Concerning the low efficiency of calculating flow accumulation on high resolution digital terrain data, a parallel algorithm was put forward based on the Compute Unified Device Architecture (CUDA) and flooding model. Based on the technology of Graphic Processing Unit (GPU), two strategies were designed to improve the speed of the extraction. Firstly, the calculation of flow accumulation was divided into a plurality of independent tasks for parallel processing. Secondly, the time of data exchange was reduced through the asynchronous data transmission. The experimental results show that the efficiency of the parallel algorithm is superior of the serial algorithm. The acceleration of river network extraction reached 62 times in NVIDIA Geforce GTX660 for 600 MB DEM data with 9784×8507 grid size.
王玉着, 刘修国, 张唯. 统一设备计算架构下的栅格河网提取并行算法[J]. 计算机应用, 2015, 35(4): 960-963.
WANG Yuzhuo, LIU Xiuguo, ZHANG Wei. Parallel algorithm of raster river network extraction based on CUDA. Journal of Computer Applications, 2015, 35(4): 960-963.
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2015, Vol. 35 
