Quantitatively driven visualization and analysis on emerging architectures

We live in a world of ever-increasing amounts of information that is not only dynamically changing but also dramatically changing in complexity. This trend of "information overload" has quickly overwhelmed our capabilities to explore, hypothesize, and thus fully interpret the underlying details in these data. To further complicate matters, the computer architectures that have traditionally provided improved performance are undergoing a revolutionary change as manufacturers transition to building multi- and many-core processors. While these trends have the potential to lead to new scientific breakthroughs via simulation and modeling, they will do so in a disruptive manner, potentially placing a significant strain on software development activities including the overall data analysis process. In this paper we explore an approach that exploits these emerging architectures to provide an integrated environment for high-performance data analysis and visualization.