Massively parallel KD-tree construction and nearest neighbor search algorithms
Department of Computer Science; Center for Scalable Architectures and Systems
This paper presents parallel algorithms for the construction of k dimensional tree (KD-tree) and nearest neighbor search (NNS) on massively parallel architecture (MPA) of graphics processing unit (GPU). Unlike previous parallel algorithms for KD-tree, for the first time, our parallel algorithms integrate high dimensional KD-tree construction and NNS on an MPA platform. The proposed massively parallel algorithms are of comparable quality as traditional sequential counterparts on CPU, while achieve high speedup performance on both low and high dimensional KD-tree. Low dimensional KD-tree construction and NNS algorithms, presented in this paper, outperform their serial CPU counterparts by a factor of up to 24 and 218, respectively. For high dimensional KD-tree, the speedup factors are even higher, raising to 30 and 242, respectively. Our implementations will potentially benefit real time three-dimensional (3D) image registration and high dimensional descriptor matching.
2015 IEEE International Symposium on Circuits and Systems
Massively parallel KD-tree construction and nearest neighbor search algorithms.
2015 IEEE International Symposium on Circuits and Systems.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1165