This study presents an efficient Boussinesq-type wave model accelerated by a single Graphics Processing Unit(GPU).The model uses the hybrid finite volume and finite difference method to solve weakly dispersive and non...This study presents an efficient Boussinesq-type wave model accelerated by a single Graphics Processing Unit(GPU).The model uses the hybrid finite volume and finite difference method to solve weakly dispersive and nonlinear Boussinesq equations in the horizontal plane,enabling the model to have the shock-capturing ability to deal with breaking waves and moving shoreline properly.The code is written in CUDA C.To achieve better performance,the model uses cyclic reduction technique to solve massive tridiagonal linear systems and overlapped tiling/shared memory to reduce global memory access and enhance data reuse.Four numerical tests are conducted to validate the GPU implementation.The performance of the GPU model is evaluated by running a series of numerical simulations on two GPU platforms with different hardware configurations.Compared with the CPU version,the maximum speedup ratios for single-precision and double-precision calculations are 55.56 and 32.57,respectively.展开更多
基金The National Key Research and Development Program under contract No.2019YFC1407700the National Natural Science Foundation of China under contract Nos 51779022, 52071057 and 51809053。
文摘This study presents an efficient Boussinesq-type wave model accelerated by a single Graphics Processing Unit(GPU).The model uses the hybrid finite volume and finite difference method to solve weakly dispersive and nonlinear Boussinesq equations in the horizontal plane,enabling the model to have the shock-capturing ability to deal with breaking waves and moving shoreline properly.The code is written in CUDA C.To achieve better performance,the model uses cyclic reduction technique to solve massive tridiagonal linear systems and overlapped tiling/shared memory to reduce global memory access and enhance data reuse.Four numerical tests are conducted to validate the GPU implementation.The performance of the GPU model is evaluated by running a series of numerical simulations on two GPU platforms with different hardware configurations.Compared with the CPU version,the maximum speedup ratios for single-precision and double-precision calculations are 55.56 and 32.57,respectively.
