EVITA - A Prototype System for Efficient Visualization and Interrogation of Terascale Datasets

物理现象的大规模计算模拟产生前所未有的数据量（tb和pb）。不幸的是，适当的数据管理和可视化技术的发展并没有跟上这些数据集的规模和复杂性。对于许多模拟，数据本身的存储是一个严重的问题，更不用说查看和理解数据了。为此，该项目开发了一个原型集成系统（EVITA），以促进对万亿级数据集的探索。该系统将涉及非常大的数据集的存储和表示，它们的分析和操作，以及结果图像的呈现和表示。这将使我们能够前所未有地访问我们的CFD数据集，我们希望这能带来新的基本理解。该项目侧重于结构化、直线或曲线网格上的计算流体动力学/水动力学和海洋学应用的时变数据集；然而，EVITA系统适用于具有类似底层结构的其他万亿级数据集的通用可视化环境。此外，该系统适用于分布式甚至远程可视化使用。EVITA系统的基石是一个表示方案，允许对数据集中的宏观特征进行排序访问。使用小波变换技术对数据和网格进行变换，同时使用特征检测算法直接识别上下文重要特征并对其进行排序。该排序用于生成包含特定应用程序知识的四维显著性图，这反过来又允许对数据中的重要特征进行有效压缩和渐进表示的编码。以此为基础，EVITA系统创建了一个有效的预览，所需数据的“有损”图像，并允许用户选择感兴趣的区域（rol）以进一步丰富。这些滚动在编码的比特流中被赋予更高的优先级，随后显示得更快。初步结果证明了该方法的有效性。该项目采用了一种新颖的方法，通过利用多学科团队的专业知识，将多个学科的研究整合到EVITA系统中。这既为问题带来了许多观点，又确保了我们的结果在实际中是有用的。

Large-scale computational simulations of physical phenomena produce data of unprecedented size (terabytes and petabytes). Unfortunately, development of appropriate data management and visualization techniques has not kept pace with the size and complexity of such datasets. For many simulations, the storage of the data itself, not to mention seeing and understanding it, is a serious problem. This project responds to this by developing a prototype, integrated system (EVITA) to facilitate exploration of terascale datasets. This system will be concerned with the storage and representation of very large datasets, their analysis and manipulation and rendering and presentation of the resulting images. This will allow unprecedented access to our CFD data sets, which we expect to lead to new fundamental understanding.The project focuses on time-varying datasets from computational fluid dynamic/hydrodynamic and oceanographic applications on structured, rectilinear or curvilinear grids; however, the EVITA system is applicable as a general visualization environment for other terascale datasets with similar underlying structure. Additionally, the system is amenable to distributed- or even remote-visualization uses. The cornerstone of the EVITA system is a representational scheme that allows ranked access to macroscopic features in the dataset. The data and grid are transformed using wavelet techniques while a feature-detection algorithm is used to identify and rank contextually significant features directly. This ranking is used to generate a four-dimensional significance map that incorporates application-specific knowledge, which in turn allows an encoding with efficient compression and a progressive representation of significant features in the data. From this, the EVITA system creates as a preview an efficient, "lossy" image of the desired data and lets the user select regions of interest (ROls) for further enrichment. These ' Rols are given higher priority in the encoded bitstream, and subsequently displayed faster. Preliminary results demonstrate the efficacy of this approach. The project takes a novel approach to terascale visualization by capitalizing on the expertise of a multidisciplinary team to integrate research from several disciplines into the EVITA system. This both brings many perspectives to bear on the problem, and ensures that our results are practically useful.