Skip to main content
SpringerLink
Log in

The Creation of Intelligent Support Methods for Solving Mathematical Physics Problems on Supercomputers

  • Conference paper
  • First Online:
Supercomputing (RuSCDays 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1129))

Included in the following conference series:

Abstract

An approach to creating methods and means of intelligent support for solving compute-intensive problems (CI problems) of mathematical physics on modern peta- and future exaflops supercomputers, containing millions and, eventually, billions of simultaneously running computational cores and providing an enormous degree of parallelism, is proposed. The relevance of the intelligent support of the process of solving problems at all stages - from setting a problem, selecting a method of numerical solution to choosing a supercomputer architecture and software implementation is substantiated. The proposed system of intelligent support is based on the ontology of computational methods and algorithms, the ontology of parallel architectures and technologies and uses decision rules to find the best possible approach to parallel solving a problem specified by a user, at all stages of its solution, up to choosing the best planning strategy for the computational process. The paper describes the concept of creating intelligent support for solving CI problems, using ontologies and inference rules. An example demonstrating the use of the proposed approach for solving a problem of astrophysics is presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Compton, M., Barnaghi, P., Bermudez, L., et al.: The SSN ontology of the W3C semantic sensor network incubator group. Web Semant. Sci. Serv. Agents World Wide Web 17, 25–32 (2012)

    Article  Google Scholar 

  2. Keet, C.M., Ławrynowicz, A., d’Amato, C., et al.: The data mining optimization ontology. Web Semant. Sci. Serv. Agents World Wide Web 32, 43–53 (2015)

    Article  Google Scholar 

  3. Cvjetkovic, V.: Web physics ontology: online interactive symbolic computation in physics. In: 2017 4th Experiment@International Conference (Exp.at’17), Faro, pp. 52–57 (2017)

    Google Scholar 

  4. Ma, X.: Ontology Spectrum for Geological Data Interoperability. ITC, Netherlands (2011)

    Google Scholar 

  5. Cook, D., Neal, M., Bookstein, F., Gennari, J.: Ontology of physics for biology: representing physical dependencies as a basis for biological processes. J Biomed. Semant. 4, 41 (2013)

    Article  Google Scholar 

  6. Sarro, L.M., Martínez, R.: First steps towards an ontology for astrophysics. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS (LNAI), vol. 2774, pp. 1389–1395. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45226-3_188

    Chapter  Google Scholar 

  7. Louge, T., Karray, M.H., Archimède, B., Knödlseder, J.: Semantic interoperability in astrophysics for workflows extraction from heterogeneous services. In: van Sinderen, M., Chapurlat, V. (eds.) IWEI 2015. LNBIP, vol. 213, pp. 3–15. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-47157-9_1

    Chapter  Google Scholar 

  8. ESPAS. https://www.espas-fp7.eu/portal/browse.html#ontology

  9. SemGen. http://sbp.bhi.washington.edu/projects/semgen

  10. Awesome geoscience semantics. http://www.geoscience-semantics.org

  11. Cannataro, M., Comito, C.: A data mining ontology for grid programming. In: Proceedings of the 1st International Workshop on Semantic in Peer-to-Peer and Grid Computing, pp. 113–134 (2003)

    Google Scholar 

  12. Amarnath, B.R., Somasundaram, T.S., Ellappan, M., Buyya, R.: Ontology‐based grid resource management. Softw. Pract. Exper. 39, 1419–1438 (2009)

    Google Scholar 

  13. Faheem, H.M., König-Ries, B., Aslam, M.A., Aljohani, N.R., Katib, I.: Ontology design for solving computationally-intensive problems on heterogeneous architectures. Sustainability 10(2), 441 (2018)

    Article  Google Scholar 

  14. Antonov, A., Dongarra, J., Voevodin, V.: Algowiki project as an extension of the TOP500 methodology. JSFI 5(1), 4–10 (2018)

    Google Scholar 

  15. Springel, V.: The cosmological simulation code GADGET-2. Mon. Not. R. Astron. Soc. 364(4), 1105–1134 (2005)

    Article  Google Scholar 

  16. Wadsley, J.W., Stadel, J., Quinn, T.: Gasoline: a flexible, parallel implementation of TreeSPH. New Astron. 9(2), 137–158 (2004)

    Article  Google Scholar 

  17. Steinmetz, M.: GRAPESPH: cosmological smoothed particle hydrodynamics simulations with the special-purpose hardware GRAPE. Mon. Not. R. Astron. Soc. 278(4), 1005–1017 (1996)

    Article  Google Scholar 

  18. The Pencil code. http://pencil-code.nordita.org/references.php

  19. Stone, J.M., Norman, M.L.: ZEUS-2D: a radiation magnetohydrodynamics code for astrophysical flows in two space dimensions. I-The hydrodynamic algorithms and tests. ApJS 80(2), 753–790 (1992)

    Article  Google Scholar 

  20. Stone, J.M., Gardiner, T.A., Teuben, P., Hawley, J.F., Simon, J.B.: Athena: a new code for astrophysical MHD. ApJS 178(1), 137–177 (2008)

    Article  Google Scholar 

  21. Mignone, A., Bodo, G., Massaglia, S., et al.: PLUTO: a numerical code for computational astrophysics. ApJS 170(1), 228 (2007)

    Article  Google Scholar 

  22. Bryan, G.L., Norman, M.L., O’Shea, B.W., et al.: ENZO: an adaptive mesh refinement code for astrophysics. ApJS 211, 19 (2014)

    Google Scholar 

  23. Teyssier, R.: Cosmological hydrodynamics with adaptive mesh refinement. A&A 385, 337–364 (2002)

    Article  Google Scholar 

  24. Hopkins, P.F.: GIZMO: a new class of accurate, mesh-free hydrodynamic simulation methods. Mon. Not. R. Astron. Soc. 450(1), 53–110 (2015)

