Communication heuristics in distributed combinatorial search algorithms

  • Alfred Taudes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 392)


Algorithms for combinatorial search problems such as the travelling salesman problem, the polynomial problem or game-tree searching have been prime candidates for a distributed implementation as these problems offer substantial parallelism on the programm level. However, a number of experimental studies show that naive approaches to distributed combinatorial search tend to yield only a moderate speedup. The problem is to find a communication strategy that is able to limit stand-stills and superfluous searching of individual processors by distributing the work-load and intermediate results among the processors effectively and that causes only moderate communication overhead. To tackle the problem of the diffusion of commonly useful intermediate results between processors linked by “slow” communication lines without shared memory, we propose a formal method to derive optimal and adaptive communication strategies based on Dynamic Programming in Markov Chains. The key idea of our approach is to compare the running time to be expected under the current contents of the local copy of the shared state with the search and communication effort when acquiring the intermediate result of another processor via an exchange of messages. Using this method we study communication strategies for various distributed combinatorial search problems: for the distributed determination of the maximum of a vector, for a distibuted version of a simple variant of alpha-beta pruning, and for distributed branch and bound methods, where we examine the set partitioning problem.


Search Space Shared Memory Intermediate Result Travel Salesman Problem Search Step 
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]
    Aho, A.V., J.E. Hopcroft und J.D. Ullman, Data Structures and Algorithms, Reading, Mass. u.a., Addison-Wesley, 1985Google Scholar
  2. [2]
    Cheriton D.R., The V Distributed System, Communications of the ACM, Vol. 31 (March), No. 3, 314–334, 1989Google Scholar
  3. [3]
    Christofides N., A. Mingozzi, P. Thot, C. Sandi (eds.), Combinatorial Optimization, New York a.o., John Wiley & Sons, 1979Google Scholar
  4. [4]
    Ferguson C. und R.E. Korf, Distributed Tree Search and its Applications to Alpha-Beta Pruning, Proceedings of the AAAI Conference 1987, 128–132Google Scholar
  5. [5]
    Finkel R., und U. Manber, DIB — A Distributed Implementation of Backtracking, ACM Transactions on Programming Languages and Systems, Vol. 9, No. 2 April 1987, 235–256Google Scholar
  6. [6]
    Giloi W.K., Rechnerarchitektur, Heidelberg, Springer, 1981Google Scholar
  7. [7]
    Howard R.A., Dynamic Probabilistic Systems, Vol. I: Markov Models, Wiley, New York, 1971Google Scholar
  8. [8]
    Howard R.A., Dynamic Probabilistic Systems, Vol II: Semi-Markov and Decision Processes, New York, 1971Google Scholar
  9. [9]
    Karp, R.M., und Y. Zhang, A Randomized Parallel Branch-and-Bound Procedure, Working Paper, University of California, Berkeley, 1988Google Scholar
  10. [10]
    Knuth, D.E. Fundamental Algorithms, Reading, Mass. u.a., Addison-Wesley, 1. The Art of Computer Programming, 1973Google Scholar
  11. [11]
    Knuth D.E. und R.W. Moore, An Analysis of Alpha-Beta Pruning, Artificial Intelligence, Vol. 6, 293–326, 1975CrossRefGoogle Scholar
  12. [12]
    Kumar V., K. Ramesh und V.N. Rao, Parallel Best-First Search of State-Space Graphs: A Summary of Results, Proceedings of the AAAI Conference 1987, 122–127Google Scholar
  13. [13]
    Leiserson C.E. und B.M. Maggs, Communication-Efficient Parallel Algorithms for Distributed Random-Access Machines, Algorithmica, Vol. 3 (1988), 53–77CrossRefGoogle Scholar
  14. [14]
    Martin J.J., Bayesian Decision Problems and Markov Chains, R.E. Krieger, New York, 1975Google Scholar
  15. [15]
    Parberry I., Parallel Complexity Theory, New York u.a., John Wiley & Sons, 1987Google Scholar
  16. [16]
    Smith D.R., Random Trees and the Analysis of Branch and Bound Procedures, Journal of the Association for Computing Machinery, Vol. 31, No. 1, January 1984, 163–188MathSciNetGoogle Scholar
  17. [17]
    Vornberger O. und B. Monien, Parallel Alpha-Beta versus Parallel SSS, in: Barton, Dagless, Reijns (eds): IFIP Conference on Distributed Processing, North-Holland 1987Google Scholar
  18. [18]
    Vornberger O., Implementing Branch-and-Bound in a Ring of Processors, in: Goos, Hartmanis (eds): Lecture Notes in Computer Science, CONPAR 86 — Conference on Algorithms and Hardware for Parallel Processing, 158–164, Springer, 1986Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1989

Authors and Affiliations

  • Alfred Taudes
    • 1
  1. 1.Department of Applied Computer Science Institute of Information Processing and Information EconomicsVienna University of Economics and Business AdministrationViennaAustria

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