Query-driven multiscale data postprocessing in computational fluid dynamics

Atanas Atanasov, Tobias Weinzierl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Massively parallel computational fluid 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 fluid 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. © 2011 Published by Elsevier Ltd.
Original languageEnglish (US)
Title of host publicationProcedia Computer Science
PublisherElsevier BV
Number of pages10
StatePublished - May 14 2011
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-28
Acknowledged KAUST grant number(s): UK-c0020
Acknowledgements: This publication is partially based on work supported by Award No. UK-c0020, made by the King Abdullah University of Science and Technology (KAUST). We furthermore thank the DEISA Consortium (www.deisa.eu), co-funded through the EU FP6 project RI-031513 and the FP7 project RI-222919, for support within the DEISA Extreme Computing Initiative. Additional thanks are due to Mihaela Mihai and Denis Jarema for implementing several software components used in this work.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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