摘要
分布式水文模型中的逐栅格汇流算法计算量大,需要借助并行计算以满足大流域长历时模拟的要求。针对目前鲜有对基于隐式有限差分的逐栅格汇流算法进行并行计算研究的情况,基于栅格分层的思想提出一种适用于共享内存并行计算环境的逐栅格汇流并行算法。该算法首先根据流向进行栅格分层,使同一层中栅格的计算相互独立,然后将同一层中栅格的计算任务分配到多个计算单元并行计算。采用C++编程语言与OpenMP并行编程库实现了该算法,并选择河北省清水河流域为实验区,在不同数据规模(30m、90m、270m分辨率)、不同核数(2～20个)以及不同栅格分层方法的情况下对算法性能进行了测试。实验结果表明本文提出的并行算法具有较好的加速比和并行效率,且并行效率随数据规模的增大而增大。栅格分层方法对算法并行性能有明显影响,从上到下的分层方法比从下到上的方法具有更高的并行效率。
Grid-to-grid muting algorithms for distributed hydrological modeling require large amount of computations which cannot be provided by sequential computation techniques. Parallel programming technology is necessary for large-scale and long-period simulations using grid-to-grid muting algorithms. There is currently little research on the parallelization of implicit finite difference based muting algorithms. A parallel implicit finite difference based grid-to-grid routing algorithm was presented, based on grids layering. In this algorithm, grids in the watershed were divided into different layers according to flow direction. The calculations of grids in a downstream layer cannot be performed until its upstream layers＇ calculations were completed. The calculations of grids in the same layer are independent of each other, thus can be performed in parallel. So the parallelization strategy is to assign the calculation tasks of grids in the same layer to different CPU-cores to perform parallel computing. The algorithm was implemented by using the C ＋＋ programing language and the Open Multi-processing （OpenMP） Application Programming Interface （API） and was tested in the Qingshuihe watershed of Hebei Province under different amount of input data. The result shows that this parallel algorithm had good speedup and parallel efficiency. In addition, the case study showed that the parallel efficiencies were higher for simulations with large datasets than with small datasets and the up-down layering method had better performances than the down-up layering method.
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2013年第1期123-129,共7页
Journal of National University of Defense Technology
基金
国家863计划资助项目(2011AA120305)
国家自然科学基金资助项目(41023010)
中国科学院知识创新工程重要方向项目(KZCX2-YW-442)
