基于非结构网格隐式算法的GPU加速研究

针对非结构网格隐式算法在GPU上的加速效果不佳的问题，通过分析GPU的架构及并行模式，研究并实现了基于非结构网格格点格式的隐式LU-SGS算法的GPU并行加速.通过采用RCM和Metis网格重排序（重组）方法，优化非结构网格的数据局部性，改善非结构网格的隐式算法在GPU上的并行加速效果.通过三维机翼算例验证了本文实现的正确性及效率.结果表明两种网格重排序（重组）方法分别得到了63%和69%的加速效果提高.优化后的LU-SGS隐式GPU并行算法获得了相较于CPU串行算法27倍的加速比，充分说明了本文方法的高效性.

Research on GPU Acceleration of Implicit Schemes Based on Unstructured Grids

With regard to the poor acceleration performance on GPU using the unstructured grids implicit method, this study realizes the GPU acceleration of LU-SGS implicit method based on unstructured grids with the cell-vertex scheme. With introduce the architecture of a GPU and its parallelization method, two grid reordering methods are set forth based on RCM and METIS, to improve data locality of unstructured grids and to improve acceleration performance on GPU using the unstructured grids implicit method. The ONERA M6 Wing test case is carried out to verify and validate this implementation. With two grid reordering methods, the GPU implementations achieve 63% and 69% improvements respectively. The GPU implementation obtains a speedup of 27 times compared to the CPU version running on a single core. It indicates that the proposed GPU implementation has a solid performance.