New Capabilities in QosCosGrid Middleware for Advanced Job Management, Advance Reservation and Co-allocation of Computing Resources – Quantum Chemistry Application Use Case

  • Bartosz Bosak
  • Jacek Komasa
  • Piotr Kopta
  • Krzysztof Kurowski
  • Mariusz Mamoński
  • Tomasz Piontek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7136)


In this chapter we present the new capabilities of QosCosGrid (QCG) middleware for advanced job and resource management in the grid environment. By connecting many computing clusters together, QosCosGrid offers easy-to-use mapping, execution and monitoring capabilities for a variety of complex computations, such as parameter sweep, workflows, MPI or hybrid MPI-OpenMP as well as multiscale simulations. Thanks to QosCosGrid, large-scale programming models written in Fortran, C, C++ or Java can be automatically distributed over a network of computing resources with guaranteed Quality of Service – for example guaranteed startup time of a job. Consequently, applications can be run at specified periods with reduced execution time and waiting times. This enables more complex problem instances to be addressed. In order to prove the usefulness of the new functionality of QosCosGrid a detailed description of the system along with a real use case scenario from the quantum chemistry science domain will be presented in this chapter.


parallel computing MPI metascheduling advance reservation QoS High Performance Computing High Throughput Computing 


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  1. 1.
    Agullo, E., Coti, C., Herault, T., Langou, J., Peyronnet, S., Rezmerita, A., Cappello, F., Dongarra, J.: QCG-OMPI: MPI Applications on Grids. Future Gener. Comput. Syst. 27, 357–369 (2011)CrossRefGoogle Scholar
  2. 2.
    Bachorz, R., Komasa, J.: Variational calculations on H2+ using exponentially correlated Gaussian wave functions. Computational Methods in Science and Technology 11(1), 5–9 (2005)CrossRefGoogle Scholar
  3. 3.
    Boys, S.F.: The Integral Formulae for the Variational Solution of the Molecular Many-Electron Wave Equations in Terms of Gaussian Functions with Direct Electronic Correlation. Royal Society of London Proceedings Series A 258, 402–411 (1960)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Cencek, W., Komasa, J., Rychlewski, J.: High-performance Computing in Molecular Sciences. In: Handbook on Parallel and Distributed Processing, p. 205. Springer, Heidelberg (2000)Google Scholar
  5. 5.
    Cencek, W., Rychlewski, J.: Many-electron Explicitly Correlated Gaussian Functions. I. General Theory and Test Results 98(2), 1252–1261 (1993)Google Scholar
  6. 6.
    Cencek, W., Szalewicz, K.: Ultra-high Accuracy Calculations for Hydrogen Molecule and Helium Dimer. International Journal of Quantum Chemistry 108, 2191–2198 (2008)CrossRefGoogle Scholar
  7. 7.
    Ciepiela, E., Nowakowski, P., Kocot, J., Harężlak, D., Gubała, T., Meizner, J., Kasztelnik, M., Bartyński, T., Malawski, M., Bubak, M.: Managing Entire Lifecycles of e-Science Applications in GridSpace2 Virtual Laboratory – from Motivation through Idea to Operable Web-Accessible Environment Built on Top of PL-Grid e-Infrastructure. In: Bubak, M., Szepieniec, T., Wiatr, K. (eds.) PL-Grid 2011. LNCS, vol. 7136, pp. 228–239. Springer, Heidelberg (2012)Google Scholar
  8. 8.
    Dziubecki, P., Grabowski, P., Krysiński, M., Kuczyński, T., Kurowski, K., Piontek, T., Szejnfeld, D.: Online Web-Based Science Gateway for Nanotechnology Research. In: Bubak, M., Szepieniec, T., Wiatr, K. (eds.) PL-Grid 2011. LNCS, vol. 7136, pp. 205–216. Springer, Heidelberg (2012)Google Scholar
  9. 9.
    Kravtsov, V., Bar, P., Carmeli, D., Schuster, A., Swain, M.: A scheduling framework for large-scale, parallel, and topology-aware applications. Journal of Parallel and Distributed Computing 70(9), 983–992 (2010)CrossRefzbMATHGoogle Scholar
