Sep2011, Vol. 4, p332-341. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+fluid+dynamics%22">Computational fluid dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Multiscale+modeling%22">Multiscale modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Querying+%28Computer+science%29%22">Querying (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+steering+%28Computer+science%29%22">Computational steering (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+solutions+to+boundary+value+problems%22">Numerical solutions to boundary value problems</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+computers%22">Parallel computers</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink>
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  Data: Abstract: Massively parallel computational uid dynamics codes that have to stream solution data to a visualisation or postprocessing component in each time step often are IO-bounded. This is especially cumbersome if the succeeding components require the simulation data only in a coarse resolution or only in specific subregions. We suggest to replace the streaming data approach found in many applications with a query-driven communication paradigm where the postprocessing components explicitly inform the uid solver which data they need in which resolution in which subregions. Two case studies reveal that such a data exchange paradigm reduces the memory footprint of the exchanged data as well as the latency of the data delivery, and that the approach scales. In particular geometric multigrid solvers based upon a non-overlapping domain decomposition can answer such queries efficiently. [Copyright &y& Elsevier]
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