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International Workshop on Languages and Compilers for Parallel Computing

LCPC 1995: Languages and Compilers for Parallel Computing pp 480–499Cite as

Automatic parallelization of the conjugate gradient algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1033))

Abstract

The conjugate gradient (CG) method is a popular Krylov space method for solving systems of linear equations of the form Ax = b, where A is a symmetric positive-definite matrix. This method can be applied regardless of whether A is dense or sparse. In this paper, we show how restructuring compiler technology can be applied to transform a sequential, dense matrix CG program into a parallel, sparse matrix CG program. On the IBM SP-2, the performance of our compiled code is comparable to that of handwritten code from the PETSc library at Argonne.

This research was supported by an NSF Presidential Young Investigator award CCR-8958543, NSF grant CCR-9008526, ONR grant N00014-93-1-0103, and a grant from Hewlett-Packard Corporation.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Cornell University, 14853, Ithaca, New York

    Vladimir Kotlyar, Keshav Pingali & Paul Stodghill

Authors
  1. Vladimir Kotlyar
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  2. Keshav Pingali
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  3. Paul Stodghill
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Editor information

Chua-Huang Huang Ponnuswamy Sadayappan Utpal Banerjee David Gelernter Alex Nicolau David Padua

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© 1996 Springer-Verlag Berlin Heidelberg

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Kotlyar, V., Pingali, K., Stodghill, P. (1996). Automatic parallelization of the conjugate gradient algorithm. In: Huang, CH., Sadayappan, P., Banerjee, U., Gelernter, D., Nicolau, A., Padua, D. (eds) Languages and Compilers for Parallel Computing. LCPC 1995. Lecture Notes in Computer Science, vol 1033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0014219

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  • DOI: https://doi.org/10.1007/BFb0014219

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60765-6

  • Online ISBN: 978-3-540-49446-1

  • eBook Packages: Springer Book Archive

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