Programming Paradigms for Scientific Problem Solving Environments

  • Dennis Gannon
  • Marcus Christie
  • Suresh Marru
  • Satoshi Shirasuna
  • Aleksander Slominski
Part of the IFIP The International Federation for Information Processing book series (IFIPAICT, volume 239)

Abstract

Scientific problem solving environments (PSEs) are software platforms that allow a community of scientific users the ability to easily solve computational problems within a specific domain. They are designed to hide the details of general purpose programming by allowing the problem to be expressed, as much as possible, in the scientific language of the discipline. In many areas of science, the nature of computational problems has evolved from simple desktop calculations to complex, multidisciplinary activities that require the monitoring and analysis of remote data streams, database and web search and large ensembles of supercomputer-hosted simulations. In this paper we will look at the class of PSE that have evolved for these “Grid based” systems and we will consider the associated programming models they support. It will be argued that a hybrid of three standard models provides the right programming support to handle the majority of the applications of these PSEs.

References

  1. 1.
    S. Wolfram, Mathematical a system for doing mathematics by computer, 1991, Adison Wesley Co.Google Scholar
  2. 2.
    D. Hanselman, B. Littlefield, Mastering MATLAB 5: A Comprehensive Tutorial and Reference, (1997)–Prentice Hall PTR Upper Saddle River, NJ, USAGoogle Scholar
  3. 3.
    C. Upson, T. Faulhaber, Jr., D. Kamins, D. H. Laidlaw, D. Schlegel, J. Vroom, R. Gurwitz, A. van Dam, The Application Visualization System: A Computational Environment for Scientific Visualization, IEEE Computer Graphics and Applications archive Vol. 9, no. 4, July 1989, pp. 30–42CrossRefGoogle Scholar
  4. 4.
    S. Parker, C. Johnson, SCIRun: a scientific programming environment for computational steering, Proceedings of the (1995) CM/IEEE conference on Supercomputing, San Diego, California, United States Article No. 52, 1995.Google Scholar
  5. 5.
    I. Taylor, E. Deelman, D. Gannon, M. Shields (Eds.), Workflows for e-Science Scientific Workflows for Grids, Springer, 2007.Google Scholar
  6. 6.
    D. Pennington, D. Higgins, A. Townsend Peterson, M. Jones, B. Ludascher, S. Bowers, Ecological Niche Modeling Using the Kepler Workflow System, in Workflows for e-Science Scientific Workflows for Grids, Springer, 2007.Google Scholar
  7. 7.
    T. Oinn, P. Li, D. Kell, C. Goble, A. Goderis, M. Greenwood, D. Hull, R. Stevens, D. Turi and J. Zhao, Taverna / myGrid: aligning a workflow system with the life sciences community, in Workflows for e-Science Scientific Workflows for Grids, Springer, 2007.Google Scholar
  8. 8.
    E. Deelman, G. Mehta, G. Singh, M-H. Su, K. Vahi, Pegasus: Mapping LargeScale Workflows to Distributed Resources, in Workflows for e-Science Scientific Workflows for Grids, Springer, 2007.Google Scholar
  9. 9.
    A. Slominski, Adapting BPEL to Scientific Workflows, in Workflows for eScience Scientific Workflows for Grids, Springer, 2007.Google Scholar
  10. 10.
    K. Droegemeier, D. Gannon, D. Reed, B. Plale, J. Alameda, T. Baltzer, K. Brewster, R. Clark, B. Domenico, S. Graves, E. Joseph, D. Murray, R. Ramachandran, M. Ramamurthy, L. Ramakkrisshnan, J. Rushing, D. Webeer, R. Wilhelmson, A. Wilson, M. Xue, S. Yalda, Service-Oriented Environments for Dynamically Interacting with Mesoscale Weather, CiSE, Computing in Science & Engineering — November (2005), vol. 7, no. 6, pp. 12–29.Google Scholar
  11. 11.
    B. Plale, D. Gannon, J. Brotzge, K. Droegemeier, J. Kurose, D. McLaughlin, R. Wilhelmson, S. Graves, M. Ramamurthy, R. Clark, S. Yalda, D. Reed, E. Joseph, V. Chandrasekar, CASA and LEAD: Adaptive Cyberinfrastructure for Real-Time Multiscale Weather Forecasting, IEEE Computer, November 2006 (Vol. 39, No. 11) pp. 56–64Google Scholar
  12. 12.
    Gopi Kandaswamy, Dennis Gannon, Liang Fang, Yi Huang, Satoshi Shirasuna, Suresh Marru, Building Web Services for Scientific Applications, IBM Journal of Research and Development, Vol 50, No. 2/3 March/May 2006.Google Scholar
  13. 13.
    I Foster, C Kesselman, Globus: A metacomputing infrastructure toolkit, International Journal of Supercomputer Applications, 1997Google Scholar
  14. 14.
    Y. Simmhan, S. Lee Pallickara, N. Vijayakumar, and B. Plale, Data Management in Dynamic Environment-driven Computational Science, IFIP Working Conference on Grid-Based Problem Solving Environments (WoCo9) August (2006), to appear as Springer-Verlag Lecture Notes in Computer Science (LNCS).Google Scholar
  15. 15.
    Beth Plale, Dennis Gannon, Yi Huang, Gopi Kandaswamy, Sangmi Lee Pallickara, and Aleksander Slominski, Cooperating Services for Data-Driven Computational Experimentation“, CiSE, Computing in Science & Engineering — September 2005 vol. 7 issue 5, pp. 34–43CrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • Dennis Gannon
    • 1
  • Marcus Christie
    • 1
  • Suresh Marru
    • 1
  • Satoshi Shirasuna
    • 1
  • Aleksander Slominski
    • 1
  1. 1.Department of Compute Science, School of InformaticsIndiana UniversityBloomington

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