A message passing standard for MPP and workstations
Communications of the ACM
Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
Fast computation of generalized Voronoi diagrams using graphics hardware
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
A Framework for Index Bulk Loading and Dynamization
ICALP '01 Proceedings of the 28th International Colloquium on Automata, Languages and Programming,
Implementing I/O-efficient Data Structures Using TPIE
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Speeding up construction of PMR quadtree-based spatial indexes
The VLDB Journal — The International Journal on Very Large Data Bases
Natural neighbor interpolation based grid DEM construction using a GPU
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
TerraNNI: natural neighbor interpolation on a 3D grid using a GPU
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Geospatial overlay computation on the GPU
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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The proliferation of lidar technology in remote sensing has resulted in extremely large, high resolution point clouds covering a wide variety of terrain. Constructing a grid digital elevation model (DEM) from these large data sets requires extensive computational resources and ample disk space. We propose a framework for leveraging modern computing resources including multi-core distributed systems and general purpose GPU computing to reduce computational bottlenecks and accelerate DEM construction. We employ an I/O-efficient strategy using quad trees to automatically partition the lidar point clouds into a set of independent work bundles. We then distribute these work bundles to multiple GPU-equipped hosts which independently interpolate a portion of the DEM and return partial results. Finally, we gather the partial results and assemble the final DEM I/O-efficiently. Our approach balances I/O, computation, and network communication to reduce bottlenecks. Experimental results show that our approach scales linearly with the number of compute hosts, and achieves speed-ups of 25 × or greater using GPU computing. These results make it practical to use more complex interpolation methods such as regularized splines with tension, which provide geomorphological advantages over simpler interpolation methods such as linear interpolation, nearest neighbor interpolation, or natural neighbor interpolation.