Distributed memory implementation of Bron-Kerbosch algorithm

This paper proposes a parallel implementation of the Bron-Kerbosch algorithm, which finds all maximal cliques in large, complex graphs using CPU and thread-level parallelism and distributed memory with multiple cores. With the growing size and complexity of modern graphs, sequential algorithms can b...

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Bibliographic Details
Published in:IEEE access Vol. 12; p. 1
Main Authors: Rote, Tejas Ravindra, Krishnamoorthy, Murugan, Bhatia, Ansh, Yadav, Rishu, Raja, S. P.
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Bio-informatics
Bioinformatics
Bron-Kerbosch algorithm
Central processing units
Complexity
Complexity theory
Compute nodes
Compute threads
CPUs
Distributed management
Distributed memory
Graph theory
Graphs
High performance computing
Instruction sets
Machine learning algorithms
Maximal cliques
Memory
Network analysis
Network security
Parallel computing
Parallel processing
Performance optimization
Proteins
Scalability
Social network analysis
Social networking (online)
Social networks
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:This paper proposes a parallel implementation of the Bron-Kerbosch algorithm, which finds all maximal cliques in large, complex graphs using CPU and thread-level parallelism and distributed memory with multiple cores. With the growing size and complexity of modern graphs, sequential algorithms can become impractical, making parallel computation a promising solution for reducing duration and enhancing scalability. Decomposing the graph into smaller components and distributing the workload across multiple threads using task parallelism is the proposed method. To optimise the algorithm's efficacy across processes, load-balancing techniques are also investigated. The modified Bron-Kerbosch algorithm is implemented and evaluated on a variety of large graphs, exhibiting significant runtime and scalability enhancements over the sequential version. The conclusion of the paper discusses the prospective applications of the parallel Bron-Kerbosch algorithm in a variety of domains, including social network analysis, bio informatics, and network security. Overall, this research contributes to the expanding body of work on parallel computing and graph analysis by emphasising the advantages of CPU and thread-level parallelism for efficiently solving complex computational problems.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3393771