An effective parallelization algorithm for DEM generalization based on CUDA
| Autor: | Huifang Li, Xuejun Liu, John Wilson, Yumin Chen, Qianjiao Wu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Environmental Engineering
Matching (graph theory) Mean squared error Computer science Generalization Ecological Modeling 0211 other engineering and technologies Parallel algorithm Response time Terrain 02 engineering and technology Parallel computing 010502 geochemistry & geophysics 01 natural sciences CUDA Surface roughness Algorithm Software 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | Environmental Modelling & Software. 114:64-74 |
| ISSN: | 1364-8152 |
| DOI: | 10.1016/j.envsoft.2019.01.002 |
| Popis: | An effective parallelization algorithm based on the compute-unified-device-architecture (CUDA) is developed for DEM generalization that is critical to multi-scale terrain analysis. It aims to efficiently retrieve the critical points for generating coarser-resolution DEMs which maximally maintain the significant terrain features. CUDA is embedded into a multi-point algorithm to provide a parallel-multi-point algorithm for enhancing its computing efficiency. The outcomes are compared with the ANUDEM, compound and maximum z-tolerance methods and the results demonstrate the proposed algorithm reduces response time by up to 96% compared to other methods. As to RMSE, it performs better than ANUDEM and needs half the number of points to keep the same RMSE. The mean slope and surface roughness are reduced by less than 1% in the tested cases. The parallel algorithm provides better streamline matching. Given its high computing efficiency, the proposed algorithm can retrieve more critical points to meet the demands of higher precision. |
| Databáze: | OpenAIRE |
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