The Use of PIMA(GE) Library for Efficient Image Processing in a Grid Environment

  • A. Clematis
  • D. D’Agostino
  • A. Galizia
Conference paper
Part of the Signals and Communication Technology book series (SCT)


Grids enable the creation of virtual laboratories for the collaborative use of\break sophisticated instruments producing large amount of data that have to be processed in order to extract knowledge. In this context, a very important task is related with image processing, since most of the data are images that have to be adequately analyzed. We present a Grid-aware version of the Parallel IMAGE processing GEnoa library (PIMA(GE)2 Lib). The major features of our approach are the preservation of the ease in the development of parallel image processing applications and the possibility to efficiently exploit the Grid resources for their executions. The resulting tool, called PIMA(GE)2 Grid, is based on the Grid Service technology; from the user point of view, it acts as an intermediate layer between Grid resources and parallel image processing applications. PIMA(GE)2 Grid represents a feasible solution to exploit multiple and computationally intensive image processing applications in a virtual laboratory.


Parallel Application Grid Resource Grid Environment Resource Selection Edge Detection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    G. Andronico, V. Ardizzone, R. Barbera, R. Catania, A. Carrieri, A. Falzone, E. Giorgio, G. La Rocca, S. Monforte, M. Pappalardo, G. Passaro, G. Platania (2005). “GILDA: The Grid INFN Virtual Laboratory for Dissemination Activities”. Procs of TRIDENTCOM’05, 304–305.Google Scholar
  2. [2]
    K. Amin, G. von Laszewski, M. Hategan, R. Al-Ali, O. Rana, D. Walker (2006). “An Abstraction Model for a Grid Execution Framework”. Journal of Systems Architecture, vol. 52, no. 2, 73–87.CrossRefGoogle Scholar
  3. [3]
    S. Balay, W. Gropp, L. Curfman McInnes, B. Smit (1996). “PETSc 2.0 Users’ Manual”. Technical Report ANL-95/11 revision 2.0.17, Argonne National Laboratory.Google Scholar
  4. [4]
    R.F. Boisvert (2002). “Mathematical Software: Past, Present and Future”. Computational Science, Mathematics, and Software, Purdue University Press.Google Scholar
  5. [5]
    A. Clematis, A. Corana, D. D’Agostino, V. Gianuzzi, A. Merlo (2006). “Resource Selection and Application Execution in a Grid: A Migration Experience from GT2 to GT4”. Procs. of the International Symposium on Grid computing, high-performAnce and Distributed Applications (GADA), LNCS 4276, Springer, 1132–1142.CrossRefGoogle Scholar
  6. [6]
    A. Clematis, D. D’Agostino, A. Galizia (2005). “A Parallel IMAGE Processing Server for Distributed Applications”. Parallel Computing: Concurrent & Future Issues of High-End Computing, John von Neumann Institute for Computing (NIC) series, vol. 33, 607–614.Google Scholar
  7. [7]
    A. Clematis, D. D’Agostino, A. Galizia (2005). “An Object Interface for Interoperability of Image Processing Parallel Library in a Distributed Environment”. Procs. of ICIAP 2005, LNCS 3617, Springer, 584–591.Google Scholar
  8. [8]
    A. Clematis, D. D’Agostino, A. Galizia (2007). “Parallel I/O Aspects in $PIMA(GE)^2$ Lib”. Parallel Computing: Architectures, Algorithms and Applications, Proceedings of the ParCo 2007, Advances in Parallel Computing, vol.15, IOS Press, 441–448.Google Scholar
  9. [9]
    A. Clematis, D. D’Agostino, A. Galizia (2006). “The Parallel IMAGE processing GEnoa Library: $\rmPIMA(GE)2$ Lib”. Technical Report IMATI-CNR-Ge, no. 21/2006Google Scholar
  10. [10]
    T. Delaitre, T. Kiss, A. Goyeneche, G. Terstyanszky, S. Winter, P. Kacsuk (2005). “GEMLCA: Running Legacy Code Applications as Grid Services”. Journal of Grid Computing, vol. 3, no. 1–2, 75–90.CrossRefGoogle Scholar
  11. [11]
