An effective parallelization algorithm for DEM generalization based on CUDA. (April 2019)

• Record Type: Journal Article
• Authors: Wu, Qianjiao; Chen, Yumin; Wilson, John P.; Liu, Xuejun; Li, Huifang
• Is Part Of: Environmental modelling & software. Volume 114(2019)
• Page Start: 64
• Page End: 74
• Publication Date: 2019-04
• Subjects: DEM -- Generalization -- Parallelization algorithm -- CUDA; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.01.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: We present a parallelization method for DEM generalization based on CUDA. We propose a parallel-multi-point algorithm to extract the critical points from the DEM. The method reduces response time by up to 96% compared with three existing methods. The method can better sustain the drainage features during the generalization process than three existing methods.