Exploring Asynchronous MMC-Based Parallel SA Schemes for Multiobjective Cell Placement on a Cluster of Workstations

Abstract

Simulated Annealing (SA) is a popular iterative heuristic used to solve a wide variety of combinatorial optimization problems. However, depending on the size of the problem, it may have large run-time requirements. One practical approach to speed up its execution is to parallelize it. In this paper, several parallel SA schemes based on the Asynchronous Multiple-Markov Chain model are explored. We investigate the speedup and solution quality characteristics of each scheme when implemented on an inexpensive cluster of workstations for solving a multi-objective cell placement problem. This problem requires the optimization of conflicting objectives (interconnect wire-length, power dissipation, and timing performance), and Fuzzy logic is used to integrate the costs of these objectives. Our goal is to develop several AMMC based parallel SA schemes and explore their suitability for different objectives: achieving near linear speedups while still meeting solution quality targets, and obtaining higher quality solutions in the least possible duration.