Parallelizing Maximal Clique Enumeration on Modern Manycore Processors

Blanuša, Jovan; Stoica, Radu; Ienne, Paolo; Atasu, Kubilay

doi:10.1109/ipdpsw50202.2020.00047

Cited by 2 publications

(1 citation statement)

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“…The authors report that KEC outperforms existing state-of-the-art algorithms on many-core architectures, achieving significant speedup over the CPU-based implementations. Blanuša et al [23] presents a parallelization approach for maximal clique enumeration on modern manycore processors. The authors use the Bron-Kerbosch algorithm, which is a popular algorithm for finding all maximal cliques in an undirected graph.…”

Section: Related Workmentioning

confidence: 99%

Distributed Memory Implementation of Bron-Kerbosch Algorithm

Rote,

Krishnamoorthy,

Bhatia

et al. 2024

IEEE Access

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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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Section: Related Workmentioning

confidence: 99%