Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

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dc.contributor.authorLoring, Burlenen_US
dc.contributor.authorWolf, Mathewen_US
dc.contributor.authorKress, Jamesen_US
dc.contributor.authorShudler, Sergeien_US
dc.contributor.authorGu, Junminen_US
dc.contributor.authorRizzi, Silvioen_US
dc.contributor.authorLogan, Jeremyen_US
dc.contributor.authorFerrier, Nicolaen_US
dc.contributor.authorBethel, E. Wesen_US
dc.contributor.editorFrey, Steffen and Huang, Jian and Sadlo, Filipen_US
dc.date.accessioned2020-05-24T13:24:38Z
dc.date.available2020-05-24T13:24:38Z
dc.date.issued2020
dc.description.abstractIn an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N: One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an ''intelligent'' mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.en_US
dc.description.sectionheadersVisualization
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualization
dc.identifier.doi10.2312/pgv.20201073
dc.identifier.isbn978-3-03868-107-6
dc.identifier.issn1727-348X
dc.identifier.pages35-45
dc.identifier.urihttps://doi.org/10.2312/pgv.20201073
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pgv20201073
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.subjectSoftware and its engineering
dc.subjectSoftware performance
dc.subjectHuman centered computing
dc.subjectVisualization systems and tools
dc.subjectComputing methodologies
dc.subjectParallel algorithms
dc.titleImproving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualizationen_US
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EGPGV20: Eurographics Symposium on Parallel Graphics and Visualization