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

对物理现象的大规模计算模拟产生了空前大小的数据(TB和PB)。不幸的是，适当的数据管理和可视化技术的发展没有跟上这种数据集的大小和复杂性。对于许多模拟来说，数据本身的存储是一个严重的问题，更不用说查看和理解它了。该项目通过开发一个原型集成系统(EVITA)来应对这一问题，以促进对千万亿级数据集的探索。该系统将涉及非常大的数据集的存储和表示、它们的分析和处理以及结果图像的绘制和呈现。这将使我们能够前所未有地访问我们的CFD数据集，我们预计这将导致新的基本理解。该项目专注于结构、直线或曲线网格上的计算流体力学/流体动力学和海洋应用程序的时变数据集；然而，Evita系统可用作其他具有类似底层结构的万亿级数据集的通用可视化环境。此外，该系统可用于分布式--甚至远程--可视化。Evita系统的基石是一个代表性方案，它允许对数据集中的宏观特征进行分级访问。数据和网格使用小波技术进行变换，而特征检测算法用于直接识别和排序上下文重要特征。该排序用于生成包含特定于应用的知识的四维重要性图，这进而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。由此，Evita系统可以创建所需数据的高效、“有损”图像作为预览，并允许用户选择感兴趣的区域(ROL)以进行进一步的丰富。这些角色在编码的比特流中被赋予更高的优先级，并且随后显示得更快。初步结果证明了该方法的有效性。该项目利用一个多学科团队的专业知识，将几个学科的研究整合到Evita系统中，从而采用了一种新的方法来实现千万亿级的可视化。这既带来了许多关于这个问题的观点，也确保了我们的结果是实际有用的。

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.