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. ∗(c) 2009 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by a contractor or affiliate of the U.S. Government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only. SC09 Novembe