© 1999

Algorithm Engineering and Experimentation

International Workshop ALENEX’99 Baltimore, MD, USA, January 15–16, 1999 Selected Papers

  • Michael T. Goodrich
  • Catherine C. McGeoch
Book ALENEX 1999

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1619)

Table of contents

  1. Front Matter
    Pages I-VII
  2. Combinatorial Algorithms

    1. Ruy Luiz Milidiú, Artur Alves Pessoa, Eduardo Sany Laber
      Pages 1-17
    2. Matthias Müller-Hannemann, Alexander Schwartz
      Pages 18-36
    3. Janet M. Six, Ioannis G. Tollis
      Pages 57-73
    4. Matthias Stallmann, Franc Brglez, Debabrata Ghosh
      Pages 74-93
    5. David M. Mount, Fan-Tao Pu
      Pages 94-113
    6. Robert J. Walker, Jack Snoeyink
      Pages 114-128
  3. Software and Applications

    1. Michael T. Goodrich, Mark Handy, Benoît Hudson, Roberto Tamassia
      Pages 129-144
    2. Maurizio Pizzonia, Giuseppe Di Battista
      Pages 145-160
    3. Andrew E. Caldwell, Andrew B. Kahng, Igor L. Markov
      Pages 182-198
    4. S. Cwilich, M. Deng, D. F. Lynch, S. J. Phillipsy, J. R. Westbrooky
      Pages 199-214
    5. Gregory L. Heileman, Carlos E. Pizano, Chaouki T. Abdallah
      Pages 230-249
  4. Algorithms for NP-Hard Problems

    1. Janos Csirik, David S. Johnson, Claire Kenyon, Peter W. Shor, Richard R. Weber
      Pages 250-269
    2. Matthew S. Levine
      Pages 270-285
  5. Data Structures

    1. Anurag Acharya, Huican Zhu, Kai Shen
      Pages 300-315
    2. Peter Sanders
      Pages 316-321

About this book


Symmetric multiprocessors (SMPs) dominate the high-end server market and are currently the primary candidate for constructing large scale multiprocessor systems. Yet, the design of e cient parallel algorithms for this platform c- rently poses several challenges. The reason for this is that the rapid progress in microprocessor speed has left main memory access as the primary limitation to SMP performance. Since memory is the bottleneck, simply increasing the n- ber of processors will not necessarily yield better performance. Indeed, memory bus limitations typically limit the size of SMPs to 16 processors. This has at least twoimplicationsfor the algorithmdesigner. First, since there are relatively few processors availableon an SMP, any parallel algorithm must be competitive with its sequential counterpart with as little as one processor in order to be r- evant. Second, for the parallel algorithm to scale with the number of processors, it must be designed with careful attention to minimizing the number and type of main memory accesses. In this paper, we present a computational model for designing e cient al- rithms for symmetric multiprocessors. We then use this model to create e cient solutions to two widely di erent types of problems - linked list pre x com- tations and generalized sorting. Both problems are memory intensive, but in die rent ways. Whereas generalized sorting algorithms typically require a large numberofmemoryaccesses, they areusuallytocontiguousmemorylocations. By contrast, prex computation algorithms typically require a more modest qu- tity of memory accesses, but they are are usually to non-contiguous memory locations.


Approximation Algorithms Combinatorial Algorithms Computational Complexity Data Structures Geometric Algorithms algorithms computational geometry algorithm analysis and problem complexity

Editors and affiliations

  • Michael T. Goodrich
    • 1
  • Catherine C. McGeoch
    • 2
  1. 1.Department of Computer ScienceJohn Hopkins UniversityBaltimoreUSA
  2. 2.Department of Mathematics and Computer ScienceAmherst UniversityAmherstUSA

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