Organization and study of cluster computing systems with functional architecture determined by executable models. Automata executable models of information processing

Objectives. An urgent task is to improve the functional architecture of cluster computing systems by introducing methodologies for creating software at the applied and intermediate levels based on formalized specifications. One such methodology is based on the use of automatic specifications for computer systems software. The complexity of resolving the problem is caused by the branching of the algorithms built, as well as the presence of cyclic sections. The execution time of the branched sections of the program and the number of cycles run depends on the type of conditions entered. In practice it can be determined using a detailed simulation model and analysis of the control program created on its basis. The aim of the work is to find approaches to the definition of functional architecture which can be applied practically at the main levels of the subject orientation of cluster computing systems.

Methods. The methods proposed and used are based on the concept of organization and research of cluster-type computing systems with a functional architecture as defined by executable automatic models.

Results. The paper proposes methods of constructing automatic and logical-probabilistic models of cluster computing systems and creating software tools based on them. The concept of the logical-probabilistic model “temporal probabilistic system of canonical equations (CES)” is introduced. This enables a visual formalization to be obtained, as well as implementation of automatic models and work programs typical for cluster and other applications. It also significantly reduced the number of “incremental” additions when enumerating discrete time moments. The main feature of the new logical-probabilistic model is the preservation of the original CES in its basis.

Conclusions. The work concludes that the choice of the system and functional architecture of a computing cluster should be determined not so much by the peak characteristics of the communication equipment specified by the manufacturer, as by the actual indicators achieved at the level of user applications and cluster usage modes. It is also shown that executable automatic models can be applied at almost all levels of cluster computing systems subject orientation.

1. Voevodin V.V., Voevodin Vl.V. Parallel’nye vychisleniya (Parallel Computing). St. Petersburg: BHV-Petersburg; 2002. 608 p. (in Russ.).

2. Pleiter D. Supercomputer Architectures: Current State and Future Trends. The AQTIVATE Project, European Union’s HORIZON MSCA Doctoral Networks Programmer, Grant Agreement No. 101072344. September 2023. 38 p.

3. Boldyrev A., Ratnikov F., Shevelev A. Approach to Finding a Robust Deep Learning Model. IEEE Access. 2025;13: 102390–102406. https://doi.org/10.1109/ACCESS.2025.3578926, https://doi.org/10.48550/arXiv.2505.17254

4. Mishenin R.M., Kostenetskii P.S. Modeling the task flow of the HSE computing cluster using SLURM Simulator. In: Parallel Computing Technologies (PCT’2025): Proceedings of the 19th All-Russian Scientific Conference with International Participation. Moscow; 2025. P. 324 (in Russ.).

5. Promyslov G., Efremov A., Ilyasov Y., Pisarev V., Timofeev A. Efficiency of Machine Learning Tasks on HPC Devices. In: Parallel Computing Technologies (PCT’2025): Proceedings of the 19th All-Russian Scientific Conference with International Participation. Moscow; 2025. P. 56–81 (in Russ.).

6. Kostenetskiy P.S., Kozyrev V.I., Chulkevich R.A., Raimova A.A. Enhancement of the Data Analysis Subsystem in the Task-Efficiency Monitoring System HPC TaskMaster for the cHARISMa Supercomputer Complex at HSE University. In: Sokolinsky L., Zymbler M., Voevodin V., Dongarra J. (Eds.). Parallel Computational Technologies (PCT’2024). Communications in Computer and Information Science. Springer; 2024. V. 2241. P. 49–64. https://doi.org/10.1007/978-3-031-73372-7_4

7. Slastnikov S.A., Zhukova L.F., Semichasnov I.V. Application for data retrieval, analysis, and forecasting in social networks. Informatsionnye tekhnologii i vychislitel’nye sistemy = Journal of Information Technologies and Computing Systems. 2024;1:97–108 (in Russ.). https://doi.org/10.14357/20718632240110

8. Kirdeev A., Burkin K., Vorobev A., Zbirovskaya E., Lifshits G., Nikolaev K., Zelenskaya E., Donnikov M., Kovalenko L., Urvantseva I., Poptsova M. Machine learning models for predicting risks of MACEs for myocardial infarction patients with different VEGFR2 genotypes. Front. Med. 2024;11:1452239. https://doi.org/10.3389/fmed.2024.1452239

