Adaptive, Multiresolution Visualization of Large Data Sets using a Distributed Memory Octree

Abstract

The interactive visualization and exploration of large scientific data sets is a challenging and difficult task; their size often far exceeds the performance and memory capacity of even the most powerful graphics workstations. To address this problem, we have created a technique that combines hierarchical data reduction methods with parallel computing to allow interactive exploration of large data sets while retaining full-resolution capability. The user may interactively change the resolution of the reduced data set either globally or by specifying a region of interest. In this way, high resolution can be obtained in local subregions without sacrificing graphics performance. We describe the software architecture of the system, give details pertaining to the use of a distributed memory octree used to create the reduced data set, and present performance results for the visualization of Rayleigh-Taylor instability and x-ray burst simulation data sets.