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dc.contributor.authorTsai, Karen C.en_US
dc.contributor.authorBujack, Roxanaen_US
dc.contributor.authorGeveci, Berken_US
dc.contributor.authorAyachit, Utkarshen_US
dc.contributor.authorAhrens, Jamesen_US
dc.contributor.editorFrey, Steffen and Huang, Jian and Sadlo, Filipen_US
dc.description.abstractFeature-driven in situ data reduction can overcome the I/O bottleneck that large simulations face in modern supercomputer architectures in a semantically meaningful way. In this work, we make use of pattern detection as a black box detector of arbitrary feature templates of interest. In particular, we use moment invariants because they allow pattern detection independent of the specific orientation of a feature. We provide two open source implementations of a rotation invariant pattern detection algorithm for high performance computing (HPC) clusters with a distributed memory environment. The first one is a straightforward integration approach. The second one makes use of the Fourier transform and the Cross-Correlation Theorem. In this paper, we will compare the two approaches with respect to performance and flexibility and showcase results of the in situ integration with real world simulation code.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.subjectHuman centered computing
dc.subjectTheory of computation
dc.subjectParallel algorithms
dc.subjectPattern matching
dc.titleApproaches for In Situ Computation of Moments in a Data-Parallel Environmenten_US
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualization

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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License