    Article  Google Scholar 

  25. Springel, V.: E pur si muove: galilean-invariant cosmological hydrodynamical simulations on a moving mesh. Mon. Not. R. Astron. Soc. 401(2), 791–851 (2010)

    Article  Google Scholar 

  26. Murphy, J., Burrows, A.: BETHE-Hydro: an arbitrary Lagrangian-Eulerian multidimensional hydrodynamics code for astrophysical simulations. ApJS 179, 209–241 (2008)

    Article  Google Scholar 

  27. Schive, H.Y., Tsai, Y.C., Chiueh, T.: GAMER: a graphic processing unit accelerated adaptive-mesh-refinement code for astrophysics. Astrophys. J. Suppl. Ser. 186(2), 457 (2010)

    Article  Google Scholar 

  28. Kulikov, I.: GPUPEGAS: a new GPU-accelerated hydrodynamic code for numerical simulation of interacting galaxies. ApJS 214, 12 (2014)

    Article  Google Scholar 

  29. Schneider, E.E., Robertson, B.E.: CHOLLA: a new massively parallel hydrodynamics code for astrophysical simulations. ApJS 217, 24 (2015)

    Article  Google Scholar 

  30. Kulikov, I.M., Chernykh, I.G., Snytnikov, A.V., Glinskiy, B.M., Tutukov, A.V.: AstroPhi: a code for complex simulation of the dynamics of astrophysical objects using hybrid supercomputers. CPC 186, 71–80 (2015)

    Google Scholar 

  31. Frutos-Alfaro, F., Carboni-Mendez, R.: MHD Generation Code. Revista de Matematicas: Teoria y Aplicaciones 23(1) (2016)

    Google Scholar 

  32. Goedbloed, J., Keppens, R., Poedts, S.: Computer simulations of solar plasmas. Space Sci. Rev. 107, 63 (2003)

    Article  Google Scholar 

  33. TOP500. https://www.top500.org

  34. Voevodin, V., Antonov, A., Nikitenko, D., et al.: Supercomputer Lomonosov-2: large scale, deep monitoring and fine analytics for the user community. JSFI 6(2), 8–11 (2019)

    Google Scholar 

  35. TASS. Russian news agency. https://tass.ru/nauka/5327107

  36. Glinskiy, B., Sapetina, A., Martynov, V., Weins, D., Chernykh, I.: The hybrid-cluster multilevel approach to solving the elastic wave propagation problem. In: Sokolinsky, L., Zymbler, M. (eds.) PCT 2017. CCIS, vol. 753, pp. 261–274. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67035-5_19

    Chapter  Google Scholar 

  37. Glinskiy, B., Kulikov, I., Snytnikov, A., Romanenko, A., Chernykh, I., Vshivkov, V.: Co-design of parallel numerical methods for plasma physics and astrophysics. JSFI 1(3), 88–98 (2014)

    Google Scholar 

  38. Podkorytov, D., Rodionov, A., Choo, H.: Agent-based simulation system AGNES for networks modeling: review and researching. In: Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication (ACM ICUIMC 2012), p. 115. ACM (2012)

    Google Scholar 

  39. Glinskiy, B., Kulikov, I., Chernykh, I., Snytnikov, A., Sapetina, A., Weins, D.: The integrated approach to solving large-size physical problems on supercomputers. In: Voevodin, V., Sobolev, S. (eds.) RuSCDays 2017. CCIS, vol. 793, pp. 278–289. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71255-0_22

    Chapter  Google Scholar 

  40. Antoniou, G., Harmelen, F.: Web ontology language: OWL. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, pp. 67–92. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24750-0_4

    Chapter  Google Scholar 

  41. Protege. https://protege.stanford.edu. Accessed 10 Jan 2018

  42. SWRL. http://www.w3.org/Submission/SWRL/. Accessed 10 Jan 2018

  43. Kulikov, I.: PEGAS: hydrodynamical code for numerical simulation of the gas components of interacting galaxies. Book Series of the Argentine Astronomical Society, vol. 4, pp. 91–95 (2013)

    Google Scholar 

  44. Kulikov, I.M., Chernykh, I.G., Glinskiy, B.M., Protasov, V.A.: An efficient optimization of Hll method for the second generation of Intel Xeon Phi Processor. Lobachevskii J. Math. 39(4), 543–551 (2018)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This research was conducted within the framework of the budget project No. 0315-2019-0009 for ICMMG SB RAS and supported in part by the Russian Foundation for Basic Research [grant No. 19-07-00085].

Author information

Authors and Affiliations

  1. Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russia

    Boris Glinskiy, Igor Kulikov & Anna Sapetina

  2. A.P. Ershov Institute of Informatics System SB RAS, Novosibirsk, Russia

    Yury Zagorulko & Galina Zagorulko

Authors
  1. Boris Glinskiy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yury Zagorulko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Galina Zagorulko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Igor Kulikov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anna Sapetina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anna Sapetina .

Editor information

Editors and Affiliations

  1. Research Computing Center, Moscow State University, Moscow, Russia

    Vladimir Voevodin

  2. Research Computing Center, Moscow State University, Moscow, Russia

    Sergey Sobolev

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Glinskiy, B., Zagorulko, Y., Zagorulko, G., Kulikov, I., Sapetina, A. (2019). The Creation of Intelligent Support Methods for Solving Mathematical Physics Problems on Supercomputers. In: Voevodin, V., Sobolev, S. (eds) Supercomputing. RuSCDays 2019. Communications in Computer and Information Science, vol 1129. Springer, Cham. https://doi.org/10.1007/978-3-030-36592-9_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36592-9_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36591-2

  • Online ISBN: 978-3-030-36592-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation