Scalable Computation of Streamlines on Very Large Datasets

Conference · Tue Sep 01 04:00:00 EDT 2009

OSTI ID:983119

Pugmire, David; Childs, Hank; Garth, Christoph; Ahern, Sean; Weber, Gunther H

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 decomposition 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.

• Research Organization: Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA (US)
• Sponsoring Organization: Computational Research Division
• DOE Contract Number: AC02-05CH11231
• OSTI ID: 983119
• Report Number(s): LBNL-3264E
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
ALGORITHMS
PERFORMANCE
VECTOR FIELDS
visualization
streamlines
flow
scaling
parallel