Vector quantization and signal compression
Vector quantization and signal compression
Self-Organizing Maps
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
Cluster Analysis
IEEE Transactions on Image Processing
The Journal of Supercomputing
A code motion technique for accelerating general-purpose computation on the GPU
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
A GPGPU approach for accelerating 2-d/3-d rigid registration of medical images
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
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This paper presents an effective scheme for clustering a huge data set using a commodity programmable graphics processing unit(GPU). Due to GPU's application-specific architecture, one of the current research issues is how to bind the rendering pipeline with the data-clustering process. By taking advantage of GPU's parallel processing capability, our implementation scheme is devised to exploit the multi-grain single-instruction multiple-data (SIMD) parallelism of 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 parallelism of the nearest neighbor search allows our scheme to efficiently execute the data-clustering process. Although data-transfer from GPU to CPU is generally costly, acceleration by GPU is significant to save the total execution time of data-clustering.