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Parallel Inference System Research in the Japanese FGCS Project

  • Takashi Chikayama
  • Kazuaki Rokusawa

Abstract

The FGCS project is a national project of Japan, that aims at establishing the basic technology required for high performance knowledge information processing systems. The research and development principle throughout the project has been to adopt logic as the theoretical backbone of knowledge information processing and parallel processing as the key technology for obtaining high performance.

Keywords

Foster Parent External Reference Garbage Collection Parallel Inference Generation Computer System 
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. [1]
    K. Ueda and T. Chikayama, “Design of the Kernel Language for the Parallel Inference Machine,” The Computer Journal, 33(6): 494–500, Oxford University Press, 1990.MathSciNetCrossRefGoogle Scholar
  2. [2]
    K. Ueda, “Guarded Horn Clauses: A Parallel Logic Programming Language with the Concept of a Guard,” Technical Report 208, ICOT, 1986.Google Scholar
  3. [3]
    T. Chikayama, “Operating System PIMOS and Kernel Language KL1,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 73–88, Ohmsha, 1992.Google Scholar
  4. [4]
    K. Taki, “Parallel Inference Machine PIM,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 50–72, Ohmsha, 1992.Google Scholar
  5. [5]
    E. Shapiro, “Systems Programming in Concurrent Prolog,” M. van Canegham and D. H. D. Warren (eds.), Logic Programming and its Applications, pp. 50–74, Albex Publishing Co., 1986.Google Scholar
  6. [6]
    K. Clark, et al, “PARLOG: Parallel Programming in Logic,” ACM Trans Programming Language Systems, 8(1), 1986.Google Scholar
  7. [7]
    V. A. Saraswat, et al., “Janus: A Step Towards Distributed Constraint Programming,” Proc. North American Conference on Logic Programming 1989, pp. 497–512, MIT Press, 1990.Google Scholar
  8. [8]
    E. Shapiro and A. Takeuchi, “Object-oriented Programming in Concurrent Prolog,” New Generation Computing, 1(1): 25–49, Ohmsha, 1983.CrossRefGoogle Scholar
  9. [9]
    M. Furuichi, et al., “A Multi-Level Load Balancing Scheme for OR-Parallel Exhaustive Search Programs on the Multi-PSI,” Proc. Second ACM SIG-PLAN Symposium on Principles and Practice of Parallel Programming, pp. 50–59, 1990.Google Scholar
  10. [10]
    K. Nakajima, et al., “Distributed Implementation of KL1 on the Multi-PSI/V2,” Proc. International Conference on Logic Programming 1989, pp. 436–451, 1989.Google Scholar
  11. [11]
    K. Hirata, et al., “Parallel and Distributed Implementation of Concurrent Logic Programming Language KL1,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 436–459, Ohmsha, 1992.Google Scholar
  12. [12]
    N. Ichiyoshi, et al., “A New External Reference Management and Distributed Unification for KL1,” New Generation Computing, 7(2, 3): 159–177, Ohmsha, 1990.CrossRefGoogle Scholar
  13. [13]
    P. Watson and I. Watson, “An Efficient Garbage Collection Scheme for Parallel Computer Architectures,” Proc. Parallel Architectures and Languages Europe 1991, LNCS, 259 (II): 432–443, Springer-Verlag, 1987.CrossRefGoogle Scholar
  14. [14]
    D. I. Bevan, “Distributed Garbage Collection Using Reference Counting,” Parallel Computing, 9(2): 179–192, North-Holland, 1989.MATHCrossRefGoogle Scholar
  15. [15]
    T. Chikayama and Y. Kimura, “Multiple Reference Management in Flat GHC,” Proc. International Conference on Logic Programming 1987, pp. 276–293, 1987.Google Scholar
  16. [16]
    Y. Inamura, et al., “Optimization Techniques Using the MRB and Their Evaluation on the Multi-PSI/V2,” Proc. North American Conference on Logic Programming 1989, pp. 907–921, MIT Press, 1989.Google Scholar
  17. [17]
    I. Foster, “Parallel Implementation of Parlog,” Proc. International Conference on Parallel Processing 1988, II: 9–16, 1988.Google Scholar
  18. [18]
    K. Rokusawa, et al., “An Efficient Termination Detection and Abortion Algorithm for Distributed Processing Systems,” Proc. International Conference on Parallel Processing 1988, I: 18–22, 1988.Google Scholar
  19. [19]
    H. Yashiro, et al., “Resource Management of PIMOS,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 269–277, Ohmsha, 1992.Google Scholar
