2010
Ahrens, James; Fout, Nathaniel; Ma, Kwan-Liu
Time varying, multivariate volume data reduction Proceedings Article
In: 2010, (LA-UR-10-02243).
Abstract | Links | BibTeX | Tags: data reduction, multivariate, time-varying
@inproceedings{Ahrens2010,
title = {Time varying, multivariate volume data reduction},
author = {James Ahrens and Nathaniel Fout and Kwan-Liu Ma},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/Time-VaryingMltivariateVolumeDataReduction.pdf},
year = {2010},
date = {2010-01-01},
institution = {Los Alamos National Laboratory (LANL)},
abstract = {Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multi-variate, and volumetric, can occupy from hundreds of giga-bytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering.},
note = {LA-UR-10-02243},
keywords = {data reduction, multivariate, time-varying},
pubstate = {published},
tppubtype = {inproceedings}
}
Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multi-variate, and volumetric, can occupy from hundreds of giga-bytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering.
: . .
1.
Ahrens, James; Fout, Nathaniel; Ma, Kwan-Liu
Time varying, multivariate volume data reduction Proceedings Article
In: 2010, (LA-UR-10-02243).
@inproceedings{Ahrens2010,
title = {Time varying, multivariate volume data reduction},
author = {James Ahrens and Nathaniel Fout and Kwan-Liu Ma},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/Time-VaryingMltivariateVolumeDataReduction.pdf},
year = {2010},
date = {2010-01-01},
institution = {Los Alamos National Laboratory (LANL)},
abstract = {Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multi-variate, and volumetric, can occupy from hundreds of giga-bytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering.},
note = {LA-UR-10-02243},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multi-variate, and volumetric, can occupy from hundreds of giga-bytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering.