Data structure and algorithms for new hardware technology

  • Yahiko Kambayashi
  • Hiroki Takakura
  • Shintaro Meki
Invited Talk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 730)


New applications and new hardware/software technology are major factors to drive database research to new directions. In this paper we will discuss effects of up-to-date hardware technology to data structure and database algorithms. Historically in database file organization, utilization of sequential access and clustering of data are two important techniques to improve processing efficiency. Flash memory, which is believed to replace conventional disks for mobile application etc., can sequentially access only small amount of data compared with disks, and data clustering will not contribute to improve system performance. On the other hand, recently developed high-speed RAM contains cache memory which contributes to speed-up sequential access. We may be able to utilize special-purpose memory to improve database performance, such as content addressable memory and dual-port RAM. Most research on improvement of database performance using hardware was to develop hardware for relational database operations. Advanced flexible logic chips such as FPGA(Field Programmable Gate Array) can realize a circuit consisting of over 10,000 gates and connections in the circuit can be changed during system operation by modifying the contents of control SRAMs. We may be able to improve system performance by using FPGAs to realize bottle-neck portions of the database software. Such techniques can be applied especially to active and real-time database systems. Pipe-line processing is a special case of parallel processing and recently pipe-line processors for workstations have been developed. Although pipe-line processing is rather restrictive, it can be combined with concurrency control mechanisms rather easily. Optimization for pipe-line processing is also simpler than that for parallel/distributed systems. Use of pipe-line processors for database operations is also an important topic.


