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Extending Index-Array Properties for Data Dependence Analysis

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Languages and Compilers for Parallel Computing (LCPC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11882))

Abstract

Automatic parallelization is an approach where a compiler analyzes serial code and identifies computations that can be rewritten to leverage parallelism. Many data dependence analysis techniques have been developed to determine which loops in a code can be parallelized. With code that includes indirect array accesses through what are commonly called index arrays, such data dependence analysis is restricted in the conclusions that can be drawn at compile time. Various approaches that use index array properties such as monotonicity have been shown to more effectively find parallel loops. In this paper, we extend the kinds of properties about index arrays that can be expressed, show how to convert loop-carried data dependence relations and relevant index-array properties to constraints that can be provided to the Z3 SMT solver, and evaluate the impact of using such index-array properties on identifying parallel loops in a set of numerical benchmarks.

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References

  1. Ctop research group webpage at utah (2018). http://ctop.cs.utah.edu/ctop/

  2. Sparselib++ homepage (2018). https://math.nist.gov/sparselib++/

  3. Z3 git homepage (2018). https://github.com/Z3Prover/z3/wiki

  4. Barrett, C., Fontaine, P., Tinelli, C.: The Satisfiability Modulo Theories Library (SMT-LIB) (2016). www.SMT-LIB.org

  5. Barthou, D., Collard, J.F., Feautrier, P.: Fuzzy array dataflow analysis. J. Parallel Distrib. Comput. 40(2), 210–226 (1997)

    Article  Google Scholar 

  6. Bradley, A.R., Manna, Z., Sipma, H.B.: What’s decidable about arrays? In: Emerson, E.A., Namjoshi, K.S. (eds.) VMCAI 2006. LNCS, vol. 3855, pp. 427–442. Springer, Heidelberg (2005). https://doi.org/10.1007/11609773_28

    Chapter  Google Scholar 

  7. Cheshmi, K., Kamil, S., Strout, M.M., Dehnavi, M.M.: Sympiler: transforming sparse matrix codes by decoupling symbolic analysis. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017, pp. 13:1–13:13. ACM, New York (2017). https://doi.org/10.1145/3126908.3126936

  8. Davis, T.A., Hu, Y.: The university of Florida sparse matrix collection. ACM Trans. Math. Softw. (TOMS) 38(1), 1:1–1:25 (2011). https://doi.org/10.1145/2049662.2049663. Article no. 1. http://doi.acm.org/10.1145/2049662.2049663

    Article  MathSciNet  MATH  Google Scholar 

  9. Ge, Y., de Moura, L.: Complete instantiation for quantified formulas in satisfiabiliby modulo theories. In: Bouajjani, A., Maler, O. (eds.) CAV 2009. LNCS, vol. 5643, pp. 306–320. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02658-4_25

    Chapter  Google Scholar 

  10. Habermehl, P., Iosif, R., Vojnar, T.: What else is decidable about integer arrays? In: Amadio, R. (ed.) FoSSaCS 2008. LNCS, vol. 4962, pp. 474–489. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78499-9_33

    Chapter  Google Scholar 

  11. Lin, Y., Padua, D.: Compiler analysis of irregular memory accesses. In: Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation, vol. 35, pp. 157–168. ACM, New York, May 2000

    Google Scholar 

  12. McKinley, K.: Dependence analysis of arrays subscriptecl by index arrays. Technical report. TR91187, Rice University (1991)

    Google Scholar 

  13. Oancea, C.E., Rauchwerger, L.: Logical inference techniques for loop parallelization. In: Proceedings of the 33rd ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2012. ACM, New York (2012)

    Google Scholar 

  14. Oancea, C.E., Rauchwerger, L.: A hybrid approach to proving memory reference monotonicity. In: Rajopadhye, S., Mills Strout, M. (eds.) LCPC 2011. LNCS, vol. 7146, pp. 61–75. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36036-7_5

