A Parallel Algorithm for Measuring Graph Similarity Using CUDA on GPU

Son, Min-Young; Kim, Young‐Hak; Choi, Sung‐Ja

doi:10.5626/ktcp.2017.23.3.156

Cited by 2 publications

(1 citation statement)

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“…A similar approach is studied in Somkunwar and Vaze (2019) using the OpenMP. To find approaches closer to the work we developed in this study, making intensive use of different HPC techniques, multicore, multigpu, we must make a more intensive search, and then we can find works developed by Wang et al (2013) and the work of Son et al (2017), both are some of the few works that use data parallelism in the implementation of their algorithms.…”

Section: Isomorph and Graph Isomorphismmentioning

confidence: 99%

ISOMORPH: an efficient application on GPU for detecting graph isomorphism

Llanes,

Imbernón Tudela,

Curado

et al. 2023

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PurposeThe authors will review the main concepts of graphs, present the implemented algorithm, as well as explain the different techniques applied to the graph, to achieve an efficient execution of the algorithm, both in terms of the use of multiple cores that the authors have available today, and the use of massive data parallelism through the parallelization of the algorithm, bringing the graph closer to the execution through CUDA on GPUs.Design/methodology/approachIn this work, the authors approach the graphs isomorphism problem, approaching this problem from a point of view very little worked during all this time, the application of parallelism and the high-performance computing (HPC) techniques to the detection of isomorphism between graphs.FindingsResults obtained give compelling reasons to ensure that more in-depth studies on the HPC techniques should be applied in these fields, since gains of up to 722x speedup are achieved in the most favorable scenarios, maintaining an average performance speedup of 454x.Originality/valueThe paper is new and original.

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Section: Isomorph and Graph Isomorphismmentioning

confidence: 99%