Advertisement

Journal of Grid Computing

, 7:463 | Cite as

From Dedicated Grid to Volunteer Grid: Large Scale Execution of a Bioinformatics Application

  • Viktors Bertis
  • Raphaël BolzeEmail author
  • Frédéric Desprez
  • Kevin Reed
Article

Abstract

Large volunteer desktop platforms are now available for several kind of applications. More and more scientists consider this type of computing power as an alternative to the classical platforms such as dedicated clusters aggregated into Grids. This paper presents the work we did to run the first phase of the Help Cure Muscular Dystrophy project to run on World Community Grid. The project was launched on December 19, 2006, and took 26 weeks to complete. During this time frame, 123 GB of results were produced by volunteers who share their idle CPU time to compute a cross docking experiment over 168 proteins. We present performance evaluation of the overall execution and compare the World Community Grid volunteer Grid with a dedicated one.

Keywords

Desktop computing Docking application World Community Grid Grid performance evaluation Grids comparison 

References

  1. 1.
    Anderson, D.P., Cobb, J., Korpela, E., Lebofsky, M., Werthimer, D.: SETI@home: an experiment in public-resource computing. Commun. ACM 45(11), 56–61 (2002)CrossRefGoogle Scholar
  2. 2.
    Taufer, M., An, C., Kerstens, A., Brooks, C.L., III.: Predictor@ Home: a protein structure prediction supercomputer based on public-resource computing. IEEE Trans. Parallel Distrib. Syst. 17, 786–796 (2006)CrossRefGoogle Scholar
  3. 3.
    World Community Grid: World Community Grid web site. http://www.worldcommunitygrid.org (2009)
  4. 4.
    Univa UD: Univa UD, PCs Grid solution: Grid MP. http://www.univaud.com/hpc/products/grid-mp/ (2009)
  5. 5.
    Anderson, D.P., Christensen, C., Allen, B.: Designing a runtime system for volunteer computing. In: Proceedings of the Supercomputing Conference, Tampa (2006)Google Scholar
  6. 6.
    Uk, B., Taufer, M., Stricker, T., Settanni, G., Cavalli, A., Caflisch, A.: Combining task- and data parallelism to speed up protein folding on a desktop grid platform - is efficient protein folding possible with CHARMM on the united devices metaprocessor. In: Proc. of the IEEE International Symposium on Cluster Computing and the Grid (CCGrid ’03) (2003)Google Scholar
  7. 7.
    Kondo, D., Fedak, G., Cappello, F., Chien, A.A., Casanova, H.: Characterizing resource availability in enterprise desktop Grids. Future Gener. Comput. Syst. 23(7), 888–903 (2007)CrossRefGoogle Scholar
  8. 8.
    HCMD team World Community Grid: Help Cure Muscular Dystrophy description. http://www.worldcommunitygridorgprojects_showcasehcmdviewHcmdMain.do (2009)
  9. 9.
    Sacquin-Mora, S., Carbone, A., Lavery, R.: Identification of protein interaction partners and protein-protein interaction sites. J. Mol. Biol. 382(5), 1276–1289 (2008)CrossRefGoogle Scholar
  10. 10.
    Zacharias, M.: Protein-protein docking with a reduced protein model accounting for side-chain flexibility. Protein Sci. 12(6), 1271–1282 (2003)CrossRefGoogle Scholar
  11. 11.
    Mintseris, J., Wiehe, K., Pierce, B., Anderson, R., Chen, R., Janin, J., Weng, Z.: Protein-protein docking benchmark 2.0: an update. Proteins: Structure, Function, and Bioinformatics 60(2), 214–216 (2005)CrossRefGoogle Scholar
  12. 12.
    Anderson, D.P.: BOINC: a system for public resource computing and storage. In: Proceedings on the Fifth IEEE/ACM International Workshop on Grid Computing (CGRID04) (2004)Google Scholar
  13. 13.
    WCG team: World Community Grid global statistics page. http://www.worldcommunitygrid.org/stat/viewGlobal.do (2009)
  14. 14.
    World Community Grid advisory board: World Community Grid request for proposals. http://www.worldcommunitygrid.org/bg/rfp.pdf (2005)
  15. 15.
    Anderson, D.P., Korpela, E., Walton, R.: High-performance task distribution for volunteer computing. In: E-SCIENCE ’05: Proceedings of the First International Conference on e-Science and Grid Computing, pp. 196–203. IEEE Computer Society, Washington, DC (2005)CrossRefGoogle Scholar
  16. 16.
    Bolze, R., Cappello, F., Caron, E., Daydé, M., Desprez, F., Jeannot E., Jégou, Y., Lantéri, S., Leduc, J., Melab, N., Mornet, G., Namyst, R., Primet, P., Quetier, B., Richard, O., Talbi, E.-G., Touche, I.: Grid’5000: a large scale and highly reconfigurable experimental Grid testbed. Int. J. High Perform. Comput. Appl. 20(4), 481–494 (2006)CrossRefGoogle Scholar
  17. 17.
    Engelen, S., Trojan, L.-A., Sacquin-Mora, S., Lavery, R., Carbone, A.: Joint evolutionary trees: a large-scale method to predict protein interfaces based on sequence sampling. PLoS Comput. Biol. 5(1), e1000267+ (2009)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Viktors Bertis
    • 1
  • Raphaël Bolze
    • 2
    • 3
    Email author
  • Frédéric Desprez
    • 2
    • 4
  • Kevin Reed
    • 5
  1. 1.IBM Systems & Technology GroupAustinUSA
  2. 2.LIP laboratoryUMR 5668, CNRS-ENS-Lyon-UCBL-INRIALyonFrance
  3. 3.CNRSParisFrance
  4. 4.INRIASophia AntipolisFrance
  5. 5.IBM InteractiveChicagoUSA

Personalised recommendations