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Distributed merge forest: a new fast and scalable approach for topological analysis at scale

Authors:

Pavol Klacansky,

Steve Petruzza,

Attila Gyulassy,

Peer-Timo Bremer,

Valerio PascucciAuthors Info & Claims

ICS '21: Proceedings of the 35th ACM International Conference on Supercomputing

Pages 367 - 377

https://doi.org/10.1145/3447818.3460358

Published: 04 June 2021 Publication History

Abstract

Topological analysis is used in several domains to identify and characterize important features in scientific data, and is now one of the established classes of techniques of proven practical use in scientific computing. The growth in parallelism and problem size tackled by modern simulations poses a particular challenge for these approaches. Fundamentally, the global encoding of topological features necessitates interprocess communication that limits their scaling. In this paper, we extend a new topological paradigm to the case of distributed computing, where the construction of a global merge tree is replaced by a distributed data structure, the merge forest, trading slower individual queries on the structure for faster end-to-end performance and scaling. Empirically, the queries that are most negatively affected also tend to have limited practical use. Our experimental results demonstrate the scalability of both the merge forest construction and the parallel queries needed in scientific workflows, and contrast this scalability with the two established alternatives that construct variations of a global tree.

References

[1]

2020. Reeber. https://github.com/mrzv/reeber.

[2]

Aditya Acharya and Vijay Natarajan. 2015. A parallel and memory efficient algorithm for constructing the contour tree. In 2015 IEEE Pacific Visualization Symposium (PacificVis). 271--278. 2165-8765

[3]

Ann S. Almgren, John B. Bell, Mike J. Lijewski, Zarija Lukić, and Ethan Van Andel. 2013. Nyx: A MASSIVELY PARALLEL AMR CODE FOR COMPUTATIONAL COSMOLOGY. The Astrophysical Journal 765, 1 (feb 2013), 39.

[4]

Ray Asbury and M Wrinn. 2004. MPI tuning with Intel/spl copy/Trace Analyzer and Intel/spl copy/Trace Collector. In 2004 IEEE International Conference on Cluster Computing (IEEE Cat. No. 04EX935). IEEE, 4.

Digital Library

[5]

Peer-Timo Bremer, Andrea Gruber, Janine Bennett, Attila Gyulassy, Hemanth Kolla, Jacqueline Chen, and Ray Grout. 2016. Identifying turbulent structures through topological segmentation. Commun. Appl. Math. Comput. Sci. 11, 1 (2016), 37--53.

[6]

Hamish Carr, Jack Snoeyink, and Ulrike Axen. 2003. Computing contour trees in all dimensions. Computational Geometry 24, 2 (2003), 75--94.

Digital Library

[7]

Anke Friederici, Wiebke Köpp, Marco Atzori, Ricardo Vinuesa, Philipp Schlatter, and Tino Weinkauf. 2019. Distributed percolation analysis for turbulent flows. In 2019 IEEE 9th Symposium on Large Data Analysis and Visualization (LDAV). 42--51.

[8]

Charles Gueunet, Pierre Fortin, Julien Jomier, and Julien Tierny. 2016. Contour forests: Fast multi-threaded augmented contour trees. In 2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV). 85--92.

[9]

Pavol Klacansky, Attila Gyulassy, Peer-Timo Bremer, and Valerio Pascucci. 2019. Toward localized topological data structures: Querying the forest for the tree. IEEE Transactions on Visualization and Computer Graphics 26, 1 (2019), 173--183.

[10]

Wiebke Köpp, Anke Friederici, Marco Atzori, Ricardo Vinuesa, Philipp Schlatter, and Tino Weinkauf. 2019. Notes on percolation analysis of sampled scalar fields. In Topology-Based Methods in Visualization (TopoInVis). Nyköping, Sweden.

[11]

Aaditya G Landge, Valerio Pascucci, Attila Gyulassy, Janine C Bennett, Hemanth Kolla, Jacqueline Chen, and Peer-Timo Bremer. 2014. In-situ feature extraction of large scale combustion simulations using segmented merge trees. In SC'14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 1020--1031.

Digital Library

[12]

Myoungkyu Lee and Robert D. Moser. 2015. Direct numerical simulation of turbulent channel flow up to Re_τ≈5200. Journal of Fluid Mechanics 774 (July 2015), 395--415.

