Chisnall, DavidChen, MinHansen, CharlesBeatriz Sousa Santos and Thomas Ertl and Ken Joy2014-01-312014-01-3120063-905673-31-21727-5296https://doi.org/10.2312/VisSym/EuroVis06/107-114Data 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.Categories and Subject Descriptors (according to ACM CCS): I.3.6 [Computer Graphics]: Methodology and Techniques - Graphics data structures and data types; I.3.m [Computer Graphics]: Visualization - Point-based techniques; D.4.2 [Operating Systems]: Storage Management - Allocation/deallocation strategies.