Optimizing the pre-processing of scientific visualization techniques using QEF

Scientific Visualization is a computer-based field concerned with techniques that allow scientists to create graphical representations from datasets generated by computational simulations or acquisition instruments. To address the computational cost of visualization tasks, specially for large datasets, researchers have explored grid environments as a platform for their parallel evaluation. It is however not trivial to adapt each different visualization technique to run in grid environments. A desirable alternative would separate the specificities of data and process distribution in grids from visualization computation logic. In this work we claim that the QEF (query evaluation framework) leverages scientific visualization computation with the above mentioned characteristics. Visualization computation techniques are modeled as operators in an algebra and integrated with a set of control operators that manage data distribution leading to a parallel QEP (query execution plan). We show the benefits of parallelization for two of those techniques: particle tracing and volume rendering. For these techniques, our experiments demonstrate many positive aspects of the solution presented, as well as opportunities for future work.