PECASE: Parallel Visualization and Interaction Techniques for Exploring Large Scale Volume Data

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One of the best methods of presenting large scientific data sets to users is visualization. Over the past ten years, our ability to create useful visualizations has undergone a revolution due to advances in 3D graphics hardware and software technology. However, we still face endless challenges as the complexity and scale of the data, whether from physical experiments or computer simulation, continue to grow. In many cases, visualization of large-scale data sets is very expensive, sometimes even more expensive than the supercomputer simulation that produced the data in the first place. This project will perform a comprehensive study aimed at two questions that arise from this situation. First, can we use parallel supercomputers to generate data and for visualization calculations in an optimal fashion? Second, are there mechanisms and methodologies to enhance the productivity of scientists doing exploratory and production visualization? The project will develop a fully parallel visualization system to maximize the overall throughput of the system. This system will emphasize computing on clusters of PCs. Four major sub-themes of this part of the project will be parallel preprocessing algorithms to prepare data for visualization, parallel rendering algorithms to produce the graphics, parallel image compression and transport to efficiently manage the graphics, and application-controlled parallel I/O to store the data for visualization. The project will particularly focus on parallel feature-enhanced visualization algorithms and parallel subsetting of the visualized data. Complementing the research component of this project is an educational component, directed at undergraduate and graduate students, aimed at inspiring interest and fostering creativity through hands-on experiences. One aspect of this education is an applied visualization course to non-CS graduate students, which should foster greater interaction and cross-fertilization of ideas from the students' home departments.