    J. Dongarra, L.S. Blackford, J. Choi, A. Cleary, E. D’Azeuedo, J. Demmel, I. Dhillon, S. Hammarling, G. Henry, A. Petitet, K. Stanley, D. Walker, R.C. Whaley (1997). ScaLAPACK user’s guide. Society for Industrial and Applied Mathematics, Philadelphia.zbMATHGoogle Scholar
  12. [12]
    E. Floros, Y. Cotronis (2004). “Exposing MPI Applications as Grid Services”. Procs. of EuroPar 2004, LNCS 3149, Springer, 436–443.Google Scholar
  13. [13]
    I. Forster, C. Kesselman (2004). The Grid: Blueprint for a New Computing Infrastructure, 2nd edition. Morgan Kaufmann.Google Scholar
  14. [14]
    S. Hastings, T. Kurc, S. Langella, U. Catalyurek, T. Pan, J. Saltz (2003). “Image Processing or the Grid: A Toolkit or Building Grid-Enabled Image Processing Applications”. Procs. of the 3rd International Symposium on Cluster Computing and the Grid, 36–43.Google Scholar
  15. [15]
    P. Heinzlreiter, D. Kranzlmuller (2003). “Visualization Services on the Grid: The Grid Visualization Kernel”. Parallel Processing Letters, vol. 13, no. 2, 135–148.MathSciNetCrossRefGoogle Scholar
  16. [16]
    T. Ho, D. Abramson (2006). “A Unified Data Grid Replication Framework”. Procs. of the 2nd IEEE International Conference on e-Science and Grid Computing, IEEE Computer Society, 52.Google Scholar
  17. [17]
    X. Huang, L. Huang, M. Li (2006). “Grid-Enabled Medical Image Processing Application System Based on OGSA-DAI Techniques”. APWeb Workshops 2006, LNCS 3842, Springer, 460–464.Google Scholar
  18. [18]
    J. Lebak, J. Kepner, H. Hoffmann, E. Rudtledge (2005). “Parallel VSIPL++: An Open Standard Library for High-Performance Parallel Signal Processing”. Proceedings of the IEEE, vol. 93, no. 2, 313–330.Google Scholar
  19. [19]
    M. Li, O.F. Rana, D.W. Walker (2001). “Wrapping MPI-Based Legacy Codes as Java/CORBA Components”. Future Generation Computer System, vol. 18, no. 2, 213–231.CrossRefGoogle Scholar
  20. [20]
    S. Liang (1999). Java Native Interface: Programmer’s Guide and Specification. Pearson Education.Google Scholar
  21. [21]
    M.S. Müller, M. Hess, E. Gabriel (2003). “Grid Enabled MPI Solutions for Clusters”. Procs of the 3rd IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2003), 18–25.Google Scholar
  22. [22]
    A. Petitet, S. Blackford, J. Dongarra, B. Ellis, G. Fagg, K. Roche, S. Vadhiyar (2001). “Numerical Libraries and the Grid”. The International Journal of High Performance Computing Applications, vol. 15, no. 4, 359–374.CrossRefGoogle Scholar
  23. [23]
    G. Ritter, J. Wilson (2001). Handbook of Computer Vision Algorithms in Image Algebra, 2nd edition. CRC Press.Google Scholar
  24. [24]
    F.J. Seinstra, D. Koelma (2001). “The Lazy Programmer’s Approach to Building a Parallel Image Processing Library”. Procs. of the 15th International Parallel & Distributed Processing Symposium (IPDPS 2001), IEEE Computer Society, 115.Google Scholar
  25. [25]
    F. Seinstra, D. Koelma, J.M. Geusebroek (2002). “A Software Architecture for User Transparent Parallel Image Processing”. Parallel Computing, vol. 28, no. 7–8, 967–993.CrossRefGoogle Scholar
  26. [26]
    G. Sipos, P. Kacsuk (2006). “Multi-grid, Multi-user Workflows in the P-GRADE Portal”. Journal of Grid Computing, vol. 3, no. 3–4, 221–238.Google Scholar
  27. [27]
    H.M. Sneed (2000). “Encapsulation of Legacy Software: A Technique for Reusing Legacy Software Components”. Annals of Software Engineering, vol. 9, no. 1–4, 293–313.Google Scholar
  28. [28]
    A. Szalay, J. Gray (2001). “The World-Wide Telescope”. Science, vol. 293, no. 5537, 2037–2040.CrossRefGoogle Scholar
  29. [29]
    The Grid Application Development Software Project (GrADS),
  30. [30]
    The Human Proteome Folding Project,
  31. [31]
    N. Wang, D.C. Schmitdt, D. Levine (2000). An Overview of the CORBA Component Model. Addison-Wesley.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • A. Clematis
    • 1
  • D. D’Agostino
    • 1
  • A. Galizia
    • 1
  1. 1.IMATI-CNRVia De Marini 6Italy

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