Adaptive, multiresolution visualization of large data sets using a distributed memory octree
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Rendering the first star in the universe: a case study
Proceedings of the conference on Visualization '02
Visualizing Very Large-Scale Earthquake Simulations
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Parallel Cell Projection Rendering of Adaptive Mesh Refinement Data
PVG '03 Proceedings of the 2003 IEEE Symposium on Parallel and Large-Data Visualization and Graphics
The Feature Tree: Visualizing Feature Tracking in Distributed AMR Datasets
PVG '03 Proceedings of the 2003 IEEE Symposium on Parallel and Large-Data Visualization and Graphics
Extraction of crack-free isosurfaces from adaptive mesh refinement data
EGVISSYM'01 Proceedings of the 3rd Joint Eurographics - IEEE TCVG conference on Visualization
Parallel volume rendering on the IBM Blue Gene/P
EG PGV'08 Proceedings of the 8th Eurographics conference on Parallel Graphics and Visualization
Data-intensive spatial filtering in large numerical simulation datasets
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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This paper describes work-in-progress on developing parallel visualization strategies for 3D Adaptive Mesh Refinement (AMR) data. AMR is a simple and powerful tool for modeling many important scientific and engineering problems. However, visualization tools for 3D AMR data are not generally available. Converting AMR data onto a uniform mesh would result in high storage requirements, and rendering the uniform-mesh data on an average graphics workstation can be painfully slow if not impossible. The adaptive nature of the embedded mesh demands sophisticated visualization calculations. In this work, we compare the performance and storage requirements of a parallel volume renderer for regular-mesh data with a new parallel renderer based on adaptive sampling. While both renderers can achieve interactive visualization, the new approach offers significant performance gains, as indicated by our experiments on the SGI/Cray T3E.