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A case for source-level transformations in MATLAB

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Published:31 December 1999Publication History
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Abstract

In this paper, we discuss various performance overheads in MATLAB codes and propose different program transformation strategies to overcome them. In particular, we demonstrate that high-level source-to-source transformations of MATLAB programs are effective in obtaining substantial performance gains regardless of whether programs are interpreted or later compiled into C or FORTRAN. We argue that automating such transformations provides a promising area of future research.

References

  1. 1 R. Allen and K, Kennedy. Automatic translation of FORTRAN programs to vector form. A CM Transactzons on Programming Languages and Systems, 9(2):491-542, October 1987.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. 2 S. Chauveau and F. Bodin. Menhir: An environment fo~ high performance MATLAB. In 4th Workshop on Languages, Compilers, and Run-t~me Systems for Scalable Computcrs, Pittsburgh, PA, May 1998.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. 3 L. De Rose. Compiler Techn,ques for MA TLAB programs. PhD thesis, University of illinois at Urbana-Champaign, 1996.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. 4 L. De Rose, K. Gallivan, E. Gallopoulos, B. Marsolf, and D. Padua. FALCON: A MAT- LAB interactive restructuring compiler. In Languages and Compders for Parallel Comput- ,ng, pages 269-288. Springer-Verlag, August 1995.]] Google ScholarGoogle Scholar
  5. 5 P. Drakenberg, P Jacobson, and B. Kagstrom. A CONLAB compiler for a distributed memory multicomputer. In 6th SlAM Conference on Parallel Processzng for Scientific Comput- ,ug, volume 2, pages 814~821, 1993.]]Google ScholarGoogle Scholar
  6. 6 J. Eaton. GNU Octave. http://w ww. che. wisc. edu / octave.]]Google ScholarGoogle Scholar
  7. 7 R. Gupta. A fresh look at optimizing array bound checking. In Prograrnrnzng Languages, Design and lmplementat,on. ACM SIGPLAN, Jun 1990.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. 8 J. Ho}lingsworth, K. Liu, and P. Pauca. Parallel Toolbox for MATLAB. Wake Forest University, 1996. http ://www.mthcsc.wfu.edu/pt/pt.html.]]Google ScholarGoogle Scholar
  9. 9 Y. Keren. MATCOM: A MATLAB to C++ translator and support libraries. Technical report, Israel Institute of Technology, 1995.]]Google ScholarGoogle Scholar
  10. 10 Kuck and Associates, Inc. KAP for IBM Fortran and C. http://www, kai .corn/product/ibminf.html.]]Google ScholarGoogle Scholar
  11. 11 B. Marsolf. Techmques for the lnteractzve Development of Numerzcal Lznear Algebra L,- brames for Sc,ent~c Computat,on. PhD thesis, University of Illinois at Urbana-Champaign, 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. 12 The MathWorks, Inc. How Do I Vectomze My Code? http://www.mathworks.com/support/technotes/vS/1100/1109.shtml.]]Google ScholarGoogle Scholar
  13. 13 The MathWorks, Inc. MATLAB Compder, 1995.]]Google ScholarGoogle Scholar
  14. 14 V. Marion and K. Pingali. High-level semantic optimization of numerical codes. In Internat, onal Conference of Supercomputzng (ICS'99), June 1999.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. 15 V. Menon and A. Trefethen. MultiMAT- LAB: Integrating MATLAB with high performance parallel computing. In Supercomputzng, November 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. 16 Pacific Sierra Research Corporation. VAST-2 for XL Fortran. h t t p://www, psrv .corn/vast/vast.xlf. html.]]Google ScholarGoogle Scholar
  17. 17 S. Pawletta, T. Pawletta, and W. Drewelow. Comparison of parallel simulation techniques - MATLAB/PSI. Ssmulat~on News Europe, 13:38-39, 1995.]]Google ScholarGoogle Scholar
  18. 18 The MATCH Project,. A MATLAB compilation environment for distributed heterogeneous adaptive computing systems. http://w ww.ece, nwu.edu/cpdc / Match / Match.html]]Google ScholarGoogle Scholar
  19. 19 M. Quinn, A. Malishevsky, and N. Seelam. Otter: Bridging the gap between MATLAB and ScaLAPACK. In 7th IEEE International Symposture on H~gh Performance D~str, buted Computing, August 1998.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. 20 S. Ramaswamy, E. W. Hodges, and P. Bancrjee. Compiling MATLAB programs to ScaLA- PACK: Exploiting task and data parallelism. in Proc. of the International Parallel Processmg Symposzum, April 1996.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. 21 M. Wolfe. H, gh Performance Compders for Parallel Computing. Addison-Wesley Publishing Company, 1995.]] Google ScholarGoogle ScholarDigital LibraryDigital Library

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        • Published in

          cover image ACM SIGPLAN Notices
          ACM SIGPLAN Notices  Volume 35, Issue 1
          Jan. 2000
          173 pages
          ISSN:0362-1340
          EISSN:1558-1160
          DOI:10.1145/331963
          Issue’s Table of Contents
          • cover image ACM Conferences
            DSL '99: Proceedings of the 2nd conference on Domain-specific languages
            December 1999
            176 pages
            ISBN:1581132557
            DOI:10.1145/331960

          Copyright © 1999 Authors

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          Association for Computing Machinery

          New York, NY, United States

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          • Published: 31 December 1999

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