Parallelization of a hydrological model using the message passing interface

  • Authors:

  • Yiping Wu;Tiejian Li;Liqun Sun;Ji Chen

  • Affiliations:

  • ASRC Research and Technology Solutions, U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA and Department of Civil Engineering, The Uni ...;State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China;Department of Civil Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China;Department of Civil Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2013

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Abstract

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.