Engineering Massively Parallel MST Algorithms

We develop and extensively evaluate highly scalable distributed-memory algorithms for computing minimum spanning trees (MSTs). At the heart of our solutions is a scalable variant of Borůvka's algorithm. For partitioned graphs with many local edges we improve this with an effective form of contr...

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Bibliographic Details
Published in:Proceedings - IEEE International Parallel and Distributed Processing Symposium pp. 691 - 701
Main Authors: Sanders, Peter, Schimek, Matthias
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2023
Subjects:
distributed algorithms
Distributed processing
Filtering
Filtering algorithms
graph algorithms
Heart
minimum spanning tree
MPI
Partitioning algorithms
ISSN:1530-2075
Online Access:Get full text
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Summary:We develop and extensively evaluate highly scalable distributed-memory algorithms for computing minimum spanning trees (MSTs). At the heart of our solutions is a scalable variant of Borůvka's algorithm. For partitioned graphs with many local edges we improve this with an effective form of contracting local parts of the graph during a preprocessing step. We also adapt the filtering concept of the best practical sequential algorithm to develop a massively parallel Filter-Borůvka algorithm that is very useful for graphs with poor locality and high average degree. Our experiments indicate that our algorithms scale well up to at least 65 536 cores and are up to 800 times faster than previous distributed MST algorithms.
ISSN:1530-2075
DOI:10.1109/IPDPS54959.2023.00075