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Parallel depth first search. Part I. Implementation

Abstract

This paper presents a parallel formulation of depth-first search which retains the storage efficiency of sequential depth-first search and can be mapped on to anyMIMD architecture. To study its effectiveness it has been implemented to solve the 15-puzzle problem on three commercially available multiprocessors—Sequent Balance 21000, the Intel Hypercube and BBN Butterfly. We have been able to achieve fairly linear speedup on Sequent up to 30 processors (the maximum configuration available) and on the Intel Hypercube andBBN Butterfly up to 128 processors (the maximum configurations available). Many researchers considered the ring architecture to be quite suitable for parallel depth-first search. Our experimental results show that hypercube and sharedmemory architectures are significantly better.

At the heart of our parallel formulation is a dynamic work distribution scheme that divides the work between different processors. The effectiveness of the parallel formulation is strongly influenced by the work distribution scheme and architectural features such as presence/absence of shared memory, the diameter of the network, relative speed of the communication network, etc. In a companion paper,(1) we analyze the effectiveness of different load-balancing schemes and architectures, and also present new improved work distribution schemes.

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This work was supported by Army Research Office Grant No. DAAG29-84-K-0060 to the Artificial Intelligence Laboratory, and Office of Naval Research Grant N00014-86-K-0763 to the Computer Science Department at the University of Texas ay Austin

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Rao, V.N., Kumar, V. Parallel depth first search. Part I. Implementation. Int J Parallel Prog 16, 479–499 (1987). https://doi.org/10.1007/BF01389000

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  • DOI: https://doi.org/10.1007/BF01389000

Key Words

  • Parallel formulation
  • depth-first search
  • work distribution schemes
  • state-space trees