Image-Based Exploration of Iso-Surfaces for Large Multi-Variable Datasets using Parameter Space

Roba Binyahib, Madhusudhanan Srinivasan, Christopher Knox
With rapid advances in HPC resources, more complex simulations have resulted in larger data size, with higher resolution and many variables. Visualizing large multivariate datasets is a challenging problem that often requires high-end clusters. Consequently, novel visualization techniques are needed to explore such complex data. Explorable image (EI) is a novel approach that provides limited interactive visualization without the need to rerender from the original data. In this work, we used the concept of EI to create a workflow that generates explorable iso-surfaces for scalar fields in a multivariate, time-varying dataset. We present a run-time tool that allows the user to interactively browse and calculate a combination of iso-surfaces superimposed on each other. The result is the same as calculating multiple iso-surfaces from the original data but without the memory and processing overhead. Our tool also allows the user to change the (scalar) values superimposed on each of the surfaces, modify their color map, and interactively re-light the surfaces. We demonstrate the effectiveness of our approach over a multi-terabyte combustion dataset. We also illustrate the efficiency and accuracy of our technique by comparing our results with those from a more traditional visualisation pipelines.