Distributed computing in the workstation environment

ABSTRACT

Exploiting unused processor time in workstation environments can result in large performance gains in user applications. A parallel each operator is shown which implements client-server based distributed processing. A time-consuming algorithm is sped up by using idle workstations in a local area network as APL servers.

REFERENCES

• 1: Andreas Geyer-Schulz , Thomas Kolarik, Distributed computing with APL, ACM SIGAPL APL Quote Quad, v.23 n.1, p.60-69, July 1992
• 2: A. Y. Gralna and V. Kumar. Parallel Proce.~sing of Discrete Optimization Problems: A Survey, 1992. (to be published).
• 3: J. R. Koza. Genetic Evohltion and Co-Evolution of Computer Programs. In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, editors, Artificial Life II. Addison-Wesley, 1991.
• 4: David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989
• 5: V. Braitenberg. Vehicles - Experimcnt.~ in Synthetic P.,ychoIogy. MIT Press, 1984.

INDEX TERMS

Primary Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.2 Language Classifications

          Nouns: APL

Additional Classification:
  D. Software
  D.1 PROGRAMMING TECHNIQUES
      D.1.3 Concurrent Programming
          Subjects: Distributed programming
  D.3 PROGRAMMING LANGUAGES
      D.3.3 Language Constructs and Features
          Subjects: Procedures, functions, and subroutines

  F. Theory of Computation
  F.2 ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY
      F.2.1 Numerical Algorithms and Problems
          Subjects: Number-theoretic computations (e.g., factoring, primality testing)

  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS
      G.2.1 Combinatorics
          Subjects: Combinatorial algorithms

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE

General Terms:
Algorithms, Languages, Performance