Knowledge-Based Out-of-Core Algorithms for Data Management in Visualization

Chisnall, David; Chen, Min; Hansen, Charles

Knowledge-Based Out-of-Core Algorithms for Data Management in Visualization

Files

107-114.pdf (377.14 KB)

Date

2006

Authors

Chisnall, David
Chen, Min
Hansen, Charles

Publisher

The Eurographics Association

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.

        @inproceedings{:10.2312/VisSym/EuroVis06/107-114
,
booktitle = {EUROVIS - Eurographics /IEEE VGTC Symposium on Visualization
},
editor = {Beatriz Sousa Santos and Thomas Ertl and Ken Joy
},
title = {{Knowledge-Based Out-of-Core Algorithms for Data Management in Visualization
}},
author = {Chisnall, David and 
Chen, Min and 
Hansen, Charles
},
year = {2006
},
publisher = {The Eurographics Association
},
ISSN = {1727-5296
},
ISBN = {3-905673-31-2
},
DOI = {/10.2312/VisSym/EuroVis06/107-114
}
}