    Chapter  Google Scholar 

  15. Paek, Y., Hoeflinger, J., Padua, D.: Simplification of array access patterns for compiler optimizations. In: Proceedings of the ACM SIGPLAN 1998 Conference on Programming Language Design and Implementation, PLDI 1998, pp. 60–71. ACM, New York (1998)

    Google Scholar 

  16. Pugh, W., Wonnacott, D.: Nonlinear array dependence analysis. In: Third Workshop on Languages, Compilers, and Run-Time Systems for Scalable Computers, Troy, New York, May 1995

    Google Scholar 

  17. Pugh, W., Wonnacott, D.: Constraint-based array dependence analysis. ACM Trans. Program. Lang. Syst. 20(3), 635–678 (1998)

    Article  Google Scholar 

  18. Rus, S.: Hybrid analysis of memory references and its application to automatic parallelization. Ph.D. thesis, Texas A&M (2006)

    Google Scholar 

  19. Rus, S., Hoeflinger, J., Rauchwerger, L.: Hybrid analysis: static & dynamic memory reference analysis. Int. J. Parallel Program. 31(4), 251–283 (2003)

    Article  Google Scholar 

  20. Shostak, R.E.: A practical decision procedure for arithmetic with function symbols. J. ACM 26(2), 351–360 (1979). https://doi.org/10.1145/322123.322137

    Article  MathSciNet  MATH  Google Scholar 

  21. Soltan Mohammadi, M., et al.: Sparse matrix code dependence analysis simplification at compile time. ArXiv e-prints, July 2018

    Google Scholar 

  22. Strout, M.M., LaMielle, A., Carter, L., Ferrante, J., Kreaseck, B., Olschanowsky, C.: An approach for code generation in the sparse polyhedral framework. Parallel Comput. 53(C), 32–57 (2016)

    Article  MathSciNet  Google Scholar 

  23. Venkat, A., et al.: Automating wavefront parallelization for sparse matrix computations. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2016, pp. 41:1–41:12 (2016)

    Google Scholar 

  24. Verdoolaege, S.: Integer Set Library: Manual (2018). http://isl.gforge.inria.fr

  25. Vuduc, R., Kamil, S., Hsu, J., Nishtala, R., Demmel, J.W., Yelick, K.A.: Automatic performance tuning and analysis of sparse triangular solve. ICS (2002)

    Google Scholar 

  26. Wang, E., et al.: Intel math kernel library. High-Performance Computing on the Intel® Xeon Phi™, pp. 167–188. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06486-4_7

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Arizona, Tucson, USA

    Mahdi Soltan Mohammadi & Michelle Mills Strout

  2. University of Toronto, Toronto, Canada

    Kazem Cheshmi & Maryam Mehri Dehnavi

  3. Intel, Santa Clara, United States

    Anand Venkat

  4. Univ Rennes, Inria, Rennes, France

    Tomofumi Yuki

Authors
  1. Mahdi Soltan Mohammadi
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  2. Kazem Cheshmi
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  3. Maryam Mehri Dehnavi
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  4. Anand Venkat
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  5. Tomofumi Yuki
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  6. Michelle Mills Strout
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Corresponding author

Correspondence to Mahdi Soltan Mohammadi .

Editor information

Editors and Affiliations

  1. University of Utah, Salt Lake City, UT, USA

    Mary Hall

  2. University of Utah, Salt Lake City, UT, USA

    Hari Sundar

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Mohammadi, M.S., Cheshmi, K., Dehnavi, M.M., Venkat, A., Yuki, T., Strout, M.M. (2019). Extending Index-Array Properties for Data Dependence Analysis. In: Hall, M., Sundar, H. (eds) Languages and Compilers for Parallel Computing. LCPC 2018. Lecture Notes in Computer Science(), vol 11882. Springer, Cham. https://doi.org/10.1007/978-3-030-34627-0_7

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  • DOI: https://doi.org/10.1007/978-3-030-34627-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34626-3

  • Online ISBN: 978-3-030-34627-0

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