Parallel contributing area calculation with granularity control on massive grid terrain datasets

详细信息    查看全文

• 作者：Ling Jiang ; Guoan Tang ; Xuejun Liu ; Xiaodong Song ; Jianyi Yang ; Kai Liu
• 关键词：Parallel computing ; Granularity control ; Digital terrain analysis ; Contributing area ; Flow direction
• 刊名：Computers and Geosciences
• 出版年：2013
• 出版时间：October, 2013
• 年：2013
• 卷：60
• 期：Complete
• 页码：70-80
• 全文大小：1236 K

文摘

The calculation of contributing areas from digital elevation models (DEMs) is one of the important tasks in digital terrain analysis (DTA). The computational process usually involves two steps in a real application: (1) calculating flow directions via a flow model, and (2) computing the contributing area for each grid cell in the DEM. The traditional algorithm for calculating contributing areas is coded as a sequential program executed on a single processor. With the increase of scope and resolution of DEMs, the serial algorithm has become increasingly difficult to perform and is often very time-consuming, especially for DEMs of large areas and fine scales. In recent years, parallel computing is able to meet this challenge with the development of computer technology. However, the parallel implementation with granularity control, an efficient strategy to reap the best parallel performance and to break the limitation of computing resources in processing massive grid terrain datasets, has not been found in DTA research field. This paper develops a message-passing-interface (MPI) parallel approach with granularity control to calculate contributing areas. According to the proposed parallelization strategy, the parallel D8 algorithm with granularity control is designed as well as the parallel AreaD8 algorithm. Based on the domain decomposition of DEM data, it is possible for each process to process multiple partitions decomposed under a grain size. According to an iterative procedure of reading source data, executing the operator and writing resulting data, the partitions achieve the calculation results one by one in each process. The experimental results on a multi-node cluster show that the proposed parallel algorithms with granularity control are the powerful tools to process the big dataset and the parallel D8 algorithm is insensitive to granularity, while the parallel AreaD8 algorithm has an optimal grain size to reap the best parallel performance.