UEB parallel: Distributed snow accumulation and melt modeling using parallel computing. (March 2020)

• Record Type: Journal Article
• Authors: Gichamo, Tseganeh Z.; Tarboton, David G.
• Is Part Of: Environmental modelling & software. Volume 125(2020)
• Publication Date: 2020-03
• Subjects: Utah Energy Balance snowmelt model (UEB) -- Message Passing Interface (MPI) -- Compute Unified Device Architecture (CUDA) -- Graphics Processing Unit (GPU) -- Parallel IO; 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.2019.104614
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The Utah Energy Balance (UEB) model supports gridded simulation of snow processes over a watershed. To enhance computational efficiency, we developed two parallel versions of the model, one using the Message Passing Interface (MPI) and the other using NVIDIA's CUDA code on Graphics Processing Unit (GPU). Evaluation of the speed-up and efficiency of the MPI version shows that the effect of input/output (IO) operations on the parallel model performance increases as the number of processor cores increases. As a result, although the computation kernel scales well with the number of cores, the efficiency of the parallel code as a whole degrades. The performance improves when the number of IO operations is reduced by reading/writing larger data arrays. The CUDA GPU implementation was done without major refactoring of the original UEB code, and tests demonstrated that satisfactory performance could be obtained without a major re-work of the existing UEB code. Highlights: The Utah Energy Balance snowmelt model computational kernel is highly parallelizable. Input/Output was the major efficiency bottleneck in parallelization. GPU implementation was achieved without major refactoring of existing UEB code.