|Title||Scalable Computation of Streamlines on Very Large Datasets
(In Proceedings) |
|in||SC '09: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis|
David Pugmire, Hank Childs, Christoph Garth, Sean Ahern, Gunther H. Weber |
|Keyword(s)||parallel computation, streamlines, vector field visualization|
|Address||New York, NY, USA|
Understanding vector fields resulting from large scientific simulations is an important and often difficult task. Streamlines, 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 computation, and requires careful balancing of computational demands placed on I/O, memory, communication, and processors. 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 prototypical application problems and demonstrate that our hybrid scheme is able to perform well in different settings.