research-article

MASTIFF: structure-aware minimum spanning tree/forest

Authors:

Mohsen Koohi Esfahani,

Peter Kilpatrick,

Hans VandierendonckAuthors Info & Claims

ICS '22: Proceedings of the 36th ACM International Conference on Supercomputing

Article No.: 9, Pages 1 - 13

https://doi.org/10.1145/3524059.3532365

Published: 28 June 2022 Publication History

Abstract

The Minimum Spanning Forest (MSF) problem finds usage in many different applications. While theoretical analysis shows that linear-time solutions exist, in practice, parallel MSF algorithms remain computationally demanding due to the continuously increasing size of data sets.

In this paper, we study the MSF algorithm from the perspective of graph structure and investigate the implications of the power-law degree distribution of real-world graphs on this algorithm.

We introduce the MASTIFF algorithm as a structure-aware MSF algorithm that optimizes work efficiency by (1) dynamically tracking the largest forest component of each graph component and exempting them from processing, and (2) by avoiding topology-related operations such as relabeling and merging neighbour lists.

The evaluations on 2 different processor architectures with up to 128 cores and on graphs of up to 124 billion edges, shows that Mastiff is 3.4--5.9X faster than previous works.

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Cited By

Sanders PSchimek M(2023)Engineering Massively Parallel MST Algorithms2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS54959.2023.00075(691-701)Online publication date: May-2023
https://doi.org/10.1109/IPDPS54959.2023.00075
Koohi Esfahani MBoldi PVandierendonck HKilpatrick PVigna S(2023)On Overcoming HPC Challenges of Trillion-Scale Real-World Graph Datasets2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386309(215-220)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386309

Index Terms

MASTIFF: structure-aware minimum spanning tree/forest
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel algorithms
      1. Massively parallel algorithms
      2. Shared memory algorithms
2. General and reference
  1. Cross-computing tools and techniques
    1. Performance

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cover image ACM Conferences

ICS '22: Proceedings of the 36th ACM International Conference on Supercomputing

June 2022

514 pages

ISBN:9781450392815

DOI:10.1145/3524059

General Chairs:
Lawrence Rauchwerger
University of Illinois at Urbana-Champaign
,
Kirk Cameron
Virginia Tech
,
Program Chairs:
Dimitrios S. Nikolopoulos
Virginia Tech
,
Dionisios Pnevmatikatos
National Technical University of Athens

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Published: 28 June 2022

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DiPET (CHIST-ERA project)
High Performance Computing center of Queen's University Belfast and the Kelvin-2 supercomputer
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ICS '22: 2022 International Conference on Supercomputing

June 28 - 30, 2022

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Cited By

Sanders PSchimek M(2023)Engineering Massively Parallel MST Algorithms2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS54959.2023.00075(691-701)Online publication date: May-2023
https://doi.org/10.1109/IPDPS54959.2023.00075
Koohi Esfahani MBoldi PVandierendonck HKilpatrick PVigna S(2023)On Overcoming HPC Challenges of Trillion-Scale Real-World Graph Datasets2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386309(215-220)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386309

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