  10. 10.
    Kurowski, K., Ludwiczak, B., Nabrzyski, J., Oleksiak, A., Pukacki, J.: Dynamic Grid Scheduling with Job Migration and Rescheduling in the GridLab Resource Management System. Sci. Program. 12, 263–273 (2004)Google Scholar
  11. 11.
    Kurowski, K., de Back, W., Dubitzky, W., Gulyás, L., Kampis, G., Mamonski, M., Szemes, G., Swain, M.: Complex System Simulations with QosCosGrid. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009, Part I. LNCS, vol. 5544, pp. 387–396. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Kurowski, K., Nabrzyski, J., Oleksiak, A., Weglarz, J.: Grid Scheduling Simulations with GSSIM. In: Proceedings of the 13th International Conference on Parallel and Distributed Systems, vol. 02, pp. 1–8. IEEE Computer Society, Washington, DC, USA (2007)Google Scholar
  13. 13.
    Kurowski, K., Oleksiak, A., Weglarz, J.: Multicriteria, Multi-user Scheduling in Grids with Advance Reservation. J. of Scheduling 13, 493–508 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Laure, E., Grandi, C., Fisher, S., Frohner, A., Kunszt, P., Krenek, A., Mulmo, O., Pacini, F., Prelz, F., White, J., Barroso, M., Buncic, P., Byrom, R., Cornwall, L., Craig, M., Di Meglio, A., Djaoui, A., Giacomini, F., Hahkala, J., Hemmer, F., Hicks, S., Edlund, A., Maraschini, A., Middleton, R., Sgaravatto, M., Steenbakkers, M., Walk, J., Wilson, A.: Programming the Grid with gLite. In: Computational Methods in Science and Technology (2006)Google Scholar
  15. 15.
    Powell, M.J.D.: An Efficient Method for Finding the Minimum of a Function of Several Variables Without Calculating Derivatives. The Computer Journal 7(2), 155–162 (1964)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in Fortran 77: The Art of Scientific Computing, 2nd edn. Cambridge University Press (September 1992)Google Scholar
  17. 17.
    Przybytek, M., Cencek, W., Komasa, J., Łach, G., Jeziorski, B., Szalewicz, K.: Relativistic and Quantum Electrodynamics Effects in the Helium Pair Potential. Phys. Rev. Lett. 104(18), 183003 (2010)CrossRefGoogle Scholar
  18. 18.
    Singer, K.: The Use of Gaussian (Exponential Quadratic) Wave Functions in Molecular Problems. I. General Formulae for the Evaluation of Integrals. Royal Society of London Proceedings Series A 258, 412–420 (1960)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Streit, A., Erwin, D., Mallmann, D., Menday, R., Rambadt, M., Riedel, M., Romberg, M., Schuller, B., Wieder, P.: UNICORE – From Project Results to Production Grids. In: Grid Computing and New Frontiers of High Performance Processing (2005)Google Scholar
  20. 20.
    Troger, P., Rajic, H., Haas, A., Domagalski, P.: Standardization of an API for Distributed Resource Management Systems. In: Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid, CCGRID 2007, pp. 619–626. IEEE Computer Society, Washington, DC, USA (2007)Google Scholar
  21. 21.
    Tuecke, S., Welch, V., Engert, D., Pearlman, L., Thompson, M.: Internet X.509 Public Key Infrastructure (PKI) Proxy Certificate Profile. RFC 3820 (Proposed Standard) (June 2004)Google Scholar
  22. 22.
    HPC Basic Profile Version 1.0,
  23. 23.
  24. 24.
    BREIN Project,
  25. 25.
    GridLab Project,
  26. 26.
    MAPPER – Multiscale Applications on European e-Infrastructures,
  27. 27.
    PRACE – The Partnership for Advanced Computing in Europe,
  28. 28.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bartosz Bosak
    • 1
  • Jacek Komasa
    • 2
  • Piotr Kopta
    • 1
  • Krzysztof Kurowski
    • 1
  • Mariusz Mamoński
    • 1
  • Tomasz Piontek
    • 1
  1. 1.Poznań Supercomputing and Networking CenterPoznańPoland
  2. 2.Faculty of ChemistryAdam Mickiewicz UniversityPoznańPoland

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