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

Abstract

Combinatorial optimization problems are generally NP hard problems that require large run-times when solved using iterative heuristics. Parallelization using distributed or shared memory computing clusters thus becomes a natural choice to speed up the execution times of such problems. In this paper, several parallel schemes based on an asynchronous multiple-Markov-chain (AMMC) model are explored to parallelize simulated annealing (SA), used for solving a multiobjective VLSI cell placement problem. The different parallel schemes are investigated based on the speedups and solution qualities achieved on an inexpensive cluster of workstations. The 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. The goal is to develop effective AMMC-based parallel SA schemes to achieve near linear speedups while maintaining or achieving higher solution qualities in less time and to analyze these parallel schemes against the common critical performance factors.