利用CUDA Fortran语言发展了基于图形处理器（GPU）的计算流体力学可压缩湍流求解器。该求解器基于结构网格有限体积法，空间离散采用AUSMPW＋格式，湍流模型为k－ωSST两方程模型，采用MPI实现并行计算。针对最新的GPU架构，讨论了通量计算的优化方法及GPU计算与PCIe数据传输、MPI通信重叠的多GPU并行算法。进行了超声速进气道及空天飞机等算例的数值模拟以验证GPU 在大网格量情况下的加速性能。计算结果表明：相对于Intel Xeon E5－2670 CPU 单一核心的计算时间，单块 NVIDIA GTX Titan Black GPU可获得107～125倍的加速比。利用四块GPU 实现了复杂外形1．34亿网格的快速计算，并行效率为91．6％。%Based on CUDA Fortran for compressible turbulence simulations,a finite volume computational fluid dynamics solver on the GPU (Graphical Processing Unit)was developed.The solver was implemented with an AUSMPW+scheme for the spatial dispersion,the k-ωSST model for turbulence model,and MPI communication for parallel computing.Some optimization strategies for fluxes computation and multi-GPU parallel algorithms for overlap of PCIe data transfer and MPI communication with GPU computation have been discussed for the latest generation GPU architecture.Several test cases,such as a supersonic inlet and a space shuttle were chosen to demonstrate the acceleration performance of GPU on large-scale grid size.Results show that when using a NVIDIA GTX Titan Black GPU,the computational expense can be reduced by 107~125 times than using a single core of an Intel Xeon E5 -2670 CPU.Fast computing for a complex configuration with 0.134 billion grid sizes has been achieved by using 4 GPUs and the parallel efficiency is 91.6%.
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