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Massively Parallel Systolic Algorithms for Real-Time Dictionary-Based Text Compression

  • James A. Storer
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 176)

Abstract

Textual substitution is a powerful and practical method of lossless data compression, where repeated substrings are replaced by pointers into a dynamically changing dictionary of strings. They are often called dictionary methods or “LZ” methods after the important work of Lempel and Ziv. With many applications, high speed hardware that can perform compression or decompression in real time is essential. We present massively parallel approaches for real-time textual substitution.

Keywords

Dictionary Entry Systolic Array Input Stream Input Alphabet Broadcast Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. S. De Agostino and J. A. Storer [1992]. “Parallel Algorithms for Optimal Compression using Dictionaries with the Prefix Property”, Proceedings IEEE Data Compression Conference, Snowbird, Utah.Google Scholar
  2. E. R. Fiala and D. H. Greene [1989]. “Data Compression with Finite Windows”, Communications of the ACM 32:4, 490–505.CrossRefGoogle Scholar
  3. M. Gonzalez and J. A. Storer [1985]. “Parallel Algorithms for Data Compression”, Journal of the ACM 32:2, 344–373.zbMATHCrossRefGoogle Scholar
  4. A. Hartman and M. Rodeh [1985], Optimal Parsing of Strings, Combinatorial Algorithms on Words, Springer-Verlag (A. Apostolico and Z. Galil, editors), 155–167.Google Scholar
  5. A. Lempel and J. Ziv [1976]. “On the Complexity of Finite Sequences”, IEEE Transactions on Information Theory 22:1, 75–81.MathSciNetzbMATHCrossRefGoogle Scholar
  6. E. M. McCreight [1976]. “A Space-Economical Suffix Tree Construction Algorithm”, Journal of the ACM 23:2, 262–272.MathSciNetzbMATHCrossRefGoogle Scholar
  7. V. S. Miller and M. N. Wegman [1985]. “Variations on a Theme by Lempel and Ziv”, Combinatorial Algorithms on Words, Springer-Verlag (A. Apostolico and Z. Galil, editors), 131–140.Google Scholar
  8. J. H. Reif and J. A. Storer [1991]. “A Parallel Architecture for High Speed Data Compression”, Journal of Parallel and Distributed Computing 13, 222–227.CrossRefGoogle Scholar
  9. J. H. Reif and J. A. Storer [1991b]. “Adaptive Lossless Data Compression over a Noisy Channel”, Proceedings Communication, Security, and Sequences Conference, Positano, Italy.Google Scholar
  10. M. Rodeh, V. R. Pratt, and S. Even [1980]. “Linear Algorithms for Compression Via String Matching”, Journal of the ACM 28:1, 16–24.MathSciNetCrossRefGoogle Scholar
  11. J. B. Seery and J. Ziv [1977]. “A Universal Data Compression Algorithm: Description and Preliminary Results”, Technical Memorandum 77-1212-6, Bell Laboratories, Murray Hill, N.J.Google Scholar
  12. J. B. Seery and J. Ziv [1978]. “Further Results on Universal Data Compression”, Technical Memorandum 78-1212-8, Bell Laboratories, Murray Hill, N.J.Google Scholar
  13. J. A. Storer [1988]. Data Compression: Methods and Theory, Computer Science Press, Rockville, MI).Google Scholar
  14. J. Storer [1991]. “Massively Parallel System for High Speed Data Compression”, patent pending.Google Scholar
  15. J. A. Storer, J. H. Reif, and T. Markas [1990]. “A Massively Parallel VLSI Design for Data Compression using a Compact Dynamic Dictionary”, Proceedings IEEE VLSI Signal Processing Conference, San Diego, CA.Google Scholar
  16. J. A. Storer and T. G. Szymanski [1978]. “The Macro Model for Data Compression”, Proceedings Tenth Annual ACM Symposium on Theory of Computing, San Diego, CA, 928–951.Google Scholar
  17. J. A. Storer and T. G. Szymanski [1982]. “Data Compression Via Textual Substitution”, Journal of the A CM 29:4, 928–951.MathSciNetzbMATHGoogle Scholar
  18. R.A. Wagner [1973], Common Phrases and Minimum Text Storage, Communications of the ACM 16, 148–152.CrossRefGoogle Scholar
  19. T. A. Welch [1984]. “A Technique for High-Performance Data Compression”, IEEE Computer 17:6, 8–19.CrossRefGoogle Scholar
  20. R. Zito-Wolf [1990]. “Broadcast / Reduce Architecture for High Speed Data Compression”, Proceedings Second IEEE Symposium on Parallel and Distributed Processing”, Dallas, TX, 1990, 174–181.Google Scholar
  21. R. Zito-Wolf [1990b]. “A Systolic Architecture for Sliding Window Data Compression”, Proceedings IEEE VLSI Signal Processing Conference, San Diego, CA, 1990, 339–351.Google Scholar
  22. J. Ziv and A. Lempel [1977]. “A Universal Algorithm for Sequential Data Compression”, IEEE Transactions on Information Theory 23:3, 337–343.MathSciNetzbMATHCrossRefGoogle Scholar
  23. J. Ziv and A. Lempel [1978]. “Compression of Individual Sequences Via Variable-Rate Coding”, IEEE Transactions on Information Theory 24:5, 530–536.MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • James A. Storer
    • 1
  1. 1.Computer Science DepartmentBrandeis UniversityWalthamUSA

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