Approaches for In Situ Computation of Moments in a Data-Parallel Environment

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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Feature-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.
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@inproceedings{
10.2312:pgv.20201075
, booktitle = {
Eurographics Symposium on Parallel Graphics and Visualization
}, editor = {
Frey, Steffen and Huang, Jian and Sadlo, Filip
}, title = {{
Approaches for In Situ Computation of Moments in a Data-Parallel Environment
}}, author = {
Tsai, Karen C.
and
Bujack, Roxana
and
Geveci, Berk
and
Ayachit, Utkarsh
and
Ahrens, James
}, year = {
2020
}, publisher = {
The Eurographics Association
}, ISSN = {
1727-348X
}, ISBN = {
978-3-03868-107-6
}, DOI = {
10.2312/pgv.20201075
} }
Citation