Abstract—Graphics Processing Units (GPUs) have enabled significant improvements in computational performance compared to traditional CPUs in several application domains. Until recently, GPUs have been programmed using C/C++ based methods such as CUDA (NVIDIA) and OpenCL (NVIDIA and AMD). Using these approaches, Fortran Numerical Weather Prediction (NWP) codes would have to be completely re-written to take full advantage of GPU performance gains. Emerging commercial Fortran compilers allow NWP codes to take advantage of GPU processing power with much less software development effort. The Nonhydrostatic Icosahedral Model (NIM) is a prototype dynamical core for global NWP. We use NIM to examine Fortran directive-based GPU compilers, evaluating code porting effort and computational performance.
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