New Generation Computing

, Volume 25, Issue 4, pp 373–394

Customized Plug-in Modules in Metascheduler CSF4 for Life Sciences Applications

  • Zhaohui Ding
  • Xiaohui Wei
  • Yuan Luo
  • Da ma
  • Peter W. Arzberger
  • Wilfred W. Li
Article

Abstract

As more and more life science researchers start to take advantages of grid technologies in their work, the demand increases for a robust yet easy to use metascheduler or resource broker. In this paper, we have extended the metascheduler CSF4 by providing a Virtual Job Model (VJM) to synchronize the resource co-allocation for cross-domain parallel jobs. The VJM eliminates dead-locks and improves resource usage for multi-cluster parallel applications compiled with MPICH-G2. Taking advantage of the extensible scheduler plug-in model of CSF4, one may develop customized metascheduling policies for life sciences applications. As an example, an array-job scheduler plug-in is developed for pleasantly parallel applications such as AutoDock and Blast. The performance of the VJM is evaluated through experiments with mpiBLAST-g2 using a Gfarm data grid testbed. Furthermore, a CSF4 portlet has been released to provide a graphical user interface for transparent grid access, with the use of Gfarm for data staging and simplified data management. The platform is open source at sourceforge.net/projects/gcsf/ and has been deployed in life science gateways by projects such as My WorkSphere, and PRAGMA Biosciences Portal. The VJM enables the development of support for more sophisticated workflows and metascheduling policies in the near future.

