Hierarchical Visualization and Compression of Large Volume Datasets Using GPU Clusters

Magnus Strengert, Marcelo Magallon, Daniel Weiskopf, Stefan Guthe, Thomas Ertl
Institute of Visualization and Interactive Systems, University of Stuttgart, Germany
Eurographics Symposium on Parallel Graphics and Visualization (2004)


   title={Hierarchical visualization and compression of large volume datasets using GPU clusters},

   author={Strengert, M. and Magall{‘o}n, M. and Weiskopf, D. and Guthe, S. and Ertl, T.},

   journal={Parallel Graphics and Visualization},




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We describe a system for the texture-based direct volume visualization of large data sets on a PC cluster equipped with GPUs. The data is partitioned into volume bricks in object space, and the intermediate images are combined to a final picture in a sort-last approach. Hierarchical wavelet compression is applied to increase the effective size of volumes that can be handled. An adaptive rendering mechanism takes into account the viewing parameters and properties of the data set to adjust the texture resolution and number of slices. We discuss the specific issues of this adaptive and hierarchical approach in the context of a distributed memory architecture and present solutions for these problems. Furthermore, our compositing scheme takes into account the footprints of volume bricks to minimize the costs for reading from framebuffer, network communication, and blending. A detailed performance analysis is provided and scaling characteristics of the parallel system are discussed. For example, our tests on a 16-node PC cluster show a rendering speed of 5 frames per second for a 204810241878 data set on a 1024 viewport.
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