C3P Proceedings of the third conference on Hypercube concurrent computers and applications: Architecture, software, computer systems, and general issues - Volume 1
Competitive learning algorithms for vector quantization
Neural Networks
Parallel processing in industrial real-time applications
Parallel processing in industrial real-time applications
Comparison of parallel and serial implementation of feedforward neural networks
Journal of Microcomputer Applications
Performance prediction of large MIMD systems for parallel neural network simulations
Future Generation Computer Systems - Special issue: massive parallel computing
A Massively Parallel Implementation of the Full Search Vector Quantization Algorithm
HPCN Europe 1994 Proceedings of the nternational Conference and Exhibition on High-Performance Computing and Networking Volume I: Applications
IEEE Transactions on Image Processing
A Low-Cost Parallel K-Means VQ Algorithm Using Cluster Computing
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Efficient parallel processing of competitive learning algorithms
Parallel Computing
The Journal of Supercomputing
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
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Vector quantization (VQ) is a widely used algorithm in speech and image data compression. One of the problems of the VQ methodology is that it requires large computation time especially for large codebook size. This paper addresses two issues. The first deals with the parallel construction of the VQ codebook which can drastically reduce the training time. A master/worker parallel implementation of a VQ algorithm is proposed. The algorithm is executed on the DM-MIMD Alex AVX-2 machine using a pipeline architecture. The second issue deals with the ability of accurately predicting the machine performance. Using communication and computation models, a comparison between expected and real performance is carried out. Results show that the two models can accurately predict the performance of the machine for image data compression. Analysis of metrics normally used in parallel realization is conducted.