SC Conference - Activity Details
Scalable Computation of Streamlines on Very Large Datasets
Authors:| David Pugmire (Oak Ridge National Laboratory) |
| Hank Childs (Lawrence Berkeley National Laboratory) |
| Christoph Garth (University of California, Davis) |
| Sean Ahern (Oak Ridge National Laboratory) |
| Gunther Weber (Lawrence Berkeley National Laboratory) |
| Large-Scale Applications |
| Tuesday, 11:30AM - 12:00PM |
| Room PB255 |
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.
The full paper can be found in the
ACM Digital Library and IEEE Computer Society
   |