Field Programmable Gate Array Flash Memory Concurrency Control Acceleration Ratio Database Operation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [AgJ89]
    R.Agrawal, H.V. Jagadish, “Recovery Algorithms for Database Machines with Nonvolatile Main Memory,” Proc. of 6th Int. Workshop Database Machines, (Lecture Notes in Computer Science), Springer-Verlag, pp.269–285. June 1989.Google Scholar
  2. [AhC75]
    A.V.Aho, M.J.Corasick, “Efficient String Matching: An Aid to Bibliographic Search,” C.ACM, Vol.18, No.6, pp.333–340, June 1975.Google Scholar
  3. [BaM72]
    R. Bayer, E.M. McCreight, “Organization and Maintenance of Large Ordered Indices,” Acta Information, Vol.7, No.3, pp.173–189, 1972.Google Scholar
  4. [BiD83]
    D.Bitton, D.J.Dewitt, C.Turbyfill, “ Benchmarking database systems: A systematic approach,” Proceedings of the 1983 Very Large Database Conference, pp.8–19, 1983.Google Scholar
  5. [BiD84]
    D.Bitton, D.J.DeWitt, D.K.Hsiao, J.Menon, “A Taxonomy of Parallel Sorting,” ACM Computing Surveys, Vol.16, No.3, pp287–318, 1984.Google Scholar
  6. [Bry92]
    R.E.Bryant, “Symbolic Boolean Manipulation with Ordered Binary-Decision Diagrams,” ACM Computing Survey, Vol.24 No.3 September 1992, pp.293–318.Google Scholar
  7. [Day88]
    U.Dayal, “Active Database Management Systems,” Proc. 3rd Int. Conf. Data and Knowledge Base, pp.150–169, 1988.Google Scholar
  8. [Dew91]
    D.J.Dewitt, “The Wisconsin Benchmark:Past, Present, and Future,” The Benchmark Handbook, Morgan Kaufman, pp.119–165, 1991.Google Scholar
  9. [Eic89]
    M.H.Eich, “Main Memory Database Research Directions,” Proc. 6th International Workshop, IWDM'89, 1989, pp.251–268.Google Scholar
  10. [Gho72]
    S.P.Ghosh, “File Organization: The Consecutive Retrieval Property,” CACM, Vol.15, No.8, pp.802–808, 1972.Google Scholar
  11. [Hus93]
    K.A.Hua, X.X.W.Su, C.M.Hua, “Efficient Evaluation of Traversal Recursive Queries,” Proc. Int. Conf. Data Engineering, pp.549–558, April 1993.Google Scholar
  12. [ImB92]
    T.Imielinski, B.R.Badrinath, “Querying in Highly Mobile Distributed Environments,” Proc. 18th VLDB Conf., pp.41–52.Google Scholar
  13. [IwK89]
    K.Iwama, Y.Kambayashi, “An O(log n) Parallel Connectivity Algorithm on the Mesh of Buses,” Proceedings of the IFIP Congress, pp.305–310, August 1989.Google Scholar
  14. [IwK93]
    K.Iwama, Y.Kambayashi, “A Simple Parallel Algorithms for Graph Connectivity,” Journal of Algorithms (to appear).Google Scholar
  15. [KaK84]
    Y.Kambayashi, S.Kondo, “Global Concurrency Control Mechanisms for a Local Network Consisting of Systems without Concurrency Control Mechanism,” Proceedings of the AFIPS National Computer Conference, Vol.53, pp.31–39, July 1984.Google Scholar
  16. [Kam79]
    Y.Kambayashi, “Logic Design of Programmable Logic Array,” IEEE Transactions on Computers, Vol. C 28, No. 9, pp. 609–617, Sept. 1979.Google Scholar
  17. [Kam84]
    Y.Kambayashi, “A Database Machine Based on the Data Distribution Approach,” Proceedings of the AFIPS National Computer Conference, Vol.53, pp.613–625, July 1984.Google Scholar
  18. [KaG85]
    Y.Kambayashi, S.P.Ghosh, “Query Processing Using the Consecutive Retrieval Property,” in Query Processing in Database Systems, pp. 217–233, Springer-Verlag, 1985.Google Scholar
  19. [Kam88]
    Y.Kambayashi, “Integration of Different Concurrency Control Mechanisms in Heterogeneous Distributed Databases,” Proceedings of the Second International Symposium on Interoperable Information Systems (ISIIS ' 88), OHM Publishing Co., November 1988.Google Scholar
  20. [KaT91]
    Y.Kambayashi, H.Takakura, “Realization of Continuously Backed-up RAMs for High-Speed Database Recovery,” The 2nd International Symposium on DASFAA, pp.236–242, 1991.Google Scholar
  21. [Kun80]
    H.T.Kung, P.L.Lehman, “Systoric (VLSI) Arrays for Relational Database Operations,” Proc. of ACM SIGMOD, pp.105–116, 1980.Google Scholar
  22. [LeC86]
    T.J. Lehman, M.J. Carey, “A Study of Index Structures for Main Memory Database Management Systems,” Proc. of the 12th Int. Conf. on VLDB, 1986, pp.294–303.Google Scholar
  23. [LeC81]
    D.T.Lee, H.Chang, C.K.Wong, “An On-Chip Compared/Steer Bubble Sorter,” IEEE Trans. on Computers, C-30, pp.398–405, 1981.Google Scholar
  24. [Mai82]
    D.Maier, “Using Write-One Memory for Database Storage,” Proc. ACM PODS, pp.239–246, 1982.Google Scholar
  25. [Mul75]
    D.E.Muller and F.P.Preparata, “Bounds to Complexites of Networks for Sortting and for Switching,” JACM vol.22, no.2, April 1975.Google Scholar
  26. [MuK89]
    S.Muroga, Y.Kambayashi, H.C.Lai, J.Culliney, “The Transaction Method — Design of Logic Network Based on Permissible Functions,” IEEE Transactions on Computers. September 1989.Google Scholar
  27. [OcI91]
    H.Ochi, N.Ishiura, S.Yajima, “Breadth-First Manipulation of SBDD of Boolean Functions for Vector Processing,” Proceedings of 28th ACM/IEEE Design Automation Conference, pp.413–41, 1991.Google Scholar
  28. [Sik80]
    A. Silberschatz, Z.Kedem, “Consistency in Hierarchical Database Systems,” Journal of ACM, Vol.27, No.1, pp.72–80, 1980.Google Scholar
  29. [Sle56]
    A.E.Slacle, H.O.MeMahon, “A Cryotron Catalog Memory System,” Proc. of EJCC, pp.115–120, 1956.Google Scholar
  30. [Sto93]
    M.Stonbraker, “Are We Polishing a Round Ball?,” Panel at the International Conf. on Data Engineering, April 1993.Google Scholar
  31. [TaN80]
    Y.Tanaka, Y.Nozaka, A.Masuyama, “Pipeline Searching and Sorting Modules as Components of a Data Flow Database Computer,” Proc. of IFIP 80, October 1980.Google Scholar
  32. [TaT90]
    T.Takagi, Y.Takenaga, S.Yajima, “Memory Parallel Computation Method and its Computation Power-The Third Approach to Realize Super Computers,” Journal of IPSJ, Vol.31, No.11, pp.1565–1571, 1990 (in Japanese).Google Scholar
  33. [Tho77]
    C.D.Thompson and H.T.Kung, “Sorting on a Mesh-Connected Parallel Computer,” CACM vol.20,no.4, April 1977.Google Scholar
  34. [Tho83]
    C.T.Thompson, “The VLSI Complexity of Sorting,” IEEE Trans. on Computers, Vol.32, No.12, pp.1171–1184, Dec. 1983.Google Scholar
  35. [Tod78]
    S.Todd, “Algorithm and Hardware for a Merge Sort Using Multiple Processors,” IBM Journal of Research and Development, Vol. 22, No. 5, September 1978.Google Scholar
  36. [Tos92]
    Toshiba, NAND E2PROM, FT-TC584000P /F/Ft/FR, Data Sheet, June 1992.Google Scholar
  37. [Uke93]
    R.L.Ukeiley, “Field Programmable Gate Ararys,” PTR prentice-Hall, 1993.Google Scholar
  38. [Ull80]
    J.D.Ullman, “Principles of database systems,” Computer science Press, 1980.Google Scholar
  39. [Yam87]
    H.Yamada,, “A High-Speed String Search Engine,” IEEE Journal of Solid-State Circuits, Vol.22, pp.829–834, Oct. 1987.Google Scholar
  40. [Yas82]
    H.Yasuura, N.Takagi and S.Yajima, “The Parallel Enumeration Sortin Scheme for VLSI,” IEEE Trans, Comput., Vol.C-31, No.12, pp.1192–1201, December 1982.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Yahiko Kambayashi
    • 1
  • Hiroki Takakura
    • 1
  • Shintaro Meki
    • 2
  1. 1.Faculty of EngineeringKyoto UniversityKyotoJapan
  2. 2.Faculty of Computer ScienceOkayama Prefecture UniversityOkayamaJapan

Personalised recommendations