Hierarchic Visualization And Parallel Computation

We are developing and implementing techniques for hierarchic visualization of very large scale data sets that may arise as a result of computer simulations of engineering applications or from experimental measurements. The basic approach is motivated by the ideas used in adaptive mesh refinement and coarsening strategies, multigrid solution schemes and wavelet multiresolution techniques. Of particular interest are large scale simulations on parallel supercomputers. The work exploits tree-traversal algorithms and data structures for adaptive refinement/coarsening to enable selective hierarchic extraction and compression of solution data for visualization locally or at a remote site. Error or feature indicators provide a theoretical framework to guide adaptive mesh schemes and provide a mechanism for selectively screening the simulation results. These indicators can also be used to construct so-called "intelligent agents" to help direct the visualization. A prototype "data handler" that incorporates some of these primitives is under development and testing. Representative examples involving adaptively refined triangular meshes in 2D and meshes of "quadrilateral brick" elements in 3D are investigated. These illustrate, for instance, how adaptive refinement may generate a nonuniform quadtree or octree based on an analysis error indicator and then how selective hierarchic visualization using a different feature indicator can be applied on the same tree. Supporting timing studies for remote visualization of hierarchic data using nested meshes or multiresolution approaches are also presented. The extension of these schemes to parallel distributed data sets using mesh partitioning strategies is also considered.