kNN-Borůvka-GPU: a fast and scalable MST construction from kNN graphs on GPU

Authors:
Ahmed Shamsul Arefin;Carlos Riveros;Regina Berretta;Pablo Moscato
Affiliations:
Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, School of EECS, FEBE, The University of Newcastle, Australia;Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, School of EECS, FEBE, The University of Newcastle, Australia;Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, School of EECS, FEBE, The University of Newcastle, Australia;Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, School of EECS, FEBE, The University of Newcastle, Australia
Venue:
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part I
Year:
2012

Citing 13
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Abstract

Computation of the minimum spanning tree (MST) is a common task in numerous fields of research, such as pattern recognition, computer vision, network design (telephone, electrical, hydraulic, cable TV, computer, road networks etc.), VLSI layout, to name a few. However, for a large-scale dataset when the graphs are complete, classical MST computation algorithms become unsuitable on general purpose computers. Interestingly, in such a case the k-nearest neighbor (kNN) structure can provide an efficient solution to this problem. Only a few attempts were found in the literature that focus on solving the problem using the kNNs. This paper briefs the state-of-the-art strategies for the MST problem and a fast and scalable solution combining the classical Borůvka's MST algorithm and the kNN graph structure. The proposed algorithm is implemented for CUDA enabled GPUs kNN-Borůvka-GPU), but the basic approach is simple and adaptable to other available architectures. Speed-ups of 30-40 times compared with CPU implementation was observed for several large-scale synthetic and real world data sets.