Nothing Special   »   [go: up one dir, main page]

Skip to main content

High performance neurocomputing: Industrial and medical applications of the RAIN system

  • 1. Industrial and General Applications
  • Conference paper
  • First Online:
High-Performance Computing and Networking (HPCN-Europe 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1401))

Included in the following conference series:

  • 253 Accesses

Abstract

We describe here the RAIN project, aimed at demonstrating the use of High Performance Computing and Networking technologies in neural network applications for industry and medicine. The target architecture of the demonstrators is a workstation cluster: a choice suggested by the cost-effectiveness of this architecture. In order to manage both the cluster and the applications running on it, we built a Java-based interface that can be executed by any Java-enhanced browser.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, E.C., Dongarra, J.: Performance of LAPACK: A Portable Library of Numerical Linear Algebra Routines. Proc. of the IEEE 81 (1993) 1094–1101

    Google Scholar 

  2. Anguita, D., Parodi, G., Zunino, R.: An Efficient Implementation of BP on RISCbased Workstations. Neurocomputing 6 (1994) 57–65

    Google Scholar 

  3. Anguita, D., DaCanal, A., DaCanal, W., Falcone, A., Scapolla, A.M.: On the distributed implementation of back-propagation. Proc. of ICANN 1994, 1376–1379

    Google Scholar 

  4. Corana, A., Rolando, C., Ridella, S.: A Highly Efficient Implementation of Back-propagation Algorithm on SIMD Computers. High Performance Computing, J.-L.Delhaye and E.Gelenbe (Eds.) (1989) 181–190

    Google Scholar 

  5. Corana, A., Rolando, C., Ridella, S.: Use of Level 3 BLAS Kernels in Neural Networks: The Back-propagation algorithm. Parallel Computing 89 (1990) 269–274

    Google Scholar 

  6. Frey, P.W., Slate, D.J.: Letter Recognition Using Holland-style Adaptive Classifiers. Machine Learning 6 (1991) 161–182

    Google Scholar 

  7. Geist, A. et al.: PVM: Parallel Virtual Machine, a Users's Guide and Tutorial for Networked Parallel Computing. The MIT Press (1994)

    Google Scholar 

  8. Hjorth, J.S.: Computer Intensive Statistical Methods: Validation Model Selection and Bootstrap. Chapman & Hall (1994)

    Google Scholar 

  9. Karp, A.H., Lusk, E., Bailey, D.H.: 1997 Gordon Bell Prize Winners. IEEE Computer 31 (1998) 86–92

    Google Scholar 

  10. Marsh, A.: EUROMED — Combining WWW and HPCN to Support Advanced Medical Imaging, International Conference and Exhibition HPCN Europe 1997, Vienna, pp. 95–104

    Google Scholar 

  11. Murphy, P.M., Aha, D.W.: UCI Repository of machine learning databases http://www.ics.uci.edu/-mlearn/MLRepository.html. Irvine, CA: University of California, Department of Information and Computer Science (1994)

    Google Scholar 

  12. Panda, D.K. and Ni, L.M.: Special Issue on Workstation Clusters and Network-Based Computing. J. of Parallel and Distributed Computing 40 (1997)

    Google Scholar 

  13. Raudys, S.J. and Jain, A.K.: Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners. IEEE Trans. on Pattern Analysis and Machine Intelligence 13 (1991) 252–263

    Google Scholar 

  14. Rovetta S., Zunino R., Buffrini L., Rovetta G.: Prototyping neural networks learn Lyme borreliosis. 8th IEEE Symp. on Computer-Based Medical Systems (1995)

    Google Scholar 

  15. Rumelhart, D.E. and McClelland, J.L.: Parallel Distributed Processing Vol. 1. MIT Press (1986)

    Google Scholar 

  16. Thurman, D.:http://www.isye.gatech.edu/JavaPVM/

    Google Scholar 

  17. Wang, C., Venkatesh, S.S., Judd, J.S.: Optimal Stopping and Effective Machine Complexity in Learning. Advances in Neural Information Processing Systems 6 (1994) 303–310

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Peter Sloot Marian Bubak Bob Hertzberger

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Anguita, D., Boni, A., Chirico, M., Giudici, F., Scapolla, A.M., Parodi, G. (1998). High performance neurocomputing: Industrial and medical applications of the RAIN system. In: Sloot, P., Bubak, M., Hertzberger, B. (eds) High-Performance Computing and Networking. HPCN-Europe 1998. Lecture Notes in Computer Science, vol 1401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0037130

Download citation

  • DOI: https://doi.org/10.1007/BFb0037130

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64443-9

  • Online ISBN: 978-3-540-69783-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics