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CUDA Accelerated Entropy Constrained Vector Quantization and Multiple K-Means

Authors:
John Ashley
Amy Braverman

Keywords: K-Means; Entropy Constrained Vector Quantization; Graphical Processing Unit; CUDA

Abstract:
Multi-trial sampled K-means performance and scalability is studied as a stepping stone towards a Graphical Processing Unit implementation of Entropy Constrained Vector Quantization for interactive data compression. Basic parallelization strategies and data layout impacts are explored with K-means. The K-means implementation is extended to Entropy Constrained Vector Quantization, and additional tuning specific to the anticipated use case is performed. The results obtained are sufficiently promising that this will in the next phase be applied to the interactive exploration and visualization of very large satellite datasets.

Pages: 30 to 34

Copyright: Copyright (c) IARIA, 2014

Publication date: July 20, 2014

Published in: conference

ISSN: 2308-3484

ISBN: 978-1-61208-365-0

Location: Paris, France

Dates: from July 20, 2014 to July 24, 2014