A Novel Information-Aware Octree for the Visualization of Large Scale Time-Varying Data

Abstract

Large scale scientific simulations are increasingly generating very large data sets that present substantial challenges to current visualization systems. In this paper, we develop a new scalable and efficient scheme for the visual exploration of 4-D isosurfaces of time varying data by rendering the 3-D isosurfaces obtained through an arbitrary axis-parallel hyperplane cut. The new scheme is based on: (i) a new 4-D hierarchical indexing structure, called Information-Aware Octree; (ii) a controllable delayed fetching technique; and (iii) an optimized data layout. Together, these techniques enable efficient and scalable out-of-core visualization of large scale time varying data sets. We introduce an entropy-based dimension integration technique by which the relative resolutions of the spatial and temporal dimensions are established, and use this information to design a compact size 4-D hierarchical indexing structure. We also present scalable and efficient techniques for out-of-core rendering. Compared with previous algorithms for constructing 4-D isosurfaces, our scheme is substantially faster and requires much less memory. Compared to the Temporal Branch-On-Need octree (T-BON), which can only handle a subset of our queries, our indexing structure is an order of magnitude smaller and is at least as effective in dealing with the queries that the T-BON can handle. We have tested our scheme on two large time-varying data sets and obtained very good performance for a wide range of isosurface extraction queries using an order of magnitude smaller indexing structures than previous techniques. In particular, we can generate isosurfaces at intermediate time steps very quickly.