Time-varying multivariate volume data reduction

Nathaniel Fout, Kwan-Liu Ma, James Ahrens

Research output: Contribution to conferencePaperpeer-review

19 Scopus citations


Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by largescale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes 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.

Original languageEnglish (US)
Number of pages7
StatePublished - Dec 1 2005
Event20th Annual ACM Symposium on Applied Computing - Santa Fe, NM, United States
Duration: Mar 13 2005Mar 17 2005


Other20th Annual ACM Symposium on Applied Computing
Country/TerritoryUnited States
CitySanta Fe, NM


  • Compression
  • Hardware acceleration
  • Interactive visualization
  • Multivariate data
  • Time-varying data
  • Vector quantization
  • Volume rendering

ASJC Scopus subject areas

  • Software


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