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Distributed Percolation Analysis for Turbulent Flows
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0002-5469-1324
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0002-3234-9368
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.ORCID iD: 0000-0001-6570-5499
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2019 (English)In: 2019 IEEE 9th Symposium on Large Data Analysis and Visualization, LDAV 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 42-51, article id 8944383Conference paper, Published paper (Refereed)
Abstract [en]

Percolation analysis is a valuable tool to study the statistical properties of turbulent flows. It is based on computing the percolation function for a derived scalar field, thereby quantifying the relative volume of the largest connected component in a superlevel set for a decreasing threshold. We propose a novel memory-distributed parallel algorithm to finely sample the percolation function. It is based on a parallel version of the union-find algorithm interleaved with a global synchronization step for each threshold sample. The efficiency of this algorithm stems from the fact that operations in-between threshold samples can be freely reordered, are mostly local and thus require no inter-process communication. Our algorithm is significantly faster than previous algorithms for this purpose, and is neither constrained by memory size nor number of compute nodes compared to the conceptually related algorithm for extracting augmented merge trees. This makes percolation analysis much more accessible in a large range of scenarios. We explore the scaling of our algorithm for different data sizes, number of samples and number of MPI processes. We demonstrate the utility of percolation analysis using large turbulent flow data sets.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 42-51, article id 8944383
Keywords [en]
Computing methodologies, Discrete mathematics, Distributed algorithms, Distributed computing methodologies, Graph theory, Mathematics of computing, Paths and connectivity problems
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-267897DOI: 10.1109/LDAV48142.2019.8944383ISI: 000525833000006Scopus ID: 2-s2.0-85078123360ISBN: 9781728126050 (print)OAI: oai:DiVA.org:kth-267897DiVA, id: diva2:1410816
Conference
9th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2019; Vancouver; Canada; 21 October 2019 through
Note

QC 20200302

Available from: 2020-03-02 Created: 2020-03-02 Last updated: 2020-06-03Bibliographically approved

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Friederici, AnkeKöpp, WiebkeAtzori, MarcoVinuesa, RicardoSchlatter, PhilippWeinkauf, Tino

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Friederici, AnkeKöpp, WiebkeAtzori, MarcoVinuesa, RicardoSchlatter, PhilippWeinkauf, Tino
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