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Efficient distribution analysis via graph contraction

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Languages and Compilers for Parallel Computing (LCPC 1995)

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

Abstract

Alignment and distribution of array data should be managed by optimizing compilers for parallel computers, but current approaches to the distribution problem formulate it as an NP-complete graph optimization problem. The graphs arising in applications are large and difficult to optimize. In this paper, we improve some earlier results on methods that use graph contraction to reduce the size of a distribution problem. We report on an experiment using seven example programs that show these contraction operations to be effective in practice; we obtain from 70 to 99 percent reductions in problem size, the larger number being more typical, without loss of solution quality.

The work of these authors was supported by the NAS Systems Division via Contract NAS 2-13721 between NASA and the Universities Space Research Association (USRA).

Copyright 1995 by Xerox Corporation. All rights reserved.

The work of this author was supported by the NAS Systems Division via Contract NAS 2-13721 between NASA and the Universities Space Research Association (USRA) while he was a postdoctoral scientist at RIACS. Current affiliation: University of North Carolina, Chapel Hill.

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References

  1. R. Bixby, K. Kennedy, and U. Kremer. Automatic data layout using 0–1 integer programming. Technical Report CRPC-TR93349-S, Center for Research on Parallel Computation, Rice University, Houston, TX, November 1993.

    Google Scholar 

  2. S. Chatterjee, J. R. Gilbert, and R. Schreiber. Mobile and replicated alignment of arrays in data-parallel programs. In Proceedings of Supercomputing'93, pages 420–429, Portland, OR, November 1993.

    Google Scholar 

  3. S. Chatterjee, J. R. Gilbert, R. Schreiber, and T. J. Sheffler. Array distribution in data-parallel programs. In K. Pingali, U. Banerjee, D. Gelernter, A. Nicolau, and D. Padua, editors, Proceedings of the Seventh Annual Workshop on Languages and Compilers for Parallel Computing, number 892 in Lecture Notes in Computer Science, pages 76–91, Ithaca, NY, August 1994. Springer-Verlag. Also available as RIACS Technical Report 94.09.

    Google Scholar 

  4. S. Chatterjee, J. R. Gilbert, R. Schreiber, and T. J. Sheffler. Modeling data-parallel programs with the alignment-distribution graph. Journal of Programming Languages, 2:227–258, 1994. Special issue on compiling and run-time issues for distributed address space machines.

    Google Scholar 

  5. J. R. Gilbert, C. Moler, and R. Schreiber. Sparse matrices in MATLAB: Design and implementation. SIAM J. Matrix Anal. Appl., 13(1):333–356, January 1992.

    Article  Google Scholar 

  6. A. V. Goldberg and R. E. Tarjan. A new approach to the maximum-flow problem. J. ACM, 35(4):921–940, October 1988.

    Article  Google Scholar 

  7. M. Gupta. Automatic Data Partitioning on Distributed Memory Multicomputers. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, IL, Sept. 1992. Available as technical reports UILU-ENG-92-2237 and CRHC-92-19.

    Google Scholar 

  8. P. Hough and T. J. Sheffler. A performance analysis of collective communication on the CM-5. RIACS Technical Report in preparation.

    Google Scholar 

  9. U. Kremer. NP-completeness of dynamic remapping. Technical Report CRPC-TR93-330-S, Center for Research on Parallel Computation, Rice University, August 1993. Appears in the Proceedings of the Fourth Workshop on Compilers for Parallel Computers, Delft, The Netherlands, December 1993.

    Google Scholar 

  10. D. J. Palermo and P. Banerjee. Automatic selection of dynamic data partitioning schemes for distributed-memory multicomputers. Talk presented at the Workshop on Automatic Data Layout and Performance Prediction. Center for Research on Parallel Computing. April 19, 1995. Rice University.

    Google Scholar 

  11. T. J. Sheffler, R. Schreiber, J. R. Gilbert, and S. Chatterjee. Aligning parallel arrays to reduce communication. In Proceedings of Frontiers '95: The Fifth Symposium on the Frontiers of Massively Parallel Computation., pages 324–331, February 1995.

    Google Scholar 

  12. S. Wholey. Automatic Data Mapping for Distributed-Memory Parallel Computers. PhD thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, May 1991. Available as Technical Report CMU-CS-91-121.

    Google Scholar 

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Author information

Author notes

  1. Siddhartha Chatterjee

    Present address: University of North Carolina, Chapel Hill

Authors and Affiliations

  1. Research Institute for Advanced Computer Science, USA

    Thomas J. Sheffler & Robert Schreiber

  2. University of Maryland, USA

    William Pugh

  3. Xerox Palo Alto Research Center, USA

    John R. Gilbert

Authors
  1. Thomas J. Sheffler
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  2. Robert Schreiber
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  3. William Pugh
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  4. John R. Gilbert
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  5. Siddhartha Chatterjee
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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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Sheffler, T.J., Schreiber, R., Pugh, W., Gilbert, J.R., Chatterjee, S. (1996). Efficient distribution analysis via graph contraction. 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/BFb0014212

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  • DOI: https://doi.org/10.1007/BFb0014212

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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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