Clusteral Models for Efficient Parallel Volume Visualization

Abstract

Volume visualization is a powerful engineering tool. However, the visualization of a three dimensional volume is computationally expensive taking significant amounts of time to produce the images on conventional computers. Parallel processing offers the possibility of rendering the volume in acceptable times. This paper discusses hierarchical and distributed clusteral models with dynamic cluster re-sizing and caching which are used in combination with dynamic task and data management strategies to provide an efficient parallel implementation for volume visualization on a large distributed memory multiprocessor system.