Programming with POSIX threads
Programming with POSIX threads
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Introduction to Parallel Computing
Introduction to Parallel Computing
Efficiency of Thread-Parallel Java Programs from Scientific Computing
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Possibilities to Solve the Clique Problem by Thread Parallelism using Task Pools
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 5 - Volume 06
Parallel Programming in C with MPI and OpenMP
Parallel Programming in C with MPI and OpenMP
Short communication: Parallelisation of storage cell flood models using OpenMP
Environmental Modelling & Software
A methodology for aligning raster flow direction data with photogrammetrically mapped hydrology
Computers & Geosciences
A comparison of three parallelisation methods for 2D flood inundation models
Environmental Modelling & Software
Parallel Programming: for Multicore and Cluster Systems
Parallel Programming: for Multicore and Cluster Systems
Scalable algorithms for large high-resolution terrain data
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
Extraction of hydrological proximity measures from DEMs using parallel processing
Environmental Modelling & Software
Parallel scanline algorithm for rapid rasterization of vector geographic data
Computers & Geosciences
Hi-index | 0.00 |
Hand in hand with the increasing availability of high resolution digital elevation models (DEMs), an efficient computation of land-surface parameters (LSPs) for large-scale digital elevation models becomes more and more important, in particular for web-based applications. Parallel processing using multi-threads on multi-core processors is a standard approach to decrease computing time for the calculation of local LSPs based on moving window operations (e.g. slope, curvature). LSPs which require non-localities for their calculation (e.g. hydrological connectivities of grid cells) make parallelization quite challenging due to data dependencies. On the example of the calculation of the LSP ''flow accumulation'', we test the two parallelization strategies ''spatial decomposition'' and ''two phase approach'' for their suitability to manage non-localities. Three datasets of digital elevation models with high spatial resolutions are used in our evaluation. These models are representative types of landscape of Central Europe with highly diverse geomorphic characteristics: a high mountains area, a low mountain range, and a floodplain area in the lowlands. Both parallelization strategies are evaluated with regard to their usability on these diversely structured areas. Besides the correctness analysis of calculated relief parameters (i.e. catchment areas), priority is given to the analysis of speed-ups achieved through the deployed strategies. As presumed, local surface parameters allow an almost ideal speed-up. The situation is different for the calculation of non-local parameters which requires specific strategies depending on the type of landscape. Nevertheless, still a significant decrease of computation time has been achieved. While the speed-ups of the computation of the high mountain dataset are higher by running the ''spatial decomposition approach'' (3.2 by using four processors and 4.2 by using eight processors), the speed-ups of the ''two phase approach'' have proved to be more efficient for the calculation of the low mountain and the floodplain dataset (2.6 by using four processors and 2.9 by using eight processors).