Contour Tree Depth Images For Large Data Visualization

Abstract

High-fidelity simulation models on large-scale parallel computer systems can produce data at high computational throughput, but modern architectural trade-offs make full persistent storage to the slow I/O subsystem prohibitively costly with respect to time. We demonstrate the feasibility and potential of combining in situ topological contour tree analysis and compact image-based data representation to address this problem. Our experiments show significant reductions in storage requirements using topology-guided layered depth imaging, while preserving flexibility for explorative visualization and analysis. Our approach represents an effective and easy-to-control trade-off between storage overhead and visualization fidelity for large data visualization.