12590
John Ashley, Amy J. Braverman
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 […]
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Rita Silva, Telmo Marques, Jorge Desirat, Patricio Domingues
Many-Core computing is an actual growing concept that allows the true parallelization of computational tasks. In the particular case of this paper, the vector quantization algorithm was adapted to the many-core concept with the objective of compressing images encoded in the PGM format. For that, a given sequential implementation of the algorithm was optimized and […]
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Roland Fraedrich, Jens Schneider, Rudiger Westermann
In this paper we investigate scalability limitations in the visualization of large-scale particle-based cosmological simulations, and we present methods to reduce these limitations on current PC architectures. To minimize the amount of data to be streamed from disk to the graphics subsystem, we propose a visually continuous level-of-detail (LOD) particle representation based on a hierarchical […]
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Yi Xiao, Chi Leung, Tze-Yui Ho, Ping-Man Lam
Vector quantization (VQ) is an effective technique applicable in a wide range of areas, such as image compression and pattern recognition. The most time-consuming procedure of VQ is codebook training, and two of the frequently used training algorithms are LBG and self-organizing map (SOM). Nowadays, desktop computers are usually equipped with programmable graphics processing units […]

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