Rectangular spatial decomposition methods for parallel simulated annealing

  • Authors:
  • Dan R. Greening;Frederica Darema

  • Affiliations:
  • UCLA Computer Science Department;IBM T.J. Watson Research Center

  • Venue:
  • ICS '89 Proceedings of the 3rd international conference on Supercomputing
  • Year:
  • 1989

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Abstract

Research on VLSI placement has extended the standard sequential simulated annealing technique to two multiprocessing variants. In one technique, processors perform moves on disjoint partitions of locally-stored circuit grids. In the other, processors perform simultaneous moves on a shared grid. Our research explores new techniques in the first category—called spatial decomposition algorithms.We describe the impact of cell mobility and cost-function errors in parallel simulated annealing. We show that changing the partition shape can affect these measures, and the quality of the final result. We also show a trade-off: execution speed vs. increased cell mobility and decreased cost-function errors.We present four rectangular decomposition methods. Using two circuit examples, we compare their convergence properties to that of a “standard” random spatial decomposition technique. Runs were performed in a simulated RP3 environment.One method we developed, “sharp random rectangles,” converged better than the other techniques we studied. On one example, sharp random rectangles on 8 processors converged better than standard sequential algorithms. This promising technique allows us to increase the stream-length, and thereby reduce execution time.The authors continue their research to better quantify cell mobility and cost-function errors, and to run the rectangular algorithms on other types of multiprocessors to obtain speed-up information.