Scalable Computation of Streamlines on Very Large Datasets

Understanding vector fields resulting from large scientific simulations is an important and often difficult task. Stream-lines, curves that are tangential to a vector field at each point, are a powerful visualization method in this context. Application of streamline-based visualization to very large vector field data represents a significant challenge due to the non-local and data-dependent nature of streamline compu-tation, and requires careful balancing of computational de-mands placed on I/O, memory, communication, and proces-sors. In this paper we review two parallelization approaches based on established parallelization paradigms (static de-composition and on-demand loading) and present a novel hybrid algorithm for computing streamlines. Our algorithm is aimed at good scalability and performance across the widely varying computational characteristics of streamline-based problems. We perform performance and scalability studies of all three algorithms on a number of prototypi-cal application problems and demonstrate that our hybrid scheme is able to perform well in different settings