Multiresolution- and Topology-Based Visualization of Large Scientific Data Sets in Parallel and Distributed Computing Environments

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Our ability to generate scientific data is growing much faster than our ability to understand it. The rapid advances in computing and imaging technology allow scientists to generate very large data sets for which appropriate means for in-depth analysis and understanding do not yet exist. Computational techniques allow the simulation of extremely complicated physical phenomena at ever increasing spatial and temporal resolutions, but data analysis technology for this type of data is still in its infancy. Today, one must choose one of these alternatives: One can either ignore a substantial fraction of a massive scientific data set and only analyze portions of it, or one can invest a significant amount of person-time to analyze and visualize a massive data set in great detail. Neither alternative is desirable. This project will develop the technology needed to address this issue, and will test the new techniques on data from Lawrence Livermore National Laboratory and NASA Ames Research Center.Technically, the project will take a 5-prong approach to the large data visualization problem. First, it will extend existing hierarchical schemes - i.e., schemes approximating a data set at multiple resolution levels - to time-varying multi-valued/multi-dimensional data. Second, it will improve topology-based approaches - i.e., approaches that extract qualitatively interesting characteristics (such as zeros, extreme, and discontinuities in scalar and vector fields) from large data sets. Third, it will develop parallel and distributed algorithms for efficient computation of hierarchical data representations, fast extraction of topology, and optimizing compute-intensive visualization processes. Fourth, it will devise interactive visualization techniques for (immersive) visualization environments that support view-dependent and user-specified, adaptive level-of-detail rendering. Fifth, it will create a simple metadata database system allow sharing of a user's experience, i.e., previously chosen rendering parameters leading to "good imagery" or entire rendering processes.