Journal of Grid Computing

, Volume 15, Issue 1, pp 41–53 | Cite as

Metadata Management in the MoSGrid Science Gateway - Evaluation and the Expansion of Quantum Chemistry Support

  • Richard Grunzke
  • Jens Krüger
  • René Jäkel
  • Wolfgang E. Nagel
  • Sonja Herres-Pawlis
  • Alexander Hoffmann


Science gateways are employed to hide increasingly complex IT infrastructures from users via easy-to-use graphical interfaces while enabling IT-driven research not possible before. The science gateway MoSGrid (Molecular Simulation Grid) is a valuable and user-friendly workbench to submit and process molecular simulation studies on a large scale. With regard to the needs of the users, we focus on the interoperability of simulations using two prominent quantum chemical codes, Gaussian09 and NWChem. At a first glimpse, the definition of functionals and basis sets seems to be sufficient to evoke the same type of calculation in both codes using the quantum chemical workflows in MoSGrid. In more detail, this is not true and more aspects such as integration grids, convergence criteria and basis set dimensions have to be well defined in order to obtain a trustworthy comparability between quantum chemical codes. In previous work, these details have not been defined and included in the MSML (Molecular Simulation Markup Language) implementation within MoSGrid. After the investigation presented here, all these details can be integrated to extend the quantum chemical workflows in MoSGrid. Furthermore, a performance evaluation of the underlying metadata management is performed to investigate its suitability and scalability to the MSML extension.


