Parallelization of a hydrological model using the message passing interface

被引:55
作者
Wu, Yiping [1 ,2 ]
Li, Tiejian [3 ]
Sun, Liqun [2 ]
Chen, Ji [2 ]
机构
[1] USGS, ASRC Res & Technol Solut, Earth Resources Observat & Sci EROS Ctr, Sioux Falls, SD 57198 USA
[2] Univ Hong Kong, Dept Civil Engn, Pokfulam, Hong Kong, Peoples R China
[3] Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China
关键词
Hydrological model; Message passing; Parallelization; SWAT; WATER ASSESSMENT-TOOL; SWAT MODEL; LARGE-SCALE; CALIBRATION; SOIL; POLLUTION; SIMULATION; RESERVOIR;
D O I
10.1016/j.envsoft.2013.02.002
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
With the increasing knowledge about the natural processes, hydrological models such as the Soil and Water Assessment Tool (SWAT) are becoming larger and more complex with increasing computation time. Additionally, other procedures such as model calibration, which may require thousands of model iterations, can increase running time and thus further reduce rapid modeling and analysis. Using the widely-applied SWAT as an example, this study demonstrates how to parallelize a serial hydrological model in a Windows (R) environment using a parallel programing technology-Message Passing Interface (MPI). With a case study, we derived the optimal values for the two parameters (the number of processes and the corresponding percentage of work to be distributed to the master process) of the parallel SWAT (P-SWAT) on an ordinary personal computer and a work station. Our study indicates that model execution time can be reduced by 42%-70% (or a speedup of 1.74-3.36) using multiple processes (two to five) with a proper task-distribution scheme (between the master and slave processes). Although the computation time cost becomes lower with an increasing number of processes (from two to five), this enhancement becomes less due to the accompanied increase in demand for message passing procedures between the master and all slave processes. Our case study demonstrates that the P-SWAT with a five-process run may reach the maximum speedup, and the performance can be quite stable (fairly independent of a project size). Overall, the P-SWAT can help reduce the computation time substantially for an individual model run, manual and automatic calibration procedures, and optimization of best management practices. In particular, the parallelization method we used and the scheme for deriving the optimal parameters in this study can be valuable and easily applied to other hydrological or environmental models. Published by Elsevier Ltd.
引用
收藏
页码:124 / 132
页数:9
相关论文
共 50 条
  • [31] Heap architectures for concurrent languages using message passing
    Johansson, E
    Sagonas, K
    Wilhelmsson, J
    [J]. ACM SIGPLAN NOTICES, 2003, 38 (02) : 195 - 206
  • [32] A two-level parallelization method for distributed hydrological models
    Liu, Junzhi
    Zhu, A-Xing
    Qin, Cheng-Zhi
    Wu, Hui
    Jiang, Jingchao
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2016, 80 : 175 - 184
  • [33] Variational message passing
    Winn, J
    Bishop, CM
    [J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2005, 6 : 661 - 694
  • [34] Message Passing and Metabolism
    Parr, Thomas
    [J]. ENTROPY, 2021, 23 (05)
  • [35] Hydrological simulation using the SWAT model in a semi-arid region in the southern part of Zacatecas, Mexico
    Hernandez-Marin, Miguel Angel
    Ortiz-Gomez, Ruperto
    Zavala, Manuel
    Rodriguez-Rodriguez, Jose Antonio
    Medellin, Pedro Alvarado
    Ortiz-Robles, Fidel Alejandro
    [J]. ENVIRONMENTAL EARTH SCIENCES, 2024, 83 (18)
  • [36] Message Passing Neural Network Versus Message Passing Algorithm for Cooperative Positioning
    Tedeschini, Bernardo Camajori
    Brambilla, Mattia
    Nicoli, Monica
    [J]. IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2023, 9 (06) : 1666 - 1676
  • [37] Generalized Memory Approximate Message Passing for Generalized Linear Model
    Tian, Feiyan
    Liu, Lei
    Chen, Xiaoming
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2022, 70 : 6404 - 6418
  • [38] MESSAGE PASSING-BASED INFERENCE IN THE GAMMA MIXTURE MODEL
    Podusenko, Albert
    van Erp, Bart
    Bagaev, Dmitry
    Senoz, Ismail
    de Vries, Bert
    [J]. 2021 IEEE 31ST INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP), 2021,
  • [39] Sensitivity of performance prediction of message passing programs
    Girona, S
    Labarta, J
    [J]. INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED PROCESSING TECHNIQUES AND APPLICATIONS, VOLS I-V, PROCEEDINGS, 1999, : 620 - 626
  • [40] Hydrological modeling of Oued El Abiod watershed using the SWAT model
    Adel Bougamouza
    Boualem Remini
    [J]. Arabian Journal of Geosciences, 2022, 15 (13)