ARCHITECTURE OF THE PARALLEL SOFTWARE FOR THE SIMULATION OF MULTIDIMENSIONAL PROBLEMS

  • Андрей Евгеньевич Краснов State Institute of Information Technologies and Telecommunications
  • Евгений Николаевич Надеждин State Institute of Information Technologies and Telecommunications
  • Дмитрий Николаевич Никольский State Institute of Information Technologies and Telecommunications

Abstract

The architecture of the parallel software constructed on the basis of a design pattern «Bridge», and intended for simulation of multidimensional tasks (network dynamics; compression of multidimensional data, pattern recognition) is considered:. The architecture is based on an object-oriented approach to programming and using design patterns. The hierarchy of the classes realizing methods for the most various solvers is created. For parallel programming on a cluster of Intel Core i5-2300 processors and graphics processors GM206, NVidia Geforce GTX 950 cards are used MPI- and CUDA-technologies. Examples of solving some multidimensional problems are given: network dynamics, data compression, pattern recognition

Author Biographies

Андрей Евгеньевич Краснов, State Institute of Information Technologies and Telecommunications

Doctor of Physical and Mathematical Sciences, Professor, Research fellow

Евгений Николаевич Надеждин, State Institute of Information Technologies and Telecommunications

doctor of technical sciences, professor, Research fellow

Дмитрий Николаевич Никольский, State Institute of Information Technologies and Telecommunications

Candidate of Physical and Mathematical Sciences, Associate Professor, Senior research fellow

References

1. Kalachev A.A., Krasnov A.E., Nadezhdin E.N., Nikol'skiy D.N., Repin D.S. Geterogennaya mnogosvyaznaya set' aktivnykh elementov. Innovatsionnye, informatsionnye i kommunikatsionnye tekhnologii // Sbornik trudov XIII Mezhdunarodnoy nauchno-prakticheskoy konferentsii. / Pod red. S.U. Uvaysova. – M.: Assotsiatsiya vypusknikov i sotrudnikov voenno-vozdushnoy inzhenernoy akademii im. prof. Zhukovskogo, 2016, S. 277-279.
2. Kalachev A.A., Krasnov A.E., Nadezhdin E.N., Nikol'skiy D.N., Repin D.S. Model' geterogennoy seti dlya simulyatsii neyrodinamicheskikh zadach. // Sovremennye informatsionnye tekhnologii i IT-obrazovanie. 2016, Tom 12, №1. S. 80–90. (ISSN 2411-1473).
3. Krasnov A.E., Krasnikov S.A., Chernov E.A. Method of the compression of phase portraits of generalized spectral data for solving their clustering and recognition // In math. of International Scientific-Practical Conference «Innovative Information Technologies» (Prague, 21–25 april 2014), 2014, Volume 2, section 2. – M.: HSE, P. 664–670.
4. Krasnov A.E., Nikol'skiy D.N., Kalachev A.A. Snizhenie razmernosti spektral'nykh dannykh neyropodobnym algoritmom // Svidetel'stvo o gosudarstvennoy registratsii programmy dlya EVM, Rossiyskaya federatsiya, № 2017612195, 2017.
5. Nikol’skii D. N. Mathematical simulation of the evolution of a liquid-liquid interface in piecewise inhomogeneous layers of complex geological structure // Computational Mathematics and Mathematical Physics. June 2013, Volume 53, Issue 6, P. 858–865. doi:10.1134/S0965542513060146.
6. Nikol’skii D. N. Three-dimensional evolution of the boundary of a polluted area in a bounded piecewise homogeneous porous material // Computational Mathematics and Mathematical Physics. May 2011, Volume 51, Issue 5, P. 855–861. doi:10.1134/S0965542511050125.
7. Krasnov A.E., Krasnikov S.A., Kompanets I.N. Correlation-statistical methods of distinguishing complicated and noisy spectra // Journal of Optics A: Pure and Applied Optics. 2002, Volume 4, № 3. P. 329 – 337.
8. Krasnov A.E., Golovkin M.E. Metody vydeleniya invariantnykh priznakov izobrazheniy // Aktual'nye problemy sovremennoy nauki. 2016, №4 (89), P. 209–212.
9. Krasnov A.E., Nadezhdin E.N., Nikol'skiy D.N., i dr. Neyrosetevoy podkhod k otsenivaniyu effektivnosti funktsionirovaniya organizatsii na osnove agregirovaniya pokazateley ee deyatel'nosti // Informatizatsiya nauki i obrazovaniya. Yanvar' 2017, №1, S. 141–154.
10. Gamma E., Khelm R., Dzhonson R., Vlissides Dzh. Priemy ob"ektno-orientirovannogo proektirovaniya. – St. Peterburg: SPb. Piter, 2016. – 366 s.
11. Bjarne S. The C++ Programming Language, - 4th Edition. Addison-Wesley, 2013. –1368 p.
12. Scott M. Effective Modern C++: 42 Specific Ways to Improve Your Use of C++11 and C++14. O'Reilly Media, 2014. 384 p.
13. Pu C.-L., Pei W.-J., Michaelsond A. Robustness analysis of network controllability // Physica, 2012, A 391. P. 4420–4425.
14. Izhikevich E.M. Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting // Cambridge. Massachusetts. London. England: The MIT Press, 2007. – 497 р.
15. Klingspor M. Hilbert transform: Mathematical theory and applications to signal processing // LiTH - MAT - EX, 2015. – 76 p.
16. Krasnov A.E., i dr. Osnovy spektral'noy komp'yuternoy kvalimetrii zhidkikh sred / Pod redaktsiey prof. Krasnova A.E. - M: «Yurisprudentsiya», 2006. - 264 p.
17. Kazakov K.V., Kalachev A.A., Krasnov A.E., Nikol'skiy D.N., Shevelev S.A. Sravnenie effektivnostey razlicheniya signalov na fone sil'nykh pomekh na osnove mnogokriterial'noy i neyrosetevoy tekhnologiy. Innovatsionnye, informatsionnye i kommunikatsionnye tekhnologii // Sbornik trudov XIII Mezhdunarodnoy nauchno-prakticheskoy konferentsii. / Pod red. S.U. Uvaysova. – Moskva: Assotsiatsiya vypusknikov i sotrudnikov voenno-vozdushnoy inzhenernoy akademii im. prof. Zhukovskogo, 2016,. S. 257–259.
Published
2017-05-30
How to Cite
КРАСНОВ, Андрей Евгеньевич; НАДЕЖДИН, Евгений Николаевич; НИКОЛЬСКИЙ, Дмитрий Николаевич. ARCHITECTURE OF THE PARALLEL SOFTWARE FOR THE SIMULATION OF MULTIDIMENSIONAL PROBLEMS. Modern Information Technologies and IT-Education, [S.l.], v. 13, n. 1, p. 49-58, may 2017. ISSN 2411-1473. Available at: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/202>. Date accessed: 20 oct. 2025. doi: https://doi.org/10.25559/SITITO.2017.1.459.
Section
Parallel and distributed programming, grid technologies, programming on GPUs