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

我们产生科学数据的能力比我们理解数据的能力增长得快得多。计算和成像技术的快速进步使科学家能够产生非常大的数据集，但对这些数据集还不存在进行深入分析和理解的适当手段。计算技术允许以不断提高的空间和时间分辨率模拟极其复杂的物理现象，但针对这类数据的数据分析技术仍处于起步阶段。今天，人们必须在这些选择中选择一个：要么忽略海量科学数据集的很大一部分而只分析其中的一部分，要么投入大量的人力时间来非常详细地分析和可视化海量数据集。这两种选择都不可取。这个项目将开发解决这个问题所需的技术，并将测试来自劳伦斯·利弗莫尔国家实验室和NASA艾姆斯研究中心的数据的新技术。在技术上，该项目将采取5个方面来解决大型数据可视化问题。首先，它将把现有的分层方案--即在多个分辨率水平上近似一个数据集的方案--扩展到时变的多值/多维数据。其次，它将改进基于拓扑的方法-即从大数据集中提取定性有趣的特征(如标量和矢量场中的零点、极值和间断)的方法。第三，它将开发并行和分布式算法，用于高效计算分层数据表示、快速提取拓扑和优化计算密集型可视化过程。第四，它将为(沉浸式)可视化环境设计交互式可视化技术，支持依赖于视图和用户指定的自适应细节级别渲染。第五，它将创建一个简单的元数据数据库系统，允许共享用户的体验，即先前选择的导致“良好图像”或整个渲染过程的渲染参数。

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.