9. Al-Khulaidi A., Sadovoy N. Analysis of existing software packages in the cluster systems. Vestnik Donskogo gosudarstvennogo tekhnicheskogo universiteta = Vestnik of Don State Technical University. 2010;10(3):303–310 (in Russ.). https://elibrary.ru/mvsqql

10. Ladygin I.I., Loginov A.V., Filatov A.V., Yankov S.G. Klastery na mnogoyadernykh protsessorakh (Clusters on Multi-Core Processors). Moscow: MPEI Publ.; 2008. 112 p. (in Russ.). ISBN 978-5-383-00142-4. https://elibrary.ru/qmsnap

11. Kokots A.V. Development of a software model of an effective cluster computing system Vychislitel’nye seti. Teoriya i praktika = Network Journal. Theory and Practice. 2016;2(29):6.1. Available from URL: https://network-journal.mpei.ac.ru/ (in Russ.). Accessed June 02, 2025.

12. Kaur K., Rai A.K. A Comparative Analysis: Grid, Cluster and Cloud Computing. Int. J. Adv. Res. Computer Commun. Eng. 2014;3(3):5730–5734.

13. Omer S.M.I., Mustafa A.B.A., Alghali F.A.E. Comparative study between Cluster, Grid, Utility, Cloud and Autonomic computing. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE). 2014;9(6):61–67. http://doi.org/10.9790/1676-09636167

14. Kumar R. Comparison between Cloud Computing, Grid Computing, Cluster Computing and Virtualization. Int. J. Mod. Computer Sci. Appl. (IJMCSA). 2015;3(1):42–47. http://doi.org/10.13140/2.1.1759.7765

15. Voevodin Vl.V., Zhumatii S.A. Vychislitel’noe delo i klasternye sistemy (Computing and Cluster Systems). Moscow: MSU Press; 2007. 150 p. (in Russ.). ISBN 978-5-211-05440-0

16. Hopcroft J.D., Motwani R., Ulman J.D. Vvedenie v teoriyu avtomatov, yazykov i vychislenii (Introduction to Automata Theory, Languages, and Computations): transl. from Engl. Moscow: Vil’yams; 2015. 528 p. (in Russ.). ISBN 978-5-8459-1969-4 [Hopcroft J.E., Motwani R., Ulman J.D. Introduction to Automata Theory, Languages and Computation. Boston, etc.: Addison-Wesley Publ. Comp.; 2001. 521 p.]

17. Baranov S.I. Sintez mikroprogrammnykh avtomatov (Graf-skhemy i avtomaty) (Synthesis of Microprogrammed Automata (Graph-Schemes and Automata)). Leningrad: Energiya; 1979. 231 p. (in Russ.).

18. Vashkevich N.P. Sintez mikroprogrammnykh upravlyayushchikh avtomatov (Synthesis of Microprogram Control Automata). Penza; 1990. 115 с. (in Russ.).

19. Vashkevich N.P., Sibiryakov M.A. The formal automatic models of algorithms of processing of cached data. Sovremennye naukoemkie tekhnologii = Modern High Technologies. 2016;(8-2):205–213 (in Russ.). https://elibrary.ru/whksst

20. Lazarev V.G., Piil’ E.I., Turuta E.N. Postroenie programmiruemykh upravlyayushchikh ustroistv (Construction of Programmable Control Devices). Moscow: Energoatomizdat; 1984. 264 p. (in Russ.).

21. Anishev P.A., Achasova S.M., Bandman O.L. Metody parallel’nogo programmirovaniya (Methods of Parallel Programming). Novosibirsk: Nauka; 1981. 180 р. (in Russ.).

22. Yuditskii S.A., Magergut V.Z. Logicheskoe upravlenie diskretnymi protsessami. Modeli, analiz, sintez (Logical Control of Discrete Processes. Models, Analysis, Synthesis). Moscow: Mashinostroenie; 1987. 176 р. (in Russ.).

