A grid-enabled MPI: message passing in heterogeneous distributed computing systems
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
Distributed frameworks and parallel algorithms for processing large-scale geographic data
Parallel Computing - Special issue: High performance computing with geographical data
A quadtree approach to domain decomposition for spatial interpolation in grid computing environments
Parallel Computing - Special issue: High performance computing with geographical data
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
An Experimental Study on How to Build Efficient Multi-core Clusters for High Performance Computing
CSE '08 Proceedings of the 2008 11th IEEE International Conference on Computational Science and Engineering
Thrashing: its causes and prevention
AFIPS '68 (Fall, part I) Proceedings of the December 9-11, 1968, fall joint computer conference, part I
Extraction of drainage networks from large terrain datasets using high throughput computing
Computers & Geosciences
Cooperative and decentralized workflow scheduling in global grids
Future Generation Computer Systems
Cloud computing for geosciences: deployment of GEOSS clearinghouse on Amazon's EC2
Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems
WPS orchestration using the Taverna workbench: The eScience approach
Computers & Geosciences
Implementation and performance optimization of a parallel contour line generation algorithm
Computers & Geosciences
Environmental Modelling & Software
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Many geographic analyses are very time-consuming and do not scale well when large datasets are involved. For example, the interpolation of DEMs (digital evaluation model) for large geographic areas could become a problem in practical application, especially for web applications such as terrain visualization, where a fast response is required and computational demands exceed the capacity of a traditional single processing unit conducting serial processing. Therefore, high performance and parallel computing approaches, such as grid computing, were investigated to speed up the geographic analysis algorithms, such as DEM interpolation. The key for grid computing is to configure an optimized grid computing platform for the geospatial analysis and optimally schedule the geospatial tasks within a grid platform. However, there is no research focused on this. Using DEM interoperation as an example, we report our systematic research on configuring and scheduling a high performance grid computing platform to improve the performance of geographic analyses through a systematic study on how the number of cores, processors, grid nodes, different network connections and concurrent request impact the speedup of geospatial analyses. Condor, a grid middleware, is used to schedule the DEM interpolation tasks for different grid configurations. A Kansas raster-based DEM is used for a case study and an inverse distance weighting (IDW) algorithm is used in interpolation experiments.