Parallel multigrid in an adaptive PDE solver based on hashing and space-filling curves
Parallel Computing - Special issue on parallelization techniques for numerical modelling
Parallel Processing Algorithms for GIS
Parallel Processing Algorithms for GIS
Guest editorial: high performance computing with geographical data
Parallel Computing - Special issue: High performance computing with geographical data
Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms 2)
Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms 2)
Research On Cluster-Based Parallel GIS with the Example of Parallelization on GRASS GIS
GCC '07 Proceedings of the Sixth International Conference on Grid and Cooperative Computing
Smugglers and border guards: the GeoStar project at RPI
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Streaming multigrid for gradient-domain operations on large images
ACM SIGGRAPH 2008 papers
Sea floor bathymetry trackline surface fitting without visible artifacts using ODETLAP
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Distributed gradient-domain processing of planar and spherical images
ACM Transactions on Graphics (TOG)
3D oceanographic data compression using 3D-ODETLAP
SIGSPATIAL Special
Slope preserving lossy terrain compression
SIGSPATIAL Special
Parallel gradient domain processing of massive images
EG PGV'11 Proceedings of the 11th Eurographics conference on Parallel Graphics and Visualization
HFPaC: GPU friendly height field parallel compression
Geoinformatica
Hi-index | 0.00 |
We introduce a parallel approximation of an Over-determined Laplacian Partial Differential Equation solver (ODETLAP) applied to the compression and restoration of terrain data used for Geographical Information Systems (GIS). ODETLAP can be used to reconstruct a compressed elevation map, or to generate a dense regular grid from airborne Light Detection and Ranging (LIDAR) point cloud data. With previous methods, the time to execute ODETLAP does not scale well with the size of the input elevation map, resulting in running times that are prohibitively long for large data sets. Our algorithm divides the data set into patches, runs ODETLAP on each patch, and then merges the patches together. This method gives two distinct speed improvements. First, we provide scalability by reducing the complexity such that the execution time grows almost linearly with the size of the input, even when run on a single processor. Second, we are able to calculate ODETLAP on the patches concurrently in a parallel or distributed environment. Our new patch-based implementation takes 2 seconds to run ODETLAP on an 800 x 800 elevation map using 128 processors, while the original version of ODETLAP takes nearly 10 minutes on a single processor (271 times longer). We demonstrate the effectiveness of the new algorithm by running it on data sets as large as 16000 x 16000 on a cluster of computers. We also discuss our preliminary results from running on an IBM Blue Gene/L system with 32,768 processors.