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International Conference on High-Performance Computing and Networking

HPCN-Europe 1994: High-Performance Computing and Networking pp 211–214Cite as

Automatic Differentiation on distributed memory MIMD systems

  • Numerical Algorithms for Engineering
  • Conference paper
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 797))

Abstract

The main target of this paper is to develop an innovative software for the Automatic Differentiation of separable functions, exploiting the parallel features of a distributed memory parallel system (MIMD architecture).

The developed software, written in Fortran, uses the Express tool, thus being easily portable on the several parallel systems supporting Express. It consists of a set of subroutines calculating the function, gradient and hessian values; due to its user friendly interface, it is greatly suitable for using inside Fortran iterative algorithms needing these values; it is not necessary any pre-compiling phase.

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References

  1. Bartholomew-Biggs, M.C.-Dixon, L.C.W.-Maany, Z.A.-Mohesininia M., Three papers on Automatic Differentiation Presented on the IFAC Symposium on “Dynamic Modelling and Control of National Economics”, Edinburgh Scotland. Report n. 223, July 1989, Numerical Optimisation Centre, The Hatfield Polytechnic.

    Google Scholar 

  2. Christianson B., Automatic Hessians by Reverse Accumulation., Report n. 228, April 1990, Numerical Optimization Centre, The Hatfield Polytechnic.

    Google Scholar 

  3. Dixon L.C.W., Automatic Differentiation and Parallel Processing in Optimisation. Report n.180, April 1987, Numerical Optimisation Centre, The Hatfield Polytechnic.

    Google Scholar 

  4. Fischer H., Automatic Differentiation: How to Compute the Hessian Matrix., Report n. 26, 1987, Technische Universitat Munchen.

    Google Scholar 

  5. Fischer H., Automatic Differentiation: Parallel Computation of Function, Gradient and Hessian Matrix., Parallel Computing 13 (1990), North-Holland.

    Google Scholar 

  6. Griewank A., On Automatic Differentiation. Preprint MCS-P10-1088, October 1988, Mathematics and Computer Science Division, Argonne National Laboratory.

    Google Scholar 

  7. Morè, J.J., Garbow, B.S., and Hillstrom, K.E., Testing Uncostrained Optimization Software, ACM Transaction on Mathematical Software, Vol. 7, p. 17, 1981.

    Google Scholar 

  8. Rall L.B., Automatic Differentiation: Techniques and Applications. Lecture Notes in Computer Science, p. 120, Springer-Verlag, Berlin, 1981.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics, Informatics and Systems, University of Calabria, 87036, Rende, Italy

    Luigi De Luca & Piero Fiorino

Authors
  1. Luigi De Luca
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  2. Piero Fiorino
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Editor information

Wolfgang Gentzsch Uwe Harms

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© 1994 Springer-Verlag Berlin Heidelberg

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De Luca, L., Fiorino, P. (1994). Automatic Differentiation on distributed memory MIMD systems. In: Gentzsch, W., Harms, U. (eds) High-Performance Computing and Networking. HPCN-Europe 1994. Lecture Notes in Computer Science, vol 797. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57981-8_118

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  • DOI: https://doi.org/10.1007/3-540-57981-8_118

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57981-6

  • Online ISBN: 978-3-540-48408-0

  • eBook Packages: Springer Book Archive

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