A layered approach to parallel computing for spatially distributed hydrological modeling
A layered approach to parallel computing for spatially distributed hydrological modeling
复制标题
空间分布式水文建模并行计算的分层方法
DOI：
10.1016/j.envsoft.2013.10.005
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Qin, Cheng-Zhi
中科院分区：
文献类型：
--
作者：
Liu, Junzhi;Zhu, A-Xing;Liu, Yongbo;Zhu, Tongxin;Qin, Cheng-Zhi
大型流域的分布式水文模拟通常需要大量的计算，这就需要使用并行计算。每种类型的水文模型都有自己的计算特征，因此需要不同的并行计算策略。在本文中，我们重点关注一种水文模型，在这种模型中，从上游模拟单元到下游模拟单元依次执行陆上水流路径和河道水流路径（称为完全顺序相关水文模型，简称FSDHM）。关于这类模型的并行计算的出版工作很少。本文提出了一种分层并行计算方法。该方法根据流动方向将模拟单元分层。在每一层中，模拟单元之间没有上下游关系。因此，同一层的仿真单元的计算是独立的，可以并行进行。基于网格的FSDHM与开放多处理（OpenMP）库并行化，以说明所提出方法的实现。在具有多核中央处理器（cpu）的计算机上，使用不同分辨率的数据集（分别为30 m、90 m和270 m）对该并行模型的性能进行了实验。结果表明，大数据集的并行性能优于小数据集，30 m数据集在24线程下的最大加速比达到12.49。
Distributed hydrological simulations over large watersheds usually require an extensive amount of computation, which necessitates the use of parallel computing. Each type of hydrological model has its own computational characteristics and therefore needs a distinct parallel-computing strategy. In this paper, we focus on one type of hydrological model in which both overland flow routing and channel flow routing are performed sequentially from upstream simulation units to downstream simulation units (referred to as Fully Sequential Dependent Hydrological Models, or FSDHM). There has been little published work on parallel computing for this type of model. In this paper, a layered approach to parallel computing is proposed. This approach divides simulation units into layers according to flow direction. In each layer, there are no upstream or downstream relationships among simulation units. Thus, the calculations on simulation units in the same layer are independent and can be conducted in parallel. A grid-based FSDHM was parallelized with the Open Multi-Processing (OpenMP) library to illustrate the implementation of the proposed approach. Experiments on the performance of this parallel model were conducted on a computer with multi-core Central Processing Units (CPUs) using datasets of different resolutions (30 m, 90 m and 270 m, respectively). The results showed that the parallel performance was higher for simulations with large datasets than with small datasets and the maximum speedup ratio reached 12.49 under 24 threads for the 30 m dataset.
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影响因子：
64.8
作者：
M. J. Hall
通讯作者：
M. J. Hall
影响因子：
3.3
作者：
Wang Gang-sheng
通讯作者：
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影响因子：
5.4
作者：
Ruh-Ming Li;D. B. Simons;M. A. Stevens
通讯作者：
Ruh-Ming Li;D. B. Simons;M. A. Stevens
DOI：
10.11820/dlkxjz.2013.04.006
发表时间：
2013-05
期刊：
Progress in geography
影响因子：
--
作者：
Liu Junzhi
通讯作者：
Liu Junzhi
DOI：
--
发表时间：
2004-09
期刊：
The Journal of Applied Behavioral Science
影响因子：
--
作者：
T. Mattson;B. Sanders;Berna L. Massingill
通讯作者：
T. Mattson;B. Sanders;Berna L. Massingill