2020
Tsai, Karen; Bujack, Roxana; Geveci, Berk; Ayachit, Utkarsh; Ahrens, James
Approaches for In Situ Computation of Moments in a Data-Parallel Environment Proceedings Article
In: Frey, Steffen; Huang, Jian; Sadlo, Filip (Ed.): Eurographics Symposium on Parallel Graphics and Visualization, The Eurographics Association, 2020, ISSN: 1727-348X.
Abstract | Links | BibTeX | Tags: • Human-centered computing → Scientific visualization, parallel algorithms, pattern matching
@inproceedings{10.2312:pgv.20201075,
title = {Approaches for In Situ Computation of Moments in a Data-Parallel Environment},
author = {Karen Tsai and Roxana Bujack and Berk Geveci and Utkarsh Ayachit and James Ahrens},
editor = {Steffen Frey and Jian Huang and Filip Sadlo},
url = {(http://www.google.com/url?q=http%3A%2F%2Fwww.informatik.uni-leipzig.de%2F~bujack%2F2020EGPGV.pdf&sa=D&sntz=1&usg=AOvVaw2HaZNoL1L9jYRO0br69mHW)},
doi = {10.2312/pgv.20201075},
issn = {1727-348X},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
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.},
keywords = {• Human-centered computing → Scientific visualization, parallel algorithms, pattern matching},
pubstate = {published},
tppubtype = {inproceedings}
}
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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1.
Tsai, Karen; Bujack, Roxana; Geveci, Berk; Ayachit, Utkarsh; Ahrens, James
Approaches for In Situ Computation of Moments in a Data-Parallel Environment Proceedings Article
In: Frey, Steffen; Huang, Jian; Sadlo, Filip (Ed.): Eurographics Symposium on Parallel Graphics and Visualization, The Eurographics Association, 2020, ISSN: 1727-348X.
@inproceedings{10.2312:pgv.20201075,
title = {Approaches for In Situ Computation of Moments in a Data-Parallel Environment},
author = {Karen Tsai and Roxana Bujack and Berk Geveci and Utkarsh Ayachit and James Ahrens},
editor = {Steffen Frey and Jian Huang and Filip Sadlo},
url = {(http://www.google.com/url?q=http%3A%2F%2Fwww.informatik.uni-leipzig.de%2F~bujack%2F2020EGPGV.pdf&sa=D&sntz=1&usg=AOvVaw2HaZNoL1L9jYRO0br69mHW)},
doi = {10.2312/pgv.20201075},
issn = {1727-348X},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.