OpenGL(R) Shading Language (2nd Edition)
OpenGL(R) Shading Language (2nd Edition)
The Journal of Supercomputing
Hierarchical clustering of gene expression profiles with graphics hardware acceleration
Pattern Recognition Letters
ACM SIGGRAPH 2006 Research posters
Scalable clustering using graphics processors
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
GPU-WAH: applying GPUs to compressing bitmap indexes with word aligned hybrid
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Mapping data mining algorithms on a GPU architecture: a study
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Dense affinity propagation on clusters of GPUs
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Fast organization of large photo collections using CUDA
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
Iterative statistical kernels on contemporary GPUs
International Journal of Computational Science and Engineering
International Journal of Reconfigurable Computing - Special issue on Selected Papers from the 2011 International Conference on Reconfigurable Computing and FPGAs (ReConFig 2011)
Technical Section: A GPU-assisted hybrid model for real-time crowd simulations
Computers and Graphics
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We exploit the parallel architecture of the Graphics Processing Unit (GPU) used in desktops to efficiently implement the traditional K-means algorithm. Our approach in clustering avoids the need for data and cluster information transfer between the GPU and CPU in between the iterations. In this paper we present the novelties in our approach and techniques employed to represent data, compute distances, centroids and identify the cluster elements using the GPU. We measure performance using the metric: computational time per iteration. Our implementation of k-means clustering on an Nvidia 5900 graphics processor is 4 to 12 times faster than the CPU and 7 to 22 times faster on the Nvidia 8500 graphics processor for various data sizes. We also achieved 12 to 64 times speed gain on the 5900 and 20 to 140 times speed gains on the 8500 graphics processor in computational time per iteration for evaluations with various cluster sizes.