Science gateways Quantum chemistry Metadata management 


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  1. 1.
    Aguilera, A., Grunzke, R., Markwardt, U., Habich, D., Schollbach, D., Garcke, J.: Towards an industry data gateway: An integrated platform for the analysis of wind turbine data. In: Science Gateways (IWSG), 2015 7th International Workshop on, pp. 62–66 (2015).  10.1109/IWSG.2015.8
  2. 2.
    Allcock, W., Bresnahan, J., Kettimuthu, R., Link, M., Dumitrescu, C., Raicu, I., Foster, I.: The globus striped gridftp framework and server Proceedings of the 2005 ACM/IEEE conference on Supercomputing, p. 54. IEEE Computer Society (2005)Google Scholar
  3. 3.
    Balaskó, Á. Workflow concept of ws-pgrade/guse. In: Kacsuk, P. (ed.) : Science gateways for distributed computing infrastructures, pp 33–50. Springer (2014)Google Scholar
  4. 4.
    Becciani, U., Sciacca, E., Costa, A., Massimino, P., Pistagna, C., Riggi, S., Vitello, F., Petta, C., Bandieramonte, M., Krokos, M.: Science gateway technologies for the astrophysics community. Concurrency and Computation: Practice and Experience 27(2), 306–327 (2015)CrossRefGoogle Scholar
  5. 5.
    Costa, A., Massimino, P., Bandieramonte, M., Becciani, U., Krokos, M., Pistagna, C., Riggi, S., Sciacca, E., Vitello, F.: An innovative science gateway for the cherenkov telescope array. Journal of Grid Computing, 1–13 (2015)Google Scholar
  6. 6.
    Dongarra, J., Beckman, P., Moore, T., Aerts, P., Aloisio, G., Andre, J.C., Barkai, D., Berthou, J.Y., Boku, T., Braunschweig, B., Cappello, F., Chapman, B., Chi, X., Choudhary, A., Dosanjh, S., Dunning, T., Fiore, S., Geist, A., Gropp, B., Harrison, R., Hereld, M., Heroux, M., Hoisie, A., Hotta, K., Jin, Z., Ishikawa, Y., Johnson, F., Kale, S., Kenway, R., Keyes, D., Kramer, B., Labarta, J., Lichnewsky, A., Lippert, T., Lucas, B., Maccabe, B., Matsuoka, S., Messina, P., Michielse, P., Mohr, B., Mueller, M.S., Nagel, W.E., Nakashima, H., Papka, M.E., Reed, D., Sato, M., Seidel, E., Shalf, J., Skinner, D., Snir, M., Sterling, T., Stevens, R., Streitz, F., Sugar, B., Sumimoto, S., Tang, W., Taylor, J., Thakur, R., Trefethen, A., Valero, M., Van Der Steen, A., Vetter, J., Williams, P., Wisniewski, R., Yelick, K.: The international exascale software project roadmap. Int. J. High Perform. Comput. Appl. 25(1), 3–60 (2011). doi: 10.1177/1094342010391989 CrossRefGoogle Scholar
  7. 7.
    Frisch, M., Trucks, G., Schlegel, H. B., Scuseria, G., Robb, M., Cheeseman, J., Scalmani, G., Barone, V., Mennucci, B., Petersson, G., et al.: Gaussian 09, revision a. 02, gaussian. Inc., Wallingford, CT 200 (2009)Google Scholar
  8. 8.
    Gaussian: Gaussian 09 user’s refference. (2015)
  9. 9.
    Gesing, S., Grunzke, R., Krüger, J., Birkenheuer, G., Wewior, M., Schäfer, P., Schuller, B., Schuster, J., Herres-Pawlis, S., Breuers, S., Balaskó, Á., Kozlovszky, M., Fabri, A.S., Packschies, L., Kacsuk, P., Blunk, D., Steinke, T., Brinkmann, A., Fels, G., Müller-Pfefferkorn, R., Jäkel, R., Kohlbacher, O.: A single sign-on infrastructure for science gateways on a use case for structural bioinformatics. Journal of Grid Computing 10(4), 769–790 (2012). doi: 10.1007/s10723-012-9247-y CrossRefGoogle Scholar
  10. 10.
    Gesing, S., Krüger, J., Grunzke, R., de la Garza, L., Herres-Pawlis, S., Hoffmann, A. Molecular simulation grid (mosgrid): A science gateway tailored to the molecular simulation community. In: Kacsuk, P. (ed.) : Science Gateways for Distributed Computing Infrastructures, pp. 151–165. Springer International Publishing (2014)Google Scholar
  11. 11.
    Gottdank, T.: Introduction to the ws-pgrade/guse science gateway framework. In: Kacsuk, P. (ed.) Science Gateways for Distributed Computing Infrastructures, pp 19–32. Springer (2014)Google Scholar
  12. 12.
    Grunzke, R., Breuers, S., Gesing, S., Herres-Pawlis, S., Kruse, M., Blunk, D., de la Garza, L., Packschies, L., Schäfer, P., Schärfe, C., Schlemmer, T., Steinke, T., Schuller, B., Müller-Pfefferkorn, R., Jäkel, R., Nagel, W.E., Atkinson, M., Krüger, J.: Standards-based Metadata Management for Molecular Simulations. Concurrency and Computation: Practice and Experience 26(10), 1744–1759 (2014). doi: 10.1002/cpe.3116 CrossRefGoogle Scholar
  13. 13.
    Grunzke, R., Gesing, S., Jäkel, R., Nagel, W.E.: Towards Generic Metadata Management in Distributed Science Gateway Infrastructures. In: IEEE/ACM CCGrid 2014 (14th International Symposium on Cluster, Cloud and Grid Computing), pp. 566–570. Chicago, IL, US (2014),  10.1109/CCGrid.2014.98
