Data analysis and visualization can efficiently extract knowledge from scientific data. As computational science approaches exascale, however, managing the scale and complexity of the visualization process can be daunting. For example, the DOE/ASCR Workshop on Visual Analysis and Data Exploration at Extreme Scale (Salt Lake City, 2007) concluded that, "datasets being produced by experiments and simulations are rapidly outstripping our ability to explore and understand them," and the International Exascale Software Project draft road map (Dongarra et al. 2009) agreed that, "analysis and visualization will be limiting factors in gaining insight from exascale data."
There is a critical need to assist scientists with intelligent
algorithms that save the most important data and extract the knowledge
contained therein. To address these needs, our team is investigating
the execution of analysis and visualization tasks in parallel directly
on leadership machines, at very large scale. The topics below
represent some of the research that our group tackles. The interaction with complex, multivariate,
time-varying datasets drives our research, which is implemented in open-source