One major factor that is contributing to the growth of data size is the increasingly widespread ability to perform very large scale time-varying simulations. Although intensive research has been undertaken to optimize the performance of visualizing very large data sets, most of the existing methods have not targeted at time-varying data. One mission of the Gravity Research Group is to perform a comprehensive study ...

As teraflop or petaflop computers become increasingly more accessible, large scale simulations that can produce three-dimensional, time-dependent scalar and vector data begin to play an instrumental role in problem solving and scientific discovery. For the past two decades, researchers have developed various visualization techniques to enable effective analysis of scientific data. Among the many research directions...

Due to the sheer size of data, it is often effective to optimize the visualization algorithms in a view-dependent manner. For example, visibility culling can be used to reduce the underlying data complexity and accelerate rendering speed. In addition, when it is expensive to visualize the data from arbitrary iviews at interactive speeds, there is a need to consider finding the optimial views so that the user can more ...

Parallel processing plays an increasingly more important role in the area of scientific visualization as the size of data continues to increase. Although  many parallel visualization algorithms have been developed in the past, the complexity and scale of the data generated by terascale (and soon petascale) simulations demand even greater advancement in fundamental visualization algorithms and system designs...