23. Girault A., Lee E.A. Hierarchical finite state machines with multiple concurrency models. IEEE Trans. Comput.-Aided Design Integr. Circuits Syst. 1999;18(6):742–760. https://doi.org/10.1109/43.766725

24. Stefansson E., Johansson K.H. Hierarchical finite state machines for efficient optimal planning in large-scale systems. In: 2023 European Control Conference (ECC). June, Bucharest, Romania. IEEE; 2023. https://doi.org/10.23919/ECC57647.2023.10178139

25. Alur R., Yannakakis M. Model checking of hierarchical state machines. ACM SIGSOFT Software Engineering Notes. 1998;23(6):175–188. http://doi.org/10.1145/503502.503503

26. Bolotova L.S. Sistemy iskusstvennogo intellekta. Modeli i tekhnologii, osnovannye na znaniyakh (Artificial Intelligence Systems. Models and Technologies Based on Knowledge). Moscow: Finansy i statistika; 2012. 664 p. (in Russ.). ISBN 978- 5-279-03530-4

27. Thayse A., Gribomont P., Louis J. Logicheskii podkhod k iskusstvennomu intellektu: ot klassicheskoi logiki k logicheskomu programmirovaniyu (Logical Approach to Artificial Intelligence: From Classical Logic to Logical Programming): transl. from French. Moscow: Mir; 1990. 429 p. (in Russ.). ISBN 5-03-001636-8

28. Ueno H., Ishizuka M. (Eds.). Predstavlenie i ispol’zovanie znanii (Representation and Use of Knowledge): transl. from. Japan. Moscow: Mir; 1989. 220 p. (in Russ.). ISBN 5-03-000685-0

29. Evreinov E.V., Kosarev Yu.G. Odnorodnye universal’nye sistemy vysokoi proizvoditel’nosti (Homogeneous Universal Systems of High Productivity). Novosibirsk: Nauka; 1966. 308 p. (in Russ.).

30. Belov V.V., Vorob’ev E.M., Shatalov V.E. Teoriya grafov (Graph Theory). Moscow: Vysshaya shkola; 1976. 392 p. (in Russ.).

31. Polikarpova N.I., Shalyto A.A. Avtomatnoe programmirovanie (Automata Programming): 2nd ed. St. Petersburg: Piter; 2011. 176 p. (in Russ.). ISBN 987-5-4237-0075-1

32. Peterson J. Teoriya setei Petri i modelirovanie system (Petri Net Theory and the Modeling of Systems): transl. from Engl. Moscow: Mir; 1984. 368 p. (in Russ.). [Peterson J.L. Petri Net Theory and the Modeling of Systems. NY: Prentice-Hall; 1981. 310 p.]

33. Kotov V.E. Seti Petri (Petri Nets). Moscow: Nauka; 1984. 160 p. (in Russ.).

34. Volchikhin V.I., Zinkin S.A. Logic and algebraic models and methods in designing functional architecture of distributed data storage and processing systems. Izvestiya vuzov. Povolzhskii region. Tekhnicheskie nauki = University Proceedings. Volga Region. Technical Sciences. 2012;2:3–16 (in Russ.). https://elibrary.ru/pfpgml

35. Zinkin S.A. Elements of a new object-oriented technology for modeling and implementing systems and networks for storing and processing data. Informatsionnye tekhnologii = Information Technologies. 2008;10:20–27 (in Russ.).

36. Andon F.I., Doroshenko A.E., Tseitlin G.E., Yatsenko E.A. Algebroalgoritmicheskie modeli i metody parallel’nogo programmirovaniya (Algebraic Algorithmic Models and Methods of Parallel Programming). Kiev: Akademperiodika; 2007. 634 p. (in Russ.).

37. Yushchenko E.L., Tseitlin G.E, Gritsai V.P., Terzyan T.K. Mnogourovnevoe strukturnoe proektirovanie programm. Teoreticheskie osnovy, instrumentarii (Multilevel structural design of programs. Theoretical foundations, tools). Moscow: Finansy i statistika; 1989. 208 p. (in Russ.). ISBN 5-279-00233-Х

38. Gurevich Y. Abstract State Machines: An Overview of the Project. In: Seipel D., Turull-Torres J.M. (Eds.). Foundations of Information and Knowledge Systems. Lecture Notes in Computer Science. Springer; 2004. V. 2942. P. 6–13. https://doi.org/10.1007/978-3-540-24627-5_2

39. Maier D. Teoriya relyatsionnykh baz dannykh (The Theory of Relational databases): transl. from Engl. Moscow: Mir; 1987. 608 p. (in Russ.). [Maier D. The Theory of Relational databases: 1st ed. Computer Sci. Press; 1983. 656 p.]