Massive dataset sizes can make visualization difficult or impossible. One solution to this problem is to divide a
dataset into smaller pieces and then stream these pieces through memory, running algorithms on each piece. This
paper presents a modular data-flow visualization system architecture for culling and prioritized data streaming.
This streaming architecture improves program performance both by discarding pieces of the input dataset that
are not required to complete the visualization, and by prioritizing the ones that are. The system supports a
wide variety of culling and prioritization techniques, including those based on data value, spatial constraints, and
occlusion tests. Prioritization ensures that pieces are processed and displayed progressively based on an estimate
of their contribution to the resulting image. Using prioritized ordering, the architecture presents a progressively
rendered result in a significantly shorter time than a standard visualization architecture. The design is modular,
such that each module in a user-defined data-flow visualization program can cull pieces as well as contribute to
the final processing order of pieces. In addition, the design is extensible, providing an interface for the addition
of user-defined culling and prioritization techniques to new or existing visualization modules.
Qviz is a lightweight, modular, and easy to use parallel system for interactive analytical query processing and visual presentation of large datasets. Qviz allows queries of arbitrary complexity to be easily constructed using a specialized scripting language. Visual presentation of the result is also easily achieved via simple scripted and interactive commands to our query-specific visualization tools. This paper describes our initial experiences with the Qviz system for querying and visualizing scientific datasets, showing how Qviz has been used in two different applications: ocean modeling and linear accelerator simulations.