Transfer Function Design for Scientific Discovery

As computation scales beyond terascale, the scientific problems under study through computing are increasingly pushing the boundaries of human knowledge about the physical world. It is more pivotal than ever to quickly and reliably extract new knowledge from these complex simulations of ultra scale. In this project, the PI expanded the traditional notion of transfer function, which maps physical quantities to visual cues via table look-ups, to include general temporal as well as multivariate patterns that can be described procedurally through specialty mini programming languages. Their efforts aimed at answering a perpetual question of fundamental importance. That is "what a visualization should show". Instead of waiting for application scientists to initiate the process, the team at University of Tennessee worked closely with scientists at ORNL in a proactive role to envision and design elegant, powerful, and reliable tools that a user can use to specify "what is interesting". Their new techniques include visualization operators that revolve around correlation and graph properties, relative patterns in statistical distribution, temporal regular expressions, concurrent attribute subspaces and traditional compound boolean range queries. The team also paid special attention to ensure that all visualization operators are inherently designed with great parallel scalability to handle tera-scale datasets in both homogeneous and heterogeneous environments. Success has been demonstrated with leading edge computational science areas include climate modeling, combustion and systems genetics