Parallelization of Minimum Spanning Tree Algorithms Using Distributed Memory Architectures

Lončar, Vladimir; Škrbić, Srdjan; Balaž, Antun

doi:10.1007/978-94-017-8832-8_39

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…Dehne et al [23] provide several practical algorithms and an evaluation on up to 16 cores for dense graphs with m/n > p. This condition essentially ensures that the memory of each machine is large enough to hold all vertices of the graph. Loncar et al [24] propose distributed variants of the Kruskal and Jarnik-Prim algorithm that also rely on replicated vertices.…”

Section: Related Workmentioning

confidence: 99%

Engineering Massively Parallel MST Algorithms

Sanders¹,

Schimek²

2023

Preprint

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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 Bor ůvka'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-Bor ůvka 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.

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Section: Related Workmentioning

confidence: 99%

Engineering Massively Parallel MST Algorithms

Sanders¹,

Schimek²

2023

Preprint

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“…Some of the mentioned algorithms are adapted for implementation on distributed memory systems [13,14,15,16,17]. Among the parallel implementations listed above there is not one implementation scalable to at least one hundred parallel processes.…”

Section: Introductionmentioning

confidence: 99%

“…Some algorithms are suitable for shared memory parallelization, there are lot of such implementations, for example [7,8,9,10]. Some of the mentioned algorithms are adapted for implementation on distributed memory systems [13,14,15,16,17]. Among the parallel implementations listed above there is not one implementation scalable to at least one hundred parallel processes.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Parallel Algorithm for the Minimum Spanning Tree Problem

Mazeev¹,

Семенов²,

Simonov³

2017

Communications in Computer and Information Science

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In this paper we present and evaluate a parallel algorithm for solving a minimum spanning tree (MST) problem for supercomputers with distributed memory. The algorithm relies on the relaxation of the message processing order requirement for one specific message type compared to the original GHS (Gallager, Humblet, Spira) algorithm. Our algorithm adopts hashing and message compression optimization techniques as well. To the best of our knowledge, this is the first parallel implementation of the GHS algorithm that linearly scales to more than 32 nodes (256 cores) of Infiniband cluster.

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Erciyes¹

2018

Texts in Computer Science

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Parallelization of Minimum Spanning Tree Algorithms Using Distributed Memory Architectures

Cited by 8 publications

References 11 publications

Engineering Massively Parallel MST Algorithms

Engineering Massively Parallel MST Algorithms

A Distributed Parallel Algorithm for the Minimum Spanning Tree Problem

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