  A high fidelity flow simulation for complex geometries for high Reynolds
number ($Re$) flow is still very challenging, which requires more powerful
computational capability of HPC system. However, the development of HPC with
traditional CPU architecture suffers bottlenecks due to its high power
consumption and technical difficulties. Heterogeneous architecture computation
is raised to be a promising solution of difficulties of HPC development. GPU
accelerating technology has been utilized in low order scheme CFD solvers on
structured grid and high order scheme solvers on unstructured meshes. The high
order finite difference methods on structured grid possess many advantages,
e.g. high efficiency, robustness and low storage, however, the strong
dependence among points for a high order finite difference scheme still limits
its application on GPU platform. In present work, we propose a set of
hardware-aware technology to optimize the efficiency of data transfer between
CPU and GPU, and efficiency of communication between GPUs. An in-house
multi-block structured CFD solver with high order finite difference methods on
curvilinear coordinates is ported onto GPU platform, and obtain satisfying
performance with speedup maximum around 2000x over a single CPU core. This work
provides efficient solution to apply GPU computing in CFD simulation with
certain high order finite difference methods on current GPU heterogeneous
computers. The test shows that significant accelerating effects can been
achieved for different GPUs.
