A data-centric algorithm for automated detection and extraction of isoparametric surfaces

Abstract

The traditional workflow for high-performance computing simulation is often to prepare input, run a simulation, and visualize the results as a post-processing step. In the biomedical and seismic industries, these results comprise uniform 3D arrays that can approach tens of petabytes depending on the domain. Visually exploring output data requires significant system resources and time, as data is moved between the simulation cluster and the visualization cluster. Resources and time can be conserved if the simulation and visualization can access the same system resources and data. End-to-end workflow time can be decreased if the simulation and visualization can be performed simultaneously. Data-centric visualization provides a platform in which the same high-performance server can execute both the simulation and visualization. In this paper, we discuss a visualization framework for exploring very-large data sets using both direct and isoparametric point extraction volume rendering techniques. Our design considers accelerators available in next-generation servers using IBM Power technology and GPUs (graphics processing units). GPUs can accelerate generation and compression of two-dimensional display images that can be transferred across a network to a variety of display devices. Users will be able to remotely steer visualization and simulation applications. In this paper, we discuss an early implementation and additional challenges for future work.