Interactive 3D Representation as a Method of Investigating Information Graph Features

  • Alexander AntonovEmail author
  • Nikita Volkov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 965)


An algorithm information graph is a structure of wide variety. It can tell a lot about algorithm features, such as computational complexity and resource of parallelism, as well as about sequential operations blocks within an algorithm. Graphs of different algorithms often share similar regular structures — their presence is an indicator of potentially similar algorithm behavior. Convenient, interactive 3D representation of an information graph is a decent method of researching it; it can demonstrate algorithm characteristics listed above and its structural features. In this article we investigate an approach to creating such representations, implement it using our AlgoView system and give examples of using a resulting tool.


Information graph Parallelism AlgoWiki AlgoView Level parallel form 



The results described in Sects. 1, 2 and 4 were obtained in Lomonosov Moscow State University with the financial support of the Russian Science Foundation (Agreement № 14–11–00190). The research is carried out using the equipment of the shared research facilities of HPC computing resources at Lomonosov Moscow State University supported by the project RFMEFI62117X0011.


  1. 1.
    Voevodin, V., Voevodin, Vl.: Parallel Computing. BHV-Petersburg, St. Petersburg (2002)Google Scholar
  2. 2.
    Hu, Y.I., Shi, L.: Visualizing large graphs. WIREs Comput. Stat. 7(2), 115–136 (2015). Scholar
  3. 3.
    Gold, R.: Control flow graphs and code coverage. Appl. Math. Comput. Sci. 20(4), 739–749 (2010). Scholar
  4. 4.
    Voevodin, V., Antonov, A., Dongarra, J.: AlgoWiki: an open encyclopedia of parallel algorithmic features. Supercomput. Front. Innovations 2(1), 4–18 (2015). Scholar
  5. 5.
    Voevodin, V., Antonov, A., Dongarra, J.: Why is it hard to describe properties of algorithms? Procedia Comput. Sci. 101, 4–7 (2016). Scholar
  6. 6.
    Open Encyclopedia of Parallel Algorithmic Features. Accessed 13 Apr 2018
  7. 7.
    Antonov, A., Teplov, A.: Generalized approach to scalability analysis of parallel applications. In: Carretero, J. (ed.) ICA3PP 2016. LNCS, vol. 10049, pp. 291–304. Springer, Cham (2016). Scholar
  8. 8.
    Antonov, A., Voevodin, V., Voevodin, Vl., Teplov, A.: A study of the dynamic characteristics of software implementation as an essential part for a universal description of algorithm properties. In: 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing Proceedings, pp. 359–363, 17–19 February 2016.
  9. 9.
    Antonov, A.S., Volkov, N.I.: An algoview web-visualization system for the AlgoWiki project. In: Sokolinsky, L., Zymbler, M. (eds.) PCT 2017. CCIS, vol. 753, pp. 3–13. Springer, Cham (2017). Scholar
  10. 10.
    Householder (reflections) method for the QR decomposition of a square matrix, real point-wise version. square_matrix,_real_point-wise_version. Accessed 13 Apr 2018
  11. 11.
    Givens method. Accessed 13 Apr 2018
  12. 12.
    Dense matrix multiplication (serial version for real matrices). Accessed 13 Apr 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Lomonosov Moscow State UniversityMoscowRussia

Personalised recommendations