Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (1): 105-116.

doi: 10.11871/jfdc.issn.2096-742X.2020.01.009

Special Issue: “高性能与高通量计算及应用”专刊

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Direct Numerical Simulation of Hypersonic Turbulence Based on CPU/GPU Heterogeneous System Architecture

Dang Guanlin1,4,Liu Shiwei3,4,Hu Xiaodong2,Zhang Jian2,Li Xinliang1,4,*()   

  1. 1.Institute of Mechanics,Chinese Academy of Sciences, Beijing 100190, China
    2.Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    3.Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
    4.University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-12-06 Online:2020-02-20 Published:2020-03-28
  • Contact: Li Xinliang


[Objective] The direct numerical simulation (DNS) of hypersonic turbulence requires great many grids points and time steps. Therefore, the amount of calculation is very large. Excessively long time of calculation is an important reason that DNS cannot be applied in real applications. In order to accelerate the calculation, design of a high-performance computational fluid mechanics program OpenCFD-SCU under the CPU/GPU Heterogeneous System Architecture (HSA) is introduced in this paper. [Method] This program is based on the CPU [fortran] code OpenCFD-SC which is a high-precision finite difference solver developed by the authors. OpenCFD-SCU has the same program framework as OpenCFD-SC, and the computing part of the GPU program is programmed by CUDA to ensure that all arithmetic operations are completed on the GPU. [Results] In a same DNS task, the GPU version of OpenCFD-SCU is 60 times faster than the CPU version of OpenCFD-SC. The computing power of GPU is much higher than that of CPU. Using GPU can effectively accelerate the calculation, which is the future trend of DNS programs for hypersonic turbulence. [Conclusion] In the future, we believe that more and more hypersonic turbulence simulation can be moved to the GPU.

Key words: hypersonic turbulence, DNS, GPU