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Average-Case Communication-Optimal Parallel Parenthesis Matching

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Algorithms and Computation (ISAAC 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2518))

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Abstract

We provide the first non-trivial lower bound, p-3/p · n/p, where p is the number of the processors and n is the data size, on the average-case communication volume, σ, required to solve the parenthesis matching problem and present a parallel algorithm that takes linear (optimal) computation time and optimal expected message volume, σ +p.

The kernel of the algorithm is to solve the all nearest smaller values problem. Provided n/p = Ω(p), we present an algorithm that achieves optimal sequential computation time and uses only a constant number of communication phases, with the message volume in each phase bounded above by (n/p +p) in the worst case and p in the average case, assuming the input instances are uniformly distributed.

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References

  1. M. Adler and J. W. Byers and R. M. Karp: Parallel Sorting With Limited Bandwidth. Proc. ACM Symposium on Parallel Algorithms and Architectures (1995) 129–136

    Google Scholar 

  2. Selim G. Akl: Parallel Computation, Models and Methods. Prentice Hall, 1997

    Google Scholar 

  3. Omer Berkman and Baruch Schieber and Uzi Vishkin: Optimal Doubly Logarithmic Parallel Algorithms Based on Finding All Nearest Smaller Values. Journal of Algorithms, Vol 14, 1993, 344–370

    Article  MATH  MathSciNet  Google Scholar 

  4. A. Borodin and J.E. Hopcroft: Routing, Merging and Sorting on Parallel Models of Computation. J. Computer and System Science, Vol 30, 1985, 130–145

    Article  MATH  MathSciNet  Google Scholar 

  5. D. Breslauer and Z. Galil: An Optimal O(loglogn) Time Parallel String Matching Algorithm. SIAM J. Computing, Vol 19, 1990, 1050–1058

    Article  MathSciNet  Google Scholar 

  6. T. H. Cormen and C. E. Leiserson and R. L. Rivest: Introduction to Algorithms. McGraw-Hill, 2000

    Google Scholar 

  7. F. Dehne and A. Fabri and A. Rau-Chaplin: Scalable Parallel Geometric Algorithms for Coarse Grained Multicomputers. Proc. 9th ACM Annual Computational Geometry, 1993, 298–307

    Google Scholar 

  8. Joseph JáJá: An Introduction to Parallel Algorithms. Addison-Wesley, 1992

    Google Scholar 

  9. J. Katajainen: Finding All Nearest Smaller Values on a Distributed Memory Machine. Proc. of Conference on Computing: The Australian Theory Symposium, 1996, 100–107

    Google Scholar 

  10. Z.M. Kedem and G.M. Landau and K.V. Palem: Optimal Parallel Prefix-Suffix Matching Algorithm and Applications. Proc. 1st ACM Symposium on Parallel Algorithms and Architectures, 1989, 388–398

    Google Scholar 

  11. D. Kravets and C. G. Plaxton: All Nearest Smaller Values on the Hypercube. IEEE Transactions on Parallel and Distributed Systems, Vol 7, No 5, 1996, 456–462

    Article  Google Scholar 

  12. C.P. Kruskal: Searching, Merging and Sorting in Parallel Computation. IEEE Transactions on Computers, Vol C-32, 1983, 942–946

    Article  MathSciNet  Google Scholar 

  13. W. F. McColl: Scalable Computing. Computer Science Today: Recent Trends and Developments, Edited by J. van Leeuwen, Lecture Notes in Computer Science, Vol 1000, Springer-Verlag, Berlin, 1995, 46–61

    Google Scholar 

  14. J.H. Reif: Synthesis of Parallel Algorithms. Morgan Kaufmann, 1993

    Google Scholar 

  15. B. Schieber and U. Vishkin: Finding All Nearest Neighbors for Convex Polygons in Parallel: A New Lower Bound Technique and A Matching Algorithm. Discrete App. Math., Vol 29, 1990, 97–111

    Article  MATH  MathSciNet  Google Scholar 

  16. Leslie G. Valiant: A Bridging Model for Parallel Computation. Communications of the ACM, Vol 33, No 8, 1990, 103–111

    Article  Google Scholar 

  17. Xin He and Chun-Hsi Huang: Communication Efficient BSP Algorithm for All Nearest Smaller Values Problem. Journal of Parallel and Distributed Computing, Vol 61, 2001, 1425–1438

    Article  MATH  Google Scholar 

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Huang, CH., He, X. (2002). Average-Case Communication-Optimal Parallel Parenthesis Matching. In: Bose, P., Morin, P. (eds) Algorithms and Computation. ISAAC 2002. Lecture Notes in Computer Science, vol 2518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36136-7_28

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  • DOI: https://doi.org/10.1007/3-540-36136-7_28

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00142-3

  • Online ISBN: 978-3-540-36136-7

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