Knowledge-based out-of-core algorithms for data management in visualization

  • Authors:

  • David Chisnall;Min Chen;Charles Hansen

  • Affiliations:

  • Department of Computer Science, University of Wales Swansea, UK;Department of Computer Science, University of Wales Swansea, UK;School of Computing, University of Utah

  • Venue:
  • EUROVIS'06 Proceedings of the Eighth Joint Eurographics / IEEE VGTC conference on Visualization
  • Year:
  • 2006

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Abstract

Data management is the very first issue in handling very large datasets. Many existing out-of-core algorithms used in visualization are closely coupled with application-specific logic. This paper presents two knowledgebased out-of-core prefetching algorithms that do not use hard-coded rendering-related logic. They acquire the knowledge of the access history and patterns dynamically, and adapt their prefetching strategies accordingly. We have compared the algorithms with a demand-based algorithm, as well as a more domain-specific out-of-core algorithm. We carried out our evaluation in conjunction with an example application where rendering multiple point sets in a volume scene graph put a great strain on the rendering algorithm in terms of memory management. Our results have shown that the knowledge-based approach offers a better cache-hit to disk-access trade-off. This work demonstrates that it is possible to build an out-of-core prefetching algorithm without depending on rendering-related application-specific logic. The knowledge based approach has the advantage of being generic, efficient, flexible and self-adaptive.