面向GPU的非结构网格有限体积计算流体力学的图染色方法优化

采用图染色方法解决通量累加和局部最大压力计算引起的两种典型资源竞争问题,并通过共享内存的使用、体编号和面编号的重排、面数据的重排三种策略优化图染色方法。针对应用在空气动力学多种规模的三维网格,分别采用双精度和单精度操作数,在Nvidia Tesla V100和K80 GPU上,展开性能测试。结果表明:共享内存的优化效果不明显；体编号和面编号重排降低了图染色方法的计算性能；面数据重排可以有效地优化图染色方法；计算性能在V100上提高20%左右,在K80上提高15%左右。

Optimizations of graph coloring method for unstructured finite volume computational fluid dynamics on GPU

Graph coloring was used to address resource competition for the two typical computing procedures, including the flux summation and the calculation of local maximum pressure. There were three optimizations applied on graph coloring including shared memory, the reordering of volume and face indices, and the reordering of face variables. The 3D aerodynamics application with a series of mesh sizes was used in the performance test by double and single precision floating point operations on GPU Nvidia Tesla V100 and K80. The performance tests show that the shared memory is not obvious in performance. Furthermore, the reorder of volume and face indices reduces the performance of graph coloring.It is found that the reorder of face variables can increase performance remarkably. Specifically, the performance of graph coloring is increased by around 20% on V100 and 15% on K80.