Abstract
To develop high performance computing methods for compressible flow calculation, a GPU-accelerated compressible flow solver is developed with Compute Unified Device Architecture (CUDA). The WENO5 scheme is adopted for spatial discretization, and the third-order Runge-Kutta scheme is used for time discretization. According to the algorithm and programming model, the heterogeneous computing method of the solver is designed. Different kernels are designed to implement different computing functions, and shared memory is used for time-advanced computations. The solver is verified by the one-dimensional shock tube case, and a good acceleration effect is obtained with the increase of the grid size. And the impact of execution configuration on kernel performance was investigated. When the block size is reduced under different grid sizes, the speedup changes in the same way, but the performance parameters change differently.
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