GPU-Accelerated Boussinesq Model Using Compute Unified Device Architecture FORTRAN

Boram Kim, Chanyoung Oh, Youngmin Yi, Dae Hong Kim

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Graphic Processing Units (GPU) have a number of arithmetic units and their associated structures specialized for graphic processes make the computational performances much faster than CPU (Central Processing Units). In these days, many numerical models implemented by FORTRAN have been applied on real field scale problems, which requires huge computational resources and simulation time as well. In this study, a GPU version of Boussinesq equation model was implemented using the Compute Unified Device Architecture (CUDA) FORTRAN. The computed results of the GPU-CUDA FORTRAN Boussinesq model were verified by comparing with the computed result of a CPU based Boussinesq model that had been already verified for many benchmark tests. Exact agreements except round off magnitude have been observed from the comparison. The GPU-CUDA FORTRAN Boussinesq model showed about 20 times faster computational time compared with the CPU based code. In addition, as the computational domain becomes larger, the computational efficiency of GPU-CUDA FORTRAN version over the CPU version more increased.

Original languageEnglish
Pages (from-to)1176-1180
Number of pages5
JournalJournal of Coastal Research
StatePublished - 1 May 2018


  • Boussinesq equation
  • CUDA
  • GPU
  • Parallelization


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