Inverse Preconditioning Techniques on a GPUs Architecture in Global Ocean Models

In this paper we prove how the use of the GPU NVIDIA architectures could improve the performance of the iterative solvers in the numerical problems related to the global ocean circulation models. In this context a linear system, obtained from the elliptical core of an ocean model by means of finite differences, is solved using an implementation of the Preconditioned Conjugate Gradient (PCG) on Graphic Processor Units (GPUs). Furthermore, because of the slow convergence of the iterative solver, when the sizes of the grids grow and the domain of the ocean model includes specific places of the Earth, then the preconditioning technique of the linear system, represented by the diagonal preconditioner, is investigated. Here we show how the combination of a more efficient preconditioning technique with the GPU computing is able to accelerate the iterative solvers of these ocean models, giving the possibility to go further in the simulations.