This paper presents a parallel adaptive rendering algorithm and its performance for visualizing time-varying unstructured volume data generated from large-scale earthquake simulations. The objective is to visualize 3D seismic wave propagation generated from a 0.5 Hz simulation of the Northridge earthquake, which is the highest resolution volume visualization of an earthquake simulation performed to date. This scalable high-fidelity visualization solution we provide to the scientists allows them to explore in the temporal, spatial, and visualization domain of their data at high resolution. This new high resolution explorability, likely not presently available to most computational science groups, will help lead to many new insights. The performance study we have conducted on a massively parallel computer operated at the Pittsburgh Supercomputing Center helps direct our design of a simulation-time visualization strategy for the higher-resolution, 1Hz and 2 Hz, simulations.

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In this paper, we present a novel visualization technique-kinetic visualization-that uses motion along a surface to aid in the perception of 3D shape and structure of static objects. The method uses particle systems, with rules such that particles flow over the surface of an object to not only bring out, but also attract attention to information on a shape that might not be readily visible with a conventional rendering method which uses lighting and view changes. Replacing still images with animations in this fashion, we demonstrate with both surface and volumetric models in the accompanying videos that, in many cases, the resulting visualizations effectively enhance the perception of three-dimensional shape and structure. We also describe how, for both types of data, a texture-based representation of this motion can be used for interactive visualization using PC graphics hardware. Finally, the results of a user study that we have conducted are presented, which show evidence that the supplemental motion cues can be helpful.

This paper presents a set of feature enhancement techniques. coupled with hardware-accelerated non-photorealistic rendering for generating more perceptually effective visualizations of multidimensional, multivariate volume data, such as those obtained from typical computational fluid dynamics simulations. For time-invariant data, one or more variables are used to either highlight important features in another variable, or add contextural information to the visualization. For time-varying data, rendering of each time step also takes into account the values at neighboring time steps to reinforce the perception of the changing features in the data over time. With hardware-accelerated rendering, interactive visualization becomes possible leading to increased explorability and comprehension of the data.

Motion provides strong visual cues for the perception of shape and depth, as demonstrated by cognitive scientists and visual artists. This paper presents a novel visualization technique-kinetic visualization -that uses particle systems to add supplemental motion cues which can aid in the perception of shape and spatial relationships of static objects. Based on a set of rules following perceptual and physical principles, particles flowing over the surface of an object not only bring out, but also attract attention to, essential information on the shape of the object that might not be readily visible with conventional rendering that uses lighting and view changes. Replacing still images with animations in this fashion, we demonstrate with both surface and volumetric models in the accompanying videos that in many cases the resulting visualizations effectively enhance the perception of three-dimensional shape and structure. The results of a preliminary user study that we have conducted also show evidence that the supplemental motion cues help.