Keywords

Metascheduling MPICH-G2 Co-Allocation Scheduler Plug-in Life Sciences 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Karonis, N.T., Toonen, B. and Foster, I., “A MPICH-G2: A Grid-enabled implementation of the Message Passing Interface,” Journal of Parallel and Distributed Computing, 2003.Google Scholar
  2. 2.
    Abramson, D., Giddy, J. and Kotler, L., “High performance parametric modeling with Nimrod/G: Killer application for the global grid,” IPDPS, 2000.Google Scholar
  3. 3.
    Wei, X., Ding, Z. and Yuan, S., “CSF4: A WSRF Compliant Meta-Scheduler,” Int. Conf. 06’ ’ on Grid Computing and Applications, June 26-29, 2006.Google Scholar
  4. 4.
    Goodsell, D.S., Morris, G.M. and Olson, A.J., “Automated docking of flexible ligands: applications of AutoDock,” J. Mol Recognit, vol. 9, pp. 1-5, 1996.CrossRefGoogle Scholar
  5. 5.
    Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W. and Lipman, D.J., “Gapped BLAST and PSI-BLAST: a new generation of protein database search programs,” Nucleic Acids Res, vol. 25, pp. 3389-402, 1997.CrossRefGoogle Scholar
  6. 6.
    Li, W.W., Krishnan, S., Mueller, K., Ichikawa, K., Date, S., Dallakyan, S., Sanner, M., Misleh, C., Ding, Z., Wei, X., Tatebe, O. and Arzberger, P.W., “Building cyberinfrastructure for bioinformatics using service oriented architecture,” Sixth IEEE Int. Symp. on Cluster Computing and the Grid Workshops Singapore, 2006.Google Scholar
  7. 7.
    Uthayopas, P., Maneesilp, J. and Ingongnam, P., “SCMS: An Integrated Cluster Management Tool for Beowulf Cluster System,” Proc. of the Int. Conf. on Parallel and Distributed Proceeding Techniques and Applications, 2000.Google Scholar
  8. 8.
    Foster, I., “Globus Toolkit Version 4: Software for Service-Oriented Systems,” LNCS, 3779, pp. 2-13, 2006.Google Scholar
  9. 9.
    Globus Alliance “Globus” http://www.globus.org, 2004.
  10. 10.
    Li, W.W., Baker, N.A. Baldridge, K., McCammon, J.A., Ellisman, M.H. Gupta, A., Holst, M.J., McCulloch, A.D., Michailova, A., Papadopoulos, P., Olson, A. Sanner, M. and Arzberger, P.W., “National Biomedical Computation Resource (NBCR): Developing End-to-End Cyberinfrastructure for Multiscale Modeling in Biomedical Research,” CTWatch Quarterly, 2, pp. 6-17, 2006.Google Scholar
  11. 11.
    Tatebe, O., Morita, Y. and Matsuoka, S., “Grid Datafarm Architecture for Petascale Data Intensive Computing,” Proc. of the 2nd IEEE/ACM Int. Symp. on Cluster Computing and the Grid, pp.102-110, 2002.Google Scholar
  12. 12.
    Geist, A., Beguelin, A., Dongarra, J., Jiang, W., Manchek, B. and Sunderam, V., PVM:Parallel Virtual Machine-A User’s Guide and Tutorial for Network Parallel Computing, MIT Press, Cambridge, MA, 1994.Google Scholar
  13. 13.
    Message Passing Interface Forum, “MPI:A message-passing interface standard,” Int. J. Supercomput. Appl. 8(3/4), 165-414, 1994.Google Scholar
  14. 14.
    Czajkowski, K., Foster, I., Kesselman, C., Sander, V. and Tuecke, S., “SNAP: A Protocol for negotiating service level agreements and coordinating resource management in distributed systems,” LNCS, 2537, pp. 153-183, 2002.Google Scholar
  15. 15.
    Distributed Architecture Group from Universidad Complutense, “Gridway” http://www.gridway.org/ , 2006.
  16. 16.
    Cluster Resources, Inc. “SILVER Design Specification,” http://www.supercluster.org/projects/silver/, Jan, 2004.
  17. 17.
    Frey, J., Tannenbaum, T., Livny, M., Foster, I. and Tuecke, S., “Condor-G: A Computation Management Agent for Multi-Institutional Grids,” Journal of Cluster Computing, 5, 3 July, 2002.Google Scholar
  18. 18.
    ROCKS Cluster Distribution, 2005, http://www.rocksclusters.org.
  19. 19.
    National Biomedical Computation Resource, 2005, http://nbcr.net.
  20. 20.
    Massie, M.L., Chun, B.N. and Culler, D.E., “The ganglia distributed monitoring system: design, implementation, and experience,” Parallel Computing, 30, pp. 817-840, 2004.CrossRefGoogle Scholar
  21. 21.
    Foster, I., Kesselman, C., Lee, C., Lindell, R, Nahrstedt, K., and Roy, A., “A Distributed Resource Management Architecture that Supports Advance Reservations and Co-Allocation,” Int. Workshop on Quality of Service, 1999.Google Scholar
  22. 22.
    “Gridsphere,” http://www.gridsphere.org, 2004.
  23. 23.
    Bhatia, K., Chandra, S., and Mueller, K., “GAMA: Grid Account Management Architecture,” 1st IEEE Int. Conf. on e-Science and Grid Computing, 2006.Google Scholar
  24. 24.
    Krishnan, S., Stearn, B., Bhatia, K., Baldridge, K. and Li, W.W., et al. “Opal: Simple Web Service Wrappers for Scientific Applications,” Int. Conf. for Web Services, 2006.Google Scholar
  25. 25.
    von Laszewski, G., Foster, I., Gawor, J. and Lane, P., “A Java Commodity Grid Kit,” Concurrency and Computation: Practice and Experience, 13, pp. 643-662, 2001.CrossRefGoogle Scholar
  26. 26.
    Ding, Z., Luo, Y., Wei, X., Misleh, C., Li, W.W., Arzberger, P.W. and Tatebe, O, “My WorkSphere: Integrated and Transparent Acess to Gfarm Computational Data Grid through GridSphere Portal with Metascheduler CSF4,” 3rd Int. Life Sciences Grid Workshop, 2006.Google Scholar
  27. 27.
    Allcock, W., Bester, J., Bresnahan, J., Chervenak, A., Liming, L. and Tuecke, S., “GridFTP: Protocol Extension to FTP for the Grid, Grid Forum Internet-Draft,” 2001.Google Scholar
  28. 28.
    Novotny, J., Tuecke, S. and Welch, V., “An Online Credential Repository for the Grid: MyProxy,” High Performance Distributed Computing, 2001Google Scholar
  29. 29.
    “Pacific Rim Applications and Grid Middleware Assembly,” 2004, http://www.pragma-grid.net/.
  30. 30.
    Darling, A., “The Design, Implementation, and Evaluation of mpiBLAST,” ClusterWorld, 2003.Google Scholar
  31. 31.
    “Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis,” 2006, http://camera.calit2.net/.
  32. 32.
    “TeraGrid,” 2004, http://www.teragrid.org.
  33. 33.
    “Open Science Grid,” 2006, http://www.opensciencegrid.org/.

Copyright information

©  2007

Authors and Affiliations

  • Zhaohui Ding
    • 1
  • Xiaohui Wei
    • 1
  • Yuan Luo
    • 1
  • Da ma
    • 1
  • Peter W. Arzberger
    • 2
  • Wilfred W. Li
    • 3
  1. 1.College of Computer Science & TechnologyJilin UniversityChangchun, JilinChina
  2. 2.Center for Research in Biological SystemsUniversity of CaliforniaSan Diego, La JollaUSA
  3. 3.San Diego Supercomputer CenterUniversity of CaliforniaSan Diego, La JollaUSA

Personalised recommendations