Vector quantization and signal compression
Vector quantization and signal compression
Self-organizing maps
OpenGL Programming Guide: The Official Guide to Learning OpenGL, Version 1.2
OpenGL Programming Guide: The Official Guide to Learning OpenGL, Version 1.2
Neural Networks
Parallel codebook design for vector quantization on a message passing MIMD architecture
Parallel Computing - Parallel computing in image and video processing
Sequential and Parallel Neural Network Vector Quantizers
IEEE Transactions on Computers
Using modern graphics architectures for general-purpose computing: a framework and analysis
Proceedings of the 35th annual ACM/IEEE international symposium on Microarchitecture
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
GPU Cluster for High Performance Computing
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Cluster Analysis
Multi-grain parallel processing of data-clustering on programmable graphics hardware
ISPA'04 Proceedings of the Second international conference on Parallel and Distributed Processing and Applications
IEEE Transactions on Image Processing
Efficient K-Means Clustering Using Accelerated Graphics Processors
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Probing biomolecular machines with graphics processors
Communications of the ACM - A View of Parallel Computing
Probing Biomolecular Machines with Graphics Processors
Queue - Bioscience
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Dense affinity propagation on clusters of GPUs
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
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This paper presents an effective scheme for clustering a huge data set using a PC cluster system, in which each PC is equipped with a commodity programmable graphics processing unit (GPU). The proposed scheme is devised to achieve three-level hierarchical parallel processing of massive data clustering. The divide-and-conquer approach to parallel data clustering is employed to perform the coarse-grain parallel processing by multiple PCs with a message passing mechanism. By taking advantage of the GPU's parallel processing capability, moreover, the proposed scheme can exploit two types of the fine-grain data parallelism at the different levels in the nearest neighbor search, which is the most computationally-intensive part of the data-clustering process. The performance of our scheme is discussed in comparison with that of the implementation entirely running on CPU. Experimental results clearly show that the proposed hierarchial parallel processing can remarkably accelerate the data clustering task. Especially, GPU co-processing is quite effective to improve the computational efficiency of parallel data clustering on a PC cluster. Although data-transfer from GPU to CPU is generally costly, acceleration by GPU co-processing is significant to save the total execution time of data-clustering.