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.
12th International Conference on Computational Science and Its Applications (ICCSA 2012). Computational Science and Its Applications - ICCSA 2012: 12th International Conference, Salvador de Bahia, Brazil, June 18-21, 2012, Proceedings, Part 1 (Salvador de Bahia, Brazil 18-21 June, 2012) p. 71-86