Large volume visualization of compressed time-dependent datasets on GPU clusters

Abstract

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 the 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 corresponding solutions. 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 for several network, CPU, and GPU architectures—and scaling characteristics of the parallel system are discussed. For example, our tests on a eight-node AMD64 cluster with InfiniBand show a rendering speed of 6 frames per second for a 2048 × 1024 × 1878 data set on a 1024 2 viewport.