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ARS Home » Southeast Area » Oxford, Mississippi » National Sedimentation Laboratory » Watershed Physical Processes Research » Research » Publications at this Location » Publication #307908
Title: Parallelized CCHE2D flow model with CUDA Fortran on Graphics Process Units

Author
item ZHANG, Y - University Of Mississippi
item JIA, Y - University Of Mississippi

Submitted to: Computers & Fluids
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2013
Publication Date: 7/1/2013
Citation: Zhang, Y., Jia, Y. 2013. Parallelized CCHE2D flow model with CUDA Fortran on Graphics Process Units. Computers & Fluids. 84:359-368. Available: www.journals.elsevier.com/computers-and-fluids/

Interpretive Summary: This paper presents the CCHE2D implicit flow model parallelized using CUDA Fortran programming technique on Graphics Processing Units (GPUs). A parallelized implicit Alternating Direction Implicit (ADI) solver using Parallel Cyclic Reduction (PCR) algorithm on GPU is developed and tested. This solver outperforms the Strong Implicit Procedure (SIP) solver and its parallel alternatives. Computing accuracy and efficiency of both CPU and GPU versions of models were compared with one experimental case and one field case. It has been demonstrated that the parallelized CCHE2D flow model with CUDA Fortran is capable of accurately predicting steady flow or unsteady flow with a much higher computing efficiency on the GPU. The parallelized CCHE2D model was validated using one experimental case, which has proved its consistency and higher efficiency compared to the serial version. The parallelized CCHE2D model has also been successfully applied to a long term, large scale unsteady flow simulation. Satisfactory results on a series of refined meshes were obtained in significantly shorter time.

Technical Abstract: This paper presents the CCHE2D implicit flow model parallelized using CUDA Fortran programming technique on Graphics Processing Units (GPUs). A parallelized implicit Alternating Direction Implicit (ADI) solver using Parallel Cyclic Reduction (PCR) algorithm on GPU is developed and tested. This solver outperforms the Strong Implicit Procedure (SIP) solver and its parallel alternatives. Computing accuracy and efficiency of both CPU and GPU versions of models were compared with one experimental case and one field case. It has been demonstrated that the parallelized CCHE2D flow model with CUDA Fortran is capable of accurately predicting steady flow or unsteady flow with a much higher computing efficiency on the GPU.