A two-level parallelization method for distributed hydrological models. (June 2016)

• Record Type: Journal Article
• Authors: Liu, Junzhi; Zhu, A-Xing; Qin, Cheng-Zhi; Wu, Hui; Jiang, Jingchao
• Is Part Of: Environmental modelling & software. Volume 80(2016:Jun.)
• Page Start: 175
• Page End: 184
• Publication Date: 2016-06
• Subjects: Distributed hydrological model -- Two-level parallelization -- Multi-core cluster -- Sub-basin -- Basic simulation unit; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.02.032
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper proposes a scalable two-level parallelization method for distributed hydrological models that can use parallelizability at both the sub-basin level and the basic simulation-unit level (e.g., grid cell) simultaneously. This approach first uses the message-passing programming model to dispatch parallel tasks at the sub-basin level to different nodes with multi-core CPUs in the cluster. Each node is responsible for some of the sub-basins. Parallel tasks for each sub-basin at the basic simulation-unit level are then dispatched to multiple cores within each node using the shared-memory programming model. A grid-based distributed hydrological model was parallelized to demonstrate the performance of the proposed method, which was tested in different scenarios (e.g., different data volume, different numbers of sub-basins). Results show that the proposed two-level parallelization method had better scalability than the parallel computation at sub-basin level alone, and the parallel performance increased with data volume and the number of sub-basins. Highlights: Two-level parallel-computing was conducted for a distributed hydrological model. The parallelizability at both sub-basin and basic simulation unit level was used. Hybrid parallel computing was conducted on multi-core clusters. A grid-based model was parallelized to illustrate the method. Two-level parallel computation has good scalability.