Improving the vector performance via algorithmic domain decomposition

  • Helmut Weberpals
Efficient Use Of Vector Processors
Part of the Lecture Notes in Computer Science book series (LNCS, volume 457)


To use the full potential of a local memory vector computer, algorithms have to comply with the memory hierarchy. Using the IBM 3090 as a paradigm we give a fairly complete account of its cache storage which turns out to play a crucial rôle in vector processing. On the basis of these results we are able to improve the vector performance of algorithms by decomposing the data domain.


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  1. A. Agarwal, J. Hennessy and M. Horowitz: Cache performance of operating system and multiprogramming workloads. ACM Transact. Computer Systems 6 (1988) 393–431.CrossRefGoogle Scholar
  2. M. Bessenrodt-Weberpals and H. Weberpals: A fast vector algorithm for solving tridiagonal linear equations. Parallel Computing 9 (1988/89) 367–372.CrossRefGoogle Scholar
  3. W. Buchholz: The IBM System/370 vector architecture. IBM Systems J. 25 (1986) 51–62.Google Scholar
  4. O. Buneman: A compact non-iterative Poisson solver. Report 294, Stanford Univ. Inst. Plasma Research (1969).Google Scholar
  5. R. S. Clark and T. L. Wilson: Vector system performance of the IBM 3090. IBM Systems J. 25 (1986) 63–82.Google Scholar
  6. M. D. Hill and A. J. Smith: Evaluating associativity in CPU caches. IEEE Transact. Computers 38 (1989) 1612–1630.CrossRefGoogle Scholar
  7. K. Hwang and F. A. Briggs: Computer architecture and parallel processing. McGraw-Hill, New York (1984).Google Scholar
  8. B. Liu and N. Strother: Programming in VS FORTRAN on the IBM 3090 for maximum vector performance. IEEE Computer 21 (1988) 65–76.Google Scholar
  9. A. Padegs, B. B. Moore, R. M. Smith, and W. Buchholz: The IBM System/370 vector architecture: Design considerations. IEEE Transact. Computers 37 (1988) 509–520.CrossRefGoogle Scholar
  10. R. Reuter: Solving tridiagonal systems of linear equations on the IBM 3090 VF. Parallel Computing 8 (1988) 371–376.CrossRefGoogle Scholar
  11. K. So and R. N. Rechtschaffen: Cache operations by MRU change. IEEE Transact. Computers 37 (1988) 700–709.CrossRefGoogle Scholar
  12. H. S. Stone: High-performance computer architecture. Addison-Wesley, Reading (1987).Google Scholar
  13. K. Stüben and U. Trottenberg: Multigrid methods: Fundamental algorithms, model problem analysis and applications. In: W. Hackbusch and U. Trottenberg (eds.): Multigrid methods. Springer, Berlin (1982) pp. 1–176.Google Scholar
  14. S. G. Tucker: The IBM 3090 system: An overview. IBM Systems J. 25 (1986) 4–19.Google Scholar
  15. H. Weberpals: Architectural approach to the IBM 3090E vector performance. Parallel Computing 13 (1990) 47–59.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Helmut Weberpals
    • 1
  1. 1.Gesellschaft für Wissenschaftliche Datenverarbeitung Göttingen and Institut für Numerische und Angewandte Mathematikder Universität GöttingenGöttingenGermany

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