Parallelization strategies to deal with non-localities in the calculation of regional land-surface parameters

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• 作者：Steffen Schiele^a ; ^{steffen.schiele@informatik.uni-halle.de} ; Markus Mö ; ller^b ; Holger Blaar^a ; Detlef Thü ; rkow^b ; Matthias Mü ; ller-Hannemann^a
• 关键词：Parallel processing ; Flow direction ; Terrain analysis ; High resolution elevation models ; Threads ; Multi-core
• 刊名：Computers and Geosciences
• 出版年：2012
• 期刊代码：16_00983004
• 类别：ear
• 出版时间：July, 2012
• 卷：44
• 期：Complete
• 页码：1-9
• 文件大小：1785 K

摘要

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 鈥渇low accumulation鈥? we test the two parallelization strategies 鈥渟patial decomposition鈥?and 鈥渢wo phase approach鈥?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 鈥渟patial decomposition approach鈥?(3.2 by using four processors and 4.2 by using eight processors), the speed-ups of the 鈥渢wo phase approach鈥?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).