A prototype framework for parallel visualization of large flow data

Abstract

Scientific visualization seeks to provide deep insight into the complex pattern underlying big data, while flow visualization plays a crucial role in oceanographic-atmospheric modeling and computational fluid dynamics simulation. As an increasingly important strategy, parallel visualization incorporates data visualization with parallel computing by means of MPI (Message Passing Interface) to achieve efficient visual analysis to facilitate scientific study. This paper presents a prototype framework for parallel visualization of large flow data, involving MPI as the low-level parallel computing paradigm, DIY (Do It Yourself) as a block-oriented data-parallel programming library on top of MPI, OSUFlow as a geometry-based flow visualization engine, and VTK (Visualization Toolkit) for data input, graphics rendering, and scene interaction. It exposes the combined power of DIY and OSUFlow, including parallel yet seamless generation of streamlines as well as pathlines from vector data defined on Cartesian, rectilinear, and curvilinear grids, to a broad community of high-performance flow visualization through VTK. Preliminary results show that this framework is capable of exploiting the horsepower of a vast number of processors to accelerate data processing and visualization for explorative analysis of massive steady/unsteady volume flows.