Scalable computation of streamlines on very large datasets

• Published in: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis pp. 1 - 12
• Main Authors: Pugmire, Dave, Childs, Hank, Garth, Christoph, Ahern, Sean, Weber, Gunther H.
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 14.11.2009
• Series: ACM Conferences
• Subjects: Data models; Data visualization; flow; General and reference > Cross-computing tools and techniques > Performance; Government; Hands; Human-centered computing > Human computer interaction (HCI) > Interactive systems and tools; Human-centered computing > Interaction design; Loading; Memory management; parallel; Program processors; Scalability; scaling; Software and its engineering > Software organization and properties > Contextual software domains > Operating systems > Memory management > Allocation > deallocation strategies; Software and its engineering > Software organization and properties > Contextual software domains > Operating systems > Process management; Software and its engineering > Software organization and properties > Extra-functional properties > Software performance; streamlines; Vectors; Visualization; scaling; visualization; streamlines; parallel; flow
• ISBN: 1605587443; 9781605587448
• ISSN: 2167-4329
• DOI: 10.1145/1654059.1654076

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.