CIC: EAGER: Scalable Algebraic Visualization in the Cloud

大型科学可视化系统和大型科学数据库的需求正在融合。 可视化系统配备了基本的查询处理能力，观察到简单地通过图形管道“抛出数据集”忽略了现实应用程序所需的可扩展重组，操纵和过滤。 此外，人们越来越重视原位可视化-在单个平台上执行可视化和数据处理，以避免数据传输成本并提供新的优化。 特别是，云计算作为一个大规模的可视化平台，除了大规模的数据处理平台的作用是underexplored.In响应这些压力，PI提出了一种新的方法来解决可扩展的可视化问题，一个由数据库社区开发的代数查询处理技术，再加上数据密集型可扩展计算平台，如MapReduce，Dryad，和他们的同时代人。 具体来说，PI正在开发一个专门用于操纵和可视化网格结构数据集的可扩展可视化运算符代数。 PI以前在有限元模拟中发现的非结构化网格数据集的代数上的工作提供了基础，但不支持有效的并行处理，并且不能表达某些常见的可视化任务。 其他现有的系统更倾向于深度而不是广度，专注于在特定硬件上优化特定的可视化算法，而不是可以在云中常见的无共享商用计算机集群上运行的通用可视化分析平台。 该新方法正在Windows Azure平台上开发和部署，提供了一组核心的可扩展原语，用于使用无共享架构操作网格数据集，能够表达各种可视化算法，并适合代数推理和优化。http://visdb.cs.washington.edu

The requirements for large-scale scientific visualization systems and large-scale scientific databases are converging. Visualization systems are being equipped with rudimentary query processing capabilities, observing that simply "throwing datasets" through the graphics pipeline ignores the scalable restructuring, manipulation, and filtering required by realistic applications. Further, there is increasing emphasis on in situ visualization --- execution of visualization and data processing on a single platform to avoid data transfer costs and afford new optimizations. In particular, the role of cloud computing as a large-scale visualizaton platform in addition to a large-scale data processing platform is underexplored.In response to these pressures, the PI proposes a novel approach to the problem of scalable visualization, one informed by the algebraic query processing techniques developed by the database community coupled with recent advances in data-intensive scalable computing latforms such as MapReduce, Dryad, and their contemporaries. Specifically, the PI is developing an algebra of scalable visualization operators specialized for manipulating and visualizing mesh-structured datasets. The PI's previous work on an algebra for unstructured grid datasets found in finite element simulations provides a foundation, but does not support efficient parallel processing and cannot express certain common visualization tasks. Other existing systems favor depth over breadth, focusing on optimizations for specific visualization algorithms on specific hardware rather than a generic platform for visual analytics that can run on the shared-nothing clusters of commodity computers typically found in the cloud. The new approach, being developed and deployed on the Windows Azure platform, provides a core set of scalable primitives for manipulating mesh datasets using shared-nothing architectures, capable of expressing a variety of visualization algorithms, and amenable to algebraic reasoning and optimization.For further information see the project web page: http://visdb.cs.washington.edu