GPU Acceleration of a Non-Hydrostatic Ocean Model Using a Mixed Precision Multigrid Preconditioned Conjugate Gradient Method
Authors: Takateru Yamagishi (Research Organization for Information Science and Technology), Yoshimasa Matsumura (Hokkaido University)
Abstract: To meet the demand for fast and detailed calculations in numerical ocean simulation, we implemented a non-hydrostatic ocean model on the GPU, using basic and essential methods such as exploitation of large-scale parallelism in threads and instructions, coalesced access to the global memory, and minimization of memory transfer between the CPU host and GPU device. We also studied and evaluated the application of mixed precision calculation to the multigrid preconditioning of a Poisson/Helmholtz solver, which would deteriorate in performance due to the small number of threads in the multigrid method. The GPU-implemented model ran 4.3 times faster on NVIDIA K20C than on Fujitsu SPARC64 VIIIfx. The application of mixed precision achieved a 16% acceleration of the Poisson/Helmholtz solver, which is consistent with the decrease of global memory transfer due to the switch to mixed precision. No specific errors were found in the outputs.
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