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Wednesday, July 20 • 10:30am - 11:00am
AD: A Scalable High-performance Topographic Flow Direction Algorithm for Hydrological Information Analysis

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Hydrological information analysis based on Digital Elevation Models (DEM) provide hydrological properties derived from high-resolution topographic data represented as elevation grid. Flow direction detection is one of the most computationally intensive functions. As the resolution of DEM becomes higher, the computational bottleneck of this function hinders the use of these DEM data in large-scale studies. As the computation of flow directions for the study extent needs global information, the parallelization involves iterative communications. This paper presents an efficient parallel flow direction detection algorithm that identifies spatial features (e.g., flats) that can or cannot be computed locally. An efficient sequential algorithm is then applied to resolve those local features, while communication is applied to compute non-local features. This strategy significantly reduces the number of iterations needed in the parallel algorithm. Experiments show that our algorithm outperformed the best existing parallel (i.e., the d8 algorithm in TauDEM) by two orders of magnitude. The parallel algorithm exhibited desirable scalability on Stampede and ROGER supercomputer.

Wednesday July 20, 2016 10:30am - 11:00am
Chopin Ballroom

Attendees (7)