An incompressible flow solver on a GPU/CPU heterogeneous architecture parallel computing platform  

作　　者：Qianqian Li Rong Li Zixuan Yang 

机构地区：[1]The State Key Laboratory of Nonlinear Mechanics,Institute of Mechanics,Chinese Academy of Sciences,Beijing 100190,China [2]School of Engineering Sciences,University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Theoretical & Applied Mechanics Letters》2023年第5期387-393,共7页力学快报（英文版）

基　　金：supported by the National Natural Science Foundation of China (NSFC);Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics’(Grant No. 11988102);NSFC project (Grant No. 11972038)

摘　　要：A computational fluid dynamics(CFD)solver for a GPU/CPU heterogeneous architecture parallel computing platform is developed to simulate incompressible flows on billion-level grid points.To solve the Poisson equation,the conjugate gradient method is used as a basic solver,and a Chebyshev method in combination with a Jacobi sub-preconditioner is used as a preconditioner.The developed CFD solver shows good performance on parallel efficiency,which exceeds 90%in the weak-scalability test when the number of grid points allocated to each GPU card is greater than 2083.In the acceleration test,it is found that running a simulation with 10403 grid points on 125 GPU cards accelerates by 203.6x over the same number of CPU cores.The developed solver is then tested in the context of a two-dimensional lid-driven cavity flow and three-dimensional Taylor-Green vortex flow.The results are consistent with previous results in the literature.

关 键 词：GPU Acceleration Parallel computing Poisson equation PRECONDITIONER 

分 类 号：O35[理学—流体力学] TP338.6[理学—力学]