Visualization has proven its value in scientific advances by helping scientists gain insight from their data and verify and correct scientific computations. As the amount of scientific data collected from sensors or simulations grows up to the order of petabytes, visualization of this large-scale data often requires high-performance distributed computing. However, it is typically challenging to develop or use visualization tools on a scalable distributed environment since they require a deep understanding of visualization algorithms, parallel processing, complex configurations and data handling. I envision the virtualization of a distributed visualization environment, which enables users to create their visualization as easily as they do on their desktop computers, while fully making use of underlying distributed visualization resource.

The individual components of a data visualization pipeline can be abstracted as: data retrieval, filtering/mining, rendering and display. My PhD work, the Scalable Adaptive Graphics Environment (SAGE) and Visualcasting, virtualizes the last component in the pipeline. With SAGE and Visualcasting, displays are totally virtualized from visualization applications. They just pass image buffers to SAGE. SAGE then scales the images to arbitrarily sized display walls ranging from a single desktop panel to a scalable array of LCD panels that are stitched together. Visualcasting extends this display virtualization by broadcasting SAGE image streams to multiple heterogeneous display clients. An analytical model of SAGE and Visualcasting was built and verified. This model will be extended to address major research questions in virtualizing the entire data visualization pipeline and then be used to investigate candidate approaches for the virtualization.

This research will improve the performance and usability of the visualization and analysis tools on ALCF's new visualization cluster EUREKA and motivate INCITE project investigators to leverage advanced visualization capabilities in their applications. Potentially this will change the paradigm of large-scale data visualization and expedite adoption of distributed visualization techniques to the broader DOE computational science community.