A high fidelity flow simulation for complex geometries for high Reynolds number(Re)flow is still very challenging,requiring a more powerful HPC system.However,the development of HPC with traditional CPU architecture s...A high fidelity flow simulation for complex geometries for high Reynolds number(Re)flow is still very challenging,requiring a more powerful HPC system.However,the development of HPC with traditional CPU architecture suffers bottlenecks due to its high power consumption and technical difficulties.Heterogeneous architecture computation is raised to be a promising solution to the challenges of HPC development.GPU accelerating technology has been utilized in low order scheme CFD solvers on the structured grid and high order scheme solvers on unstructured meshes.The high-order finite difference methods on structured grids possess many advantages,e.g.,high efficiency,robustness,and low storage.However,the strong dependence among points for a high-order finite difference scheme still limits its application on the GPU platform.In the present work,we propose a set of hardware-aware technology to optimize data transfer efficiency between CPU and GPU,as well as communication efficiency among GPUs.An in-house multi-block structured CFD solver with high order finite difference methods on curvilinear coordinates is ported onto the GPU platform and obtains satisfying performance with a speedup maximum of around 2000x over a single CPU core.This work provides an efficient solution to apply GPU computing in CFD simulation with specific high order finite difference methods on current GPU heterogeneous computers.The test shows that significant accelerating effects can be achieved for different GPUs.展开更多
基金National Numerical Windtunnel Project,the National Natural Science Foundation projects(91952103,11772323,11621202)Fundamental Research Funds for the Central Universities.
文摘A high fidelity flow simulation for complex geometries for high Reynolds number(Re)flow is still very challenging,requiring a more powerful HPC system.However,the development of HPC with traditional CPU architecture suffers bottlenecks due to its high power consumption and technical difficulties.Heterogeneous architecture computation is raised to be a promising solution to the challenges of HPC development.GPU accelerating technology has been utilized in low order scheme CFD solvers on the structured grid and high order scheme solvers on unstructured meshes.The high-order finite difference methods on structured grids possess many advantages,e.g.,high efficiency,robustness,and low storage.However,the strong dependence among points for a high-order finite difference scheme still limits its application on the GPU platform.In the present work,we propose a set of hardware-aware technology to optimize data transfer efficiency between CPU and GPU,as well as communication efficiency among GPUs.An in-house multi-block structured CFD solver with high order finite difference methods on curvilinear coordinates is ported onto the GPU platform and obtains satisfying performance with a speedup maximum of around 2000x over a single CPU core.This work provides an efficient solution to apply GPU computing in CFD simulation with specific high order finite difference methods on current GPU heterogeneous computers.The test shows that significant accelerating effects can be achieved for different GPUs.
