PROJECT TITLE :
A data-centric algorithm for automated detection and extraction of isoparametric surfaces
The traditional workflow for top-performance computing simulation is usually to arrange 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 may approach tens of petabytes relying on the domain. Visually exploring output data requires important system resources and time, as knowledge is moved between the simulation cluster and the visualization cluster. Resources and time will be conserved if the simulation and visualization can access the same system resources and knowledge. End-to-end workflow time can be decreased if the simulation and visualization will be performed simultaneously. Knowledge-centric visualization provides a platform in which the identical high-performance server can execute both the simulation and visualization. During this paper, we discuss a visualization framework for exploring very-giant data sets using each direct and isoparametric point extraction volume rendering techniques. Our style considers accelerators accessible in next-generation servers using IBM Power technology and GPUs (graphics processing units). GPUs will accelerate generation and compression of 2-dimensional display pictures that can be transferred across a network to a variety of show devices. Users can be in a position to remotely steer visualization and simulation applications. In this paper, we have a tendency to discuss an early implementation and extra challenges for future work.
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