CIC: EAGER: Scalable Algebraic Visualization in the Cloud

University Of Washington, Seattle WA

Investigators

Abstract

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