Climate change could have a critical impact on the Republic of Panama where a major segment of the economy is dependent
on the operation of the Panama Canal. New capabilities to do targeted research around climate change impacts on Panama is
therefore being established. This includes anew GPU-cluster infrastructure called Iberogun, based around 2 DGX1 servers
(each running 16 NVIDIA Tesla P100 GPUs). This infrastructure will be used to evaluate potential climate models and models
of extreme weather events. In this review we therefore present an evaluation of the GPGPU (General Purpose Graphic
Processing Unit, here abbreviated GPU) implementation methods for the study of weather projections and dynamical
downscaling in the Republic of Panama. Different methods are discussed, including: domain-specific languages (DSLs),
directive-based porting methods, granularity optimization methods, and memory layout transforming methods. One of these
approaches that has yielded interesting previous results is further discussed, a directive-based code transformation method
called ‘Hybrid Fortran’ that permits a high-performance GPU port for arranged lattice Fortran codes. Finally, we suggest a
method akin to previous investigations related to climate change done for the Republic of Panama, but with acceleration via
GPU capabilities.
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