  20. [20]
    M. Kawamura, et al., “Parallel Database Management System: Kappa-P,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 248–256, Ohmsha, 1992.Google Scholar
  21. [21]
    S. Aikawa, et al, “ParaGraph: A Graphical Tuning Tool for Multiprocessor Systems,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 286–293, Ohmsha, 1992.Google Scholar
  22. [22]
    K. Kimura and N. Ichiyoshi, “Probabilistic Analysis of the Optimal Efficiency of the Multi-Level Dynamic Load Balancing Scheme,” Proc. Sixth Distributed Memory Computing Conference, 1991.Google Scholar
  23. [23]
    S. Terasaki, et al., “Parallel Constraint Logic Programming Language GDCC and its Parallel Constraint Solvers,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 330–346, Ohmsha, 1992.Google Scholar
  24. [24]
    H. Yasukawa, et al, “Object, Properties, and Modules in QUIXOTE,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 257–268, Ohmsha, 1992.Google Scholar
  25. [25]
    M. Fujita, et al., “Model Generation Theorem Provers on a Parallel Inference Machine,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 357–375, Ohmsha, 1992.Google Scholar
  26. [26]
    M. Ishikawa, et al, “Protein Sequence Analysis by Parallel Inference Machine,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 294–299, Ohmsha, 1992.Google Scholar
  27. [27]
    M. Hirosawa, et al, “Folding Simulation using Temperature Parallel Simulated Annealing,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 300–306, Ohmsha, 1992.Google Scholar
  28. [28]
    M. Hirosawa, et al, “Protein Multiple Sequence Alignment using Knowledge,” Proc. 26th Annual Hawaii International Conference on System Science, 1: 803–812, 1993.Google Scholar
  29. [29]
    K. Onizuka, et al., “A Multi-Level Description Scheme of Protein Conformation,” Proc. First Intelligent Systems for Molecular Biology, pp. 301–310, 1993.Google Scholar
  30. [30]
    Y. Matsumoto and K. Taki, “Adaptive Time-Ceiling for Efficient Parallel Discrete Event Simulation,” Western Multiconference on Computer Simulation, pp. 101–106, 1993.Google Scholar
  31. [31]
    H. Date, et al, “LSI-CAD Programs on Parallel Inference Machine,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 237–247, Ohmsha, 1992.Google Scholar
  32. [32]
    T. Watanabe, et al., “Co-HLEX: Co-operative Recursive LSI Layout Problem Solver on Japan’s Fifth Generation Parallel Inference Machine,” Proc. International Conference on Fifth Generation Computer Systems 1992,pp. 1173–1180, Ohmsha, 1992.Google Scholar
  33. [33]
    H. Date and K. Taki, “A Parallel Lookahead line Search Router with Automatic Ripup-and-reroute,” Proc. EDAC-EUROASIC 93, 1993.Google Scholar
  34. [34]
    Y. Minoda, et al., “A Cooperative Logic Design Expert System on a Multiprocessor,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 1181–1189, Ohmsha, 1992.Google Scholar
  35. [35]
    K. Nitta, et al, “HELIC-II: A Legal Reasoning System on the Parallel Inference Machine,” Proc. International Conference on Fifth Generation Computer Systems 1992, pp. 1115–1124, Ohmsha, 1992.Google Scholar
  36. [36]
    K. Ueda and M. Morita, “A New Implementation Technique for flat GHC,” Proc. International Conference on Logic Programming 1990, pp. 3–17, MIT Press, 1990.Google Scholar
  37. [37]
    T. Chikayama, et al., “A Portable and Efficient Implementation of KL1,” Proc. Programming Language Implementation and Logic Programming 1994, LNCS 844: 25–39, Springer-Verlag, 1994.CrossRefGoogle Scholar
  38. [38]
    F. Mattern, “Global Quiescence Detection Based on Credit Distribution and Recovery,” Information Processing Letters, 30(4): 195–200, 1989.MathSciNetCrossRefGoogle Scholar
  39. [39]
    G. Tel and F. Mattern, “The Derivation of Distributed Termination Detection Algorithms from Garbage Collection Schemes,” Proc. Parallel Architectures and Languages Europe 1991, LNCS, 505 (I): 137–149, Springer-Verlag, 1991.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1995

Authors and Affiliations

  • Takashi Chikayama
    • 1
  • Kazuaki Rokusawa
    • 1
  1. 1.Institute for New Generation Computer TechnologyMinato-ku, Tokyo, 108Japan

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