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Parallel physical optimization algorithms for allocating data to multicomputer nodes


Three parallel physical optimization algorithms for allocating irregular data to multicomputer nodes are presented. They are based on simulated annealing, neural networks and genetic algorithms. All three algorithms deviate from the sequential versions in order to achieve acceptable speedups. The parallel simulated annealing (PSA) and neural network (PNN) algorithms include communication schemes that are adapted to the properties of the allocation problem and of the algorithms themselves for maintaining both good solutions and reasonable execution times. The parallel genetic algorithm (PGA) is based on a natural model of evolution. The performances of these algorithms are evaluated and compared. The three parallel algorithms maintain the good solution qualities of their sequential counterparts. Their comparison shows their suitability for different applications. For example, PGA yields the best solutions, but it is the slowest of the three. PNN is the fastest, but it yields lower quality solutions. PSA's performance lies in the middle.

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Mansour, N., Fox, G.C. Parallel physical optimization algorithms for allocating data to multicomputer nodes. J Supercomput 8, 53–80 (1994).

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  • Automatic parallelization
  • data allocation
  • data partitioning
  • genetic algorithms
  • load balancing
  • mapping
  • neural networks
  • physical optimization
  • simulated annealing