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. © 2012 Springer-Verlag.
CITATION STYLE
Arefin, A. S., Riveros, C., Berretta, R., & Moscato, P. (2012). kNN-Borůvka-GPU: A fast and scalable MST construction from kNN graphs on GPU. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7333 LNCS, pp. 71–86). https://doi.org/10.1007/978-3-642-31125-3_6
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