On a Pipeline-Based Architecture for Parallel Visualization of Large-Scale Scientific Data

Many extreme-scale scientific applications generate colossal amounts of data that require a large number of processors for parallel visualization. Among the three well-known visualization schemes, i.e. sort-first/middle/last, sort-last, which is comprised of two stages, i.e. image rendering and composition, is often preferred due to its adaptability to load balance. We propose a very-high-speed pipeline-based architecture for parallel sort-last visualization of big data by developing and integrating three component techniques: i) a fully parallelized per-ray integration method that significantly reduces the number of iterations required for image rendering, ii) a real-time over operator that not only eliminates the restriction of pre-sorting and order-dependency, but also facilitates a high degree of parallelization for image composition, and iii) a novel sort-last visualization pipeline that overlaps rendering and composition to completely avoid waiting time between these two stages. The performance superiority of the proposed parallel visualization architecture is evaluated through rigorous theoretical analyses and further verified by extensive experimental results from the visualization of various real-life scientific datasets on a high-performance visualization cluster.