• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.3
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• pp.189-197
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• 2022

With the development of high-performance computing technology and the advancement of numerical model, it is possible to predict the better weather forecasting. The purpose of this paper is the performance improvement for the SCAM climate model for the model running time excluding the compilation time. Therefore, the model previously performed using the Intel Fortran Compiler was changed to PGI Fortran Compiler. To this end, we reconfigure system environment variables, reset compilation options, install dependencies SW and library, and modify source code. In addition, we proposed and applied the 'PGI Compile with OpenACC' method. As a result, when the compiler was changed from intel to PGI, it led to an improvement of 6.08% in running time and when the openACC method was applied, it led to an improvement of 43.05% in running time. This demonstrates that the PGI Compile with OpenACC method proposed in this paper leads to excellent performance.

• Journal of Environmental Science International
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• v.27 no.8
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• pp.723-735
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• 2018

In this study, to investigate an optimal configuration method for the modeling system, we performed an optimization experiment by controlling the types of compilers and libraries, and the number of CPU cores because it was important to provide reliable model data very quickly for the national air quality forecast. We were made up the optimization experiment of twelve according to compilers (PGI and Intel), MPIs (mvapich-2.0, mvapich-2.2, and mpich-3.2) and NetCDF (NetCDF-3.6.3 and NetCDF-4.1.3) and performed wall clock time measurement for the WRF and CMAQ models based on the built computing resources. In the result of the experiment according to the compiler and library type, the performance of the WRF (30 min 30 s) and CMAQ (47 min 22 s) was best when the combination of Intel complier, mavapich-2.0, and NetCDF-3.6.3 was applied. Additionally, in a result of optimization by the number of CPU cores, the WRF model was best performed with 140 cores (five calculation servers), and the CMAQ model with 120 cores (five calculation servers). While the WRF model demonstrated obvious differences depending on the number of CPU cores rather than the types of compilers and libraries, CMAQ model demonstrated the biggest differences on the combination of compilers and libraries.