Automated Construction of High Performance Distributed Programs in LuNA System

  • Darkhan Akhmed-Zaki
  • Danil Lebedev
  • Victor Malyshkin
  • Vladislav PerepelkinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11657)


The paper concerns the problem of efficient distributed execution of fragmented programs in LuNA system, which is a automated parallel programs construction system. In LuNA an application algorithm is represented with a high-level programming language, which makes the representation portable, but also causes the complex problem of automatic construction of an efficient distributed program, which implements the algorithm on given hardware and data. The concept of adding supplementary information (recommendations) is employed to direct the process of program construction based on user knowledge. With this approach the user does not have to program complex distributed logic, while the system makes advantage of the user knowledge to optimize program and its execution. Implementation of this concept within LuNA system is concerned. In particular, a conventional compiler is employed to optimize the generated code. Some performance tests are conducted to compare efficiency of the approach with both previous LuNA release and reference hand-coded MPI implementation performance.


Automated parallel programs construction Fragmented programming technology LuNA system 


  1. 1.
    ANSYS Fluent Web Page. Accessed 01 Apr 2019
  2. 2.
    Phillips, J., et al.: Scalable molecular dynamics with NAMD. J. Comput. Chem. 26, 1781–1802 (2005)CrossRefGoogle Scholar
  3. 3.
    MathWorks MATLAB official web-site. Accessed 01 Apr 2019
  4. 4.
    GNU Octave Web Site. Accessed 01 Apr 2019
  5. 5.
    WOLFRAM MATHEMATICA Web Site. Accessed 01 Apr 2019
  6. 6.
    Robson, M., Buch, R., Kale, L.: Runtime coordinated heterogeneous tasks in Charm++. In: Proceedings of the Second International Workshop on Extreme Scale Programming Models and Middleware (2016)Google Scholar
  7. 7.
    Wu, W., Bouteiller, A., Bosilca, G., Faverge, M., Dongarra, J.: Hierarchical DAG scheduling for hybrid distributed systems. In: 29th IEEE International Parallel and Distributed Processing Symposium (2014)Google Scholar
  8. 8.
    Bauer, M., Treichler, S., Slaughter, E., Aiken, A.: Legion: expressing locality and independence with logical regions. In: The International Conference on Supercomputing (SC 2012) (2012)Google Scholar
  9. 9.
    Malyshkin, V.E., Perepelkin, V.A.: LuNA fragmented programming system, main functions and peculiarities of run-time subsystem. In: Malyshkin, V. (ed.) PaCT 2011. LNCS, vol. 6873, pp. 53–61. Springer, Heidelberg (2011). Scholar
  10. 10.
    Sterling, T., Anderson, M., Brodowicz, M.: A survey: runtime software systems for high performance computing. Supercomput. Front. Innovations Int. J. 4(1), 48–68 (2017). Scholar
  11. 11.
    Thoman, P., Dichev, K., Heller, T., et al.: A taxonomy of task-based parallel programming technologies for high-performance computing. J. Supercomputing 74(4), 1422–1434 (2018). Scholar
  12. 12.
    Valkovsky, V., Malyshkin, V.: Synthesis of Parallel Programs and Systems on the Basis of Computational Models. Nauka, Novosibirak (1988)Google Scholar
  13. 13.
    Akhmed-Zaki, D., Lebedev, D., Perepelkin, V.: J. Supercomput. (2018).
  14. 14.
    Sapronov, I., Bykov, A.: Parallel pipelined algorithm. Atom 2009, no. 44, pp. 24–25 (2009). (in Russian)Google Scholar
  15. 15.
    Joint Supercomputing Centre of Russian Academy of Sciences Official Site. Accessed 01 Apr 2019

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Al-Farabi Kazakh National UniversityAlmatyKazakhstan
  2. 2.Institute of Computational Mathematics and Mathematical Geophysics SB RASNovosibirskRussia
  3. 3.Novosibirsk State UniversityNovosibirskRussia
  4. 4.Novosibirsk State Technical UniversityNovosibirskRussia

Personalised recommendations