  14. 14.
    Grunzke, R., Krüger, J., Gesing, S., Herres-Pawlis, S., Hoffmann, A., Aguilera, A., Nagel, W.E.: Managing complexity in distributed data life cycles enhancing scientific discovery (2015)Google Scholar
  15. 15.
    gUSE: Available science gateways. (2015)
  16. 16.
    Hajnal, Á., Farkas, Z., Kacsuk, P.: Data avenue: remote storage resource management in WS-PGRADE/gUSE. In: 2014 6th International Workshop on Science Gateways (IWSG), pp 1–5. IEEEGoogle Scholar
  17. 17.
    Hajnal, Á., Farkas, Z., Kacsuk, P., Pintér, T.: Remote storage resource management in WS-PGRADE/gUSE. In: Kacsuk, P. (ed.) Science Gateways for Distributed Computing Infrastructures, pp 69–81. Springer (2014)Google Scholar
  18. 18.
    HBP: The human brain project (2015)
  19. 19.
    Herres-Pawlis, S., Birkenheuer, G., Brinkmann, A., Gesing, S., Grunzke, R., Jäkel, R., Kohlbacher, O., Krüger, J., Dos Santos Vieira, I.: Workflow-enhanced conformational analysis of guanidine zinc complexes via a science gateway (2012)Google Scholar
  20. 20.
    Herres-Pawlis, S., Hoffmann, A., Garza, L.D.L., Krüger, J., Grunzke, R.: Expansion of quantum chemical metadata for workflows in the mosgrid science gateway. In: Science Gateways (IWSG), 2014 6th International Workshop on, pp. 67–72 (2014),  10.1109/IWSG.2014.18
  21. 21.
    Herres-Pawlis, S., Hoffmann, A., Grunzke, R., Packschies, L.: Orbital analysis of oxo and peroxo dicopper complexes via quantum chemical workflows in MoSGrid. In: Proceedings of the International Workshop on Scientific Gateways 2013 (IWSG) (2013)Google Scholar
  22. 22.
    Hoffmann, A., Grunzke, R., Herres-Pawlis, S.: Insights into the influence of dispersion correction in the theoretical treatment of guanidine-quinoline copper(i) complexes. J. Comput. Chem. 35(27), 1943–1950 (2014). doi: 10.1002/jcc.23706 CrossRefGoogle Scholar
  23. 23.
    Hoffmann, A., Herres-Pawlis, S.: Hiking on the potential energy surface of a functional tyrosinase model - implications of singlet, broken-symmetry and triplet description. Chem. Commun. 50, 403–405 (2014). doi: 10.1039/C3CC46893C CrossRefGoogle Scholar
  24. 24.
    Hoffmann, A., Rohrmüller, M., Jesser, A., dos Santos Vieira, I., Schmidt, W.G., Herres-Pawlis, S.: Geometrical and optical benchmarking of copper(ii) guanidine–quinoline complexes: Insights from td-dft and many-body perturbation theory (part ii). J. Comput. Chem. 35(29), 2146–2161 (2014). doi: 10.1002/jcc.23740 CrossRefGoogle Scholar
  25. 25.
    Jesser, A., Rohrmüller, M., Schmidt, W.G., Herres-Pawlis, S.: Geometrical and optical benchmarking of copper guanidine?quinoline complexes: Insights from td-dft and many-body perturbation theory. J. Comput. Chem. 35(1), 1–17 (2014). doi: 10.1002/jcc.23449 CrossRefGoogle Scholar
  26. 26.
    Jäkel, R., Müller-Pfefferkorn, R., Kluge, M., Grunzke, R., Nagel, W.E.: Architectural implications for exascale based on big data workflow requirements. In: Big Data and High Performance Computing, Advances in Parallel Computing, vol. 26, pp. 101–113. IOS Press (2015),  10.3233/978-1-61499-583-8-101
  27. 27.
    Kacsuk, P.: Science gateways for distributed computing infrastructures springer (2014)Google Scholar
  28. 28.
    Kacsuk, P., Farkas, Z., Kozlovszky, M., Hermann, G., Balasko, A., Karoczkai, K., Marton, I.: Ws-pgrade/guse generic dci gateway framework for a large variety of user communities. Journal of Grid Computing 10(4), 601–630 (2012). doi: 10.1007/s10723-012-9240-5 CrossRefGoogle Scholar
  29. 29.
    Kiss, T., Greenwell, P., Heindl, H., Terstyanszky, G., Weingarten, N.: Parameter sweep workflows for modelling carbohydrate recognition. Journal of Grid Computing 8(4), 587–601 (2010)CrossRefGoogle Scholar
  30. 30.
    Kozlovszky, M., Karóczkai, K., Márton, I., Kacsuk, P., Gottdank, T. Dci bridge: Executing ws-pgrade workflows in distributed computing infrastructures. In: Kacsuk, P. (ed.) : Science Gateways for Distributed Computing Infrastructures, pp 51–67. Springer (2014)Google Scholar
  31. 31.
    Krüger, J., Grunzke, R., Gesing, S., Breuers, S., Brinkmann, A., de la Garza, L., Kohlbacher, O., Kruse, M., Nagel, W.E., Packschies, L., Müller-Pfefferkorn, R., Schärfer, P., Schärfe, C., Steinke, T., Schlemmer, T., Warzecha, K.D., Zink, A., Herres-Pawlis, S.: The mosgrid science gateway a complete solution for molecular simulations. J. Chem. Theory Comput. 10 (6), 2232–2245 (2014). doi: 10.1021/ct500159h CrossRefGoogle Scholar
