LH*lh: A scalable high performance data structure for switched multicomputers

  • Jonas S. Karlsson
  • Witold Litwin
  • Tore Risch
Parallel Databases
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1057)


LH*lh is a new data structure for scalable high-performance hash files on the increasingly popular switched multicomputers, i.e., MIMD multiprocessor machines with distributed RAM memory and without shared memory. An LH*lh file scales up gracefully over available processors and the distributed memory, easily reaching Gbytes. Address calculus does not require any centralized component that could lead to a hot-spot. Access times to the file can be under a millisecond and the file can be used in parallel by several client processors. We show the LH*lh design, and report on the performance analysis. This includes experiments on the Parsytec GC/PowerPlus multicomputer with up to 128 Power PCs divided into 64 nodes with 32 MB of RAM per node. We prove the efficiency of the method and justify various algorithmic choices that were made. LHI*lh opens a new perspective for high-performance applications, especially for the database management of new types of data and in real-time environments.


Access Time Message Size File Parameter Disk File Correct Server 
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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  1. 1.
    Teradata Corporation. DBC/1012 data base computer concepts and facilities. Technical Report Teradata Document C02-001-05, Teradata Corporation, 1988.Google Scholar
  2. 2.
    D. Culler. NOW: Towards Everyday Supercomputing on a Network of Workstations. Technical report, EECS Tech. Rep. UC Berkeley, 1994.Google Scholar
  3. 3.
    R. Devine. Design and implementation of DDH: A distributed dynamic hashing algorithm. In Proc. of the 4th Intl. Conf. on Foundations of Data Organization and Algorithms (FODO), 1993.Google Scholar
  4. 4.
    D. DeWitt, R. Gerber, G. Graefe, M. Heytens, K. Kumar, and M. Muralikrishna. GAMMA: A high performance dataflow database machine. In Proc of VLDB, August 1986.Google Scholar
  5. 5.
    G. Fahl, T. Risch, and M. Sköld. AMOS — An Architecture for Active Mediators. In IEEE Transactions on Knowledge and Data Engineering, Haifa, Israel, June 1993.Google Scholar
  6. 6.
    J. S. Karlsson. LH*lh: Architecture and Implementation. Technical report, IDA, Linkping University, Sweden, 1995.Google Scholar
  7. 7.
    J. S. Karlsson, S. Larsson, T. Risch, M. Sköld, and M. Werner. AMOS User's Guide. CAELAB, IDA, IDA, Dept. of Computer Science and Information Science, Linköping University, Sweden, memo 94-01 edition, Mars 1994. URL: Scholar
  8. 8.
    M. Kitsuregawa, H. Tanaka, and T. Moto-Oka. Architecture and performance of relational algebra machine GRACE. In Proc. of the Intl. Conf. on Parallel Processing, Chicago, 1984.Google Scholar
  9. 9.
    B. Kroll and P. Widmayer. Distributing a Search Tree Among a Growing Number of Processors. In ACM-SIGMOD Int. Conf. On Management of Data, 1994.Google Scholar
  10. 10.
    P.A. Larson. Dynamic hashing. BIT, 18(2):184–201, 1978.Google Scholar
  11. 11.
    P.A. Larson. Dynamic hash tables. In Communications of the A CM, volume 31(4), pages 446–57. April 1988.Google Scholar
  12. 12.
    W. Litwin. Linear Hashing: A new tool for file and table addressing. Montreal, Canada, 1980. Proc. of VLDB.Google Scholar
  13. 13.
    W. Litwin. Linear Hashing: A new tool for file and table addressing. In Michael Stonebraker, editor, Readings in DATABASE SYSTEMS, 2nd edition, pages 96–107. 1994.Google Scholar
  14. 14.
    W. Litwin, M-A. Neimat, and D. Schneider. LH*: A Scalable Distributed Data Structure, submitted for journal publication, Nov 1993.Google Scholar
  15. 15.
    W. Litwin, M-A Neimat, and D. Schneider. LH*: Linear hashing for distributed files. ACM SIGMOD International Conference on Management of Data, May 1993.Google Scholar
  16. 16.
    W. Litwin, M-A Neimat, and D. Schneider. RP*: A Family of Order Preserving Scalable Distributed Data Structures. VLDB Conference, 1994.Google Scholar
  17. 17.
    M. Tamer Özsu and Patrick Valduriez. Principles of Distributed Database Systems. Number ISBN 0-13-715681-2. Prentice Hall, 1991.Google Scholar
  18. 18.
    Parsytec Computer GmbH. Programmers Guide, Parix 1.2-PowerPC., 1994.Google Scholar
  19. 19.
    M. Pettersson. Main-Memory Linear Hashing — Some Enhancements of Larson's Algorithm. Technical Report LiTH-IDA-R-93-04, ISSN-0281-4250, IDA, 1993.Google Scholar
  20. 20.
    C. Severance, S. Pramanik, and P. Wolberg. Distributed linear hashing and parallel projection in main memory databases. In Proceedings of the 16th International Conference on VLDB, Brisbane, Australia, 1990.Google Scholar
  21. 21.
    Andrew S. Tanenbaum. Distributed Operating Systems. 1995.Google Scholar
  22. 22.
    R. Wingralek, Y. Breitbart, and G. Weikum. Distributed file organisation with scalable cost/performance. In Proc of ACM-SIGMOD, May 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Jonas S. Karlsson
    • 1
  • Witold Litwin
    • 2
  • Tore Risch
    • 1
  1. 1.EDSLAB - Engineering Databases and Systems Laboratory, Department of Computer and Information ScienceLinköping UniversitySweden
  2. 2.Universite Paris 9 DauphineParis Cedex 16France

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