An efficient communication strategy for massively parallel computation in CFD

With the development of high-performance computers, it is necessary to develop efficient parallel algorithms in the field of computational fluid dynamics (CFD). In this study, a novel parallel communication strategy based on asynchronous and packaged communication is proposed. The strategy implements an aggregated communication process, which requires only one communication in each iteration step, significantly reducing the number of communications. The correctness and convergence of the novel strategy are demonstrated from both theoretical and experimental perspectives. And based on the real vehicle CHN-T model with 140 million meshes, a detailed performance comparison and analysis is performed for the novel strategy and the traditional strategy, showing that the novel strategy has significant advantages in terms of scalability. Finally, the strong scalability and weak scalability tests are carried out separately for the CHN-T model. The strong scaling efficiency can reach 74% with 10.5 billion meshes and 256,000 cores. The weak scaling parallel efficiency can reach 90% with 10 billion meshes and 179,000 cores. This research work has laid an important foundation for the development of the fast design of aircraft and cutting-edge numerical methods.

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Data Availibility Statement

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Shang J (2004) Three decades of accomplishments in computational fluid dynamics. Progr Aerosp Sci 40(3):173–197 ArticleGoogle Scholar
  2. Spalart PR, Venkatakrishnan V (2016) On the role and challenges of CFD in the aerospace industry. Aeronaut J 120(1223):209–232 ArticleGoogle Scholar
  3. Witherden FD, Jameson A (2017) Future directions in computational fluid dynamics. In: 23rd AIAA Computational Fluid Dynamics Conference, p. 3791
  4. Witherden FD, Jameson A (2017) Future directions in computational fluid dynamics. In: 23rd AIAA Computational Fluid Dynamics Conference
  5. Spalart PR (2000) Strategies for turbulence modelling and simulations. Int J Heat Fluid Flow 21(3):252–263 ArticleGoogle Scholar
  6. Top 500 supercomputer sites; http://www.top500.org/
  7. Slotnick J, Alonso J et al (2014) CFD vision 2030 study: A path to revolutionary computational aerosciences [R]. NASA/CR, 2014-218178
  8. Al Farhan MA, Kaushik DK, Keyes DE (2016) Unstructured computational aerodynamics on many integrated core architecture. J Supercomput 59:97–118 MathSciNetGoogle Scholar
  9. Duran A, Celebi MS, Piskin S, Tuncel M (2015) Scalability of OpenFOAM for bio-medical flow simulations. J Supercomput 71(3):938–951 ArticleGoogle Scholar
  10. Economon TD, Mudigere D, Bansal G, Heinecke A, Palacios F, Park J, Smelyanskiy M, Alonso JJ, Dubey P (2016) Performance optimizations for scalable implicit rans calculations with su2. Comput Fluids 129:146–158 ArticleMathSciNetMATHGoogle Scholar
  11. Jin H, Jespersen D, Mehrotra P, Biswas R, Huang L, Chapman B (2011) High performance computing using MPI and OpenMP on multi-core parallel systems. Parallel Comput 37(9):562–575 ArticleGoogle Scholar
  12. Lee S, Gounley J, Randles A, Vetter JS (2019) Performance portability study for massively parallel computational fluid dynamics application on scalable heterogeneous architectures. J Parallel Distrib Comput 129:1–13 ArticleGoogle Scholar
  13. Xue W, Jackson CW, Roy CJ (2021) An improved framework of GPU computing for CFD applications on structured grids using OpenACC. J Parallel Distribut Comput 156:64–85 ArticleGoogle Scholar
  14. Wang Y, Yan X, Zhang J (2021) Research on GPU parallel algorithm for direct numerical solution of two-dimensional compressible flows. J Supercomput 77:1–21 ArticleGoogle Scholar
  15. Kissami I, Cerin C, Benkhaldoun F, Scarella G (2021) Towards parallel CFD computation for the adapt framework. Springer, Cham Google Scholar
  16. Shang Z (2013) Large-scale CFD parallel computing dealing with massive mesh. J Eng 2013:1–6 ArticleGoogle Scholar
  17. Zhong ZHAO (2020) Design of general CFD software PHengLEI. Comput Eng Sci 42(2):210–219 Google Scholar
  18. Zhong ZHAO (2019) PHengLEI: a large scale parallel CFD framework for arbitrary grids. Chin J Comput 42(11):2368–2383 Google Scholar
  19. Roe PL (1981) Approximate Riemann solvers, parameter vectors, and difference schemes. J Comput Phys 43(2):357–372 ArticleMathSciNetMATHGoogle Scholar
  20. Venkatakrishnan V (1995) Convergence to steady state solutions of the Euler equations on unstructured grids with limiters. J Comput Phys 118(1):120–130 ArticleMATHGoogle Scholar
  21. George Karypis, Vipin Kumar (1998) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20(1):359–92 ArticleMathSciNetMATHGoogle Scholar
  22. Yuntao W, Gang L, Zuobin C (2019) Summary of the first aeronautical computational fluid dynamics Redibility workshop. Acta Aerodyn Sinica 37(2):247–261 Google Scholar

Acknowledgements

This paper was supported by the National Key Research and Development Program of China(2017YFB0202104), the National Key Research and Development Program of China(2018YFB0204301), National Numerical Windtunnel(NNW) Project, and the national supercomputer center in JiNan.

Funding

This study was funded by National Key Research and Development Program of China(2017YFB0202104, 2018YFB0204301) and National Numerical Windtunnel(NNW) Project.

Author information

Authors and Affiliations

  1. Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, 410073, HuNan, China YunBo Wan & Jie Liu
  2. Laboratory of Software Engineering for Complex Systems, National University of Defense Technology, Changsha, 410073, Hunan, China Jie Liu
  3. China Aerodynamics Research and Development Center, Computational Aerodynamics Institute, Mianyang, 621000, Sichuan, China YunBo Wan, Lei He, Yong Zhang, Zhong Zhao & HaoYuan Zhang
  1. YunBo Wan