  32. 32.
    Liferay: Enterprise open source portal and collaboration software (2015).
  33. 33.
    McCandless, M., Hatcher, E., Gospodnetic, O.: Lucene in Action: Covers Apache Lucene 3.0 Manning Publications Co (2010)Google Scholar
  34. 34.
    Murray-Rust, P., Rzepa, H. S.: Chemical markup, xml, and the worldwide web. 1. basic principles. J. Chem. Inf. Comput. Sci. 39(6), 928–942 (1999)CrossRefGoogle Scholar
  35. 35.
    Noor, W., Schuller, B.: MMF: A flexible framework for metadata management in UNICORE. In: UNICORE Summit 2010 Proceedings, vol. 5, pp. 51–60 (2010)Google Scholar
  36. 36.
  37. 37.
  38. 38.
    Olabarriaga, S. D., Benabdelkader, A., Caan, M. W., Jaghoori, M. M., Krüger, J., de la Garza, L., Mohr, C., Schubert, B., Danezi, A., Kiss, T. Ws-pgrade/guse-based science gateways in teaching. In: Kacsuk, P. (ed.) : Science Gateways for Distributed Computing Infrastructures, pp 223–234. Springer (2014)Google Scholar
  39. 39.
    PRACE: Prace research infrastructure. (2015)
  40. 40.
    Putz, M. V., Mingos, D.M.P.: Applications of density functional theory to biological and bioinorganic chemistry preface (2013)Google Scholar
  41. 41.
    Rohrmüller, M., Herres-Pawlis, S., Witte, M., Schmidt, W.G.: Bis- μ-oxo and μ- η2: η2-peroxo dicopper complexes studied within (time-dependent) density-functional and many-body perturbation theory. J. Comput. Chem. 12, 1035–1045 (2013). doi: 10.1002/jcc.23230 CrossRefGoogle Scholar
  42. 42.
    Rolff, M., Schottenheim, J., Decker, H., Tuczek, F.: Copper–o 2 reactivity of tyrosinase models towards external monophenolic substrates: molecular mechanism and comparison with the enzyme. Chem. Soc. Rev. 40(7), 4077–4098 (2011)CrossRefGoogle Scholar
  43. 43.
    Sciacca, E., Vitello, F., Becciani, U., Costa, A., Massimino, P.: Visivo gateway and visivo mobile for the astrophysics community. In: Kacsuk, P. (ed.) Science Gateways for Distributed Computing Infrastructures, pp 181–194. Springer (2014)Google Scholar
  44. 44.
    Shahand, S., Santcroos, M., van Kampen, A. H., Olabarriaga, S. D.: A grid-enabled gateway for biomedical data analysis. Journal of Grid Computing 10(4), 725–742 (2012)CrossRefGoogle Scholar
  45. 45.
    Solomon, E. I., Heppner, D. E., Johnston, E. M., Ginsbach, J. W., Cirera, J., Qayyum, M., Kieber-Emmons, M. T., Kjaergaard, C. H., Hadt, R. G., Tian, L.: Copper active sites in biology. Chem. Rev. 114(7), 3659–3853 (2014)CrossRefGoogle Scholar
  46. 46.
    Solomon, E. I., Scott, R. A., King, R. B.: Computational inorganic and bioinorganic chemistry John Wiley andamp; Sons (2013)Google Scholar
  47. 47.
    Streit, A., Bala, P., Beck-Ratzka, A., Benedyczak, K., Bergmann, S., Breu, R., Daivandy, J. M., Demuth, B., Eifer, A., Giesler, A., et al.: Unicore 6 - recent and future advancements. Annals of Telecommunications-annales des Télécommunications 65(11-12), 757–762 (2010)CrossRefGoogle Scholar
  48. 48.
    Tao, J., Perdew, J. P., Staroverov, V. N., Scuseria, G. E.: Climbing the density functional ladder: Nonempirical meta–generalized gradient approximation designed for molecules and solids. Phys. Rev. Lett. 146–401(14) (2003)Google Scholar
  49. 49.
    Valiev, M., Bylaska, E. J., Govind, N., Kowalski, K., Straatsma, T. P., Van Dam, H. J., Wang, D., Nieplocha, J., Apra, E., Windus, T. L., et al: Nwchem: a comprehensive and scalable open-source solution for large scale molecular simulations. Comput. Phys. Commun. 181(9), 1477–1489 (2010)CrossRefzbMATHGoogle Scholar
  50. 50.
    Weigend, F., Ahlrichs, R.: Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for h to rn: Design and assessment of accuracy. Phys. Chem. Chem. Phys. 7, 3297–3305 (2005). doi: 10.1039/B508541A CrossRefGoogle Scholar
  51. 51.
    XSEDE: Extreme science and engineering discovery environment. (2015)

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Richard Grunzke
    • 1
  • Jens Krüger
    • 2
  • René Jäkel
    • 1
  • Wolfgang E. Nagel
    • 1
  • Sonja Herres-Pawlis
    • 3
    • 4
  • Alexander Hoffmann
    • 3
    • 4
  1. 1.Technische Universität DresdenDresdenGermany
  2. 2.University of TübingenTübingenGermany
  3. 3.RWTH Aachen UniversityAachenGermany
  4. 4.Ludwig-Maximilians-Universität MünchenMünchenGermany

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