[13]

Zarija Lukic. 2019. Nyx cosmological simulation data.

[14]

Senthilnathan Maadasamy, Harish Doraiswamy, and Vijay Natarajan. 2012. A hybrid parallel algorithm for computing and tracking level set topology. In 2012 19th International Conference on High Performance Computing. 1--10.

[15]

A Mascarenhas, RW Grout, Chun Sang Yoo, and JH Chen. 2009. Tracking flame base movement and interaction with ignition kernels using topological methods. In Journal of Physics: Conference Series, Vol. 180. IOP Publishing, 012086.

[16]

Dmitriy Morozov and Gunther Weber. 2013. Distributed merge trees. In Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming. 93--102.

Digital Library

[17]

Dmitriy Morozov and Gunther H. Weber. 2014. Distributed contour trees. In Topological Methods in Data Analysis and Visualization III. Springer, 89--102.

[18]

Arnur Nigmetov and Dmitriy Morozov. 2019. Local-global merge tree computation with local exchanges. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 1--13.

Digital Library

[19]

Valerio Pascucci and Kree Cole-McLaughlin. 2004. Parallel computation of the topology of level sets. Algorithmica 38, 1 (2004), 249--268.

Digital Library

[20]

Steve Petruzza, Sean Treichler, Valerio Pascucci, and Peer-Timo Bremer. 2018. Babelflow: An embedded domain specific language for parallel analysis and visualization. In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 463--473.

[21]

Wathsala Widanagamaachchi, Alexander Jacques, Bei Wang, Erik Crosman, Peer-Timo Bremer, Valerio Pascucci, and John Horel. 2017. Exploring the evolution of pressure-perturbations to understand atmospheric phenomena. In 2017 IEEE Pacific Visualization Symposium (PacificVis). 101--110. 2165-8773

Cited By

Le Guillou EWill MGuillou PLukasczyk JFortin PGarth CTierny J(2024)TTK is Getting MPI-ReadyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.339021930:8(5875-5892)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2024.3390219
Liu GIuricich F(2023)A Task-Parallel Approach for Localized Topological Data StructuresIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332718230:1(1271-1281)Online publication date: 31-Oct-2023
https://dl.acm.org/doi/10.1109/TVCG.2023.3327182
Klacansky PGyulassy ABremer PPascucci V(2022)A Study of the Locality of Persistence-Based Queries and Its Implications for the Efficiency of Localized Data Structures2022 IEEE 15th Pacific Visualization Symposium (PacificVis)10.1109/PacificVis53943.2022.00021(121-130)Online publication date: Apr-2022
https://doi.org/10.1109/PacificVis53943.2022.00021

Index Terms

Distributed merge forest: a new fast and scalable approach for topological analysis at scale
1. Computing methodologies
  1. Distributed computing methodologies
    1. Distributed algorithms
2. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Scientific visualization

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cover image ACM Conferences

ICS '21: Proceedings of the 35th ACM International Conference on Supercomputing

June 2021

506 pages

ISBN:9781450383356

DOI:10.1145/3447818

General Chairs:
Huiyang Zhou
North Carolina State University
,
Jose Moreira
IBM Research
,
Program Chairs:
Frank Mueller
North Carolina State University
,
Yoav Etsion
Technion

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 04 June 2021

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ICS '21: 2021 International Conference on Supercomputing

June 14 - 17, 2021

Virtual Event, USA

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ICS '21 Paper Acceptance Rate 39 of 157 submissions, 25%;

Overall Acceptance Rate 629 of 2,180 submissions, 29%

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Cited By

Le Guillou EWill MGuillou PLukasczyk JFortin PGarth CTierny J(2024)TTK is Getting MPI-ReadyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.339021930:8(5875-5892)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2024.3390219
Liu GIuricich F(2023)A Task-Parallel Approach for Localized Topological Data StructuresIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332718230:1(1271-1281)Online publication date: 31-Oct-2023
https://dl.acm.org/doi/10.1109/TVCG.2023.3327182
Klacansky PGyulassy ABremer PPascucci V(2022)A Study of the Locality of Persistence-Based Queries and Its Implications for the Efficiency of Localized Data Structures2022 IEEE 15th Pacific Visualization Symposium (PacificVis)10.1109/PacificVis53943.2022.00021(121-130)Online publication date: Apr-2022
https://doi.org/10.1109/PacificVis53943.2022.00021

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