College of Computer Sciences and EngineeringKing Fahd University of Petroleum & Minerals

Ali M. Zaidi

College of Computer Sciences and EngineeringKing Fahd University of Petroleum & Minerals

Mustafa I. Ali

College of Computer Sciences and EngineeringKing Fahd University of Petroleum & Minerals

Khawar S. Khan

College of Computer Sciences and EngineeringKing Fahd University of Petroleum & Minerals

Sanaullah Syed

College of Computer Sciences and EngineeringKing Fahd University of Petroleum & Minerals

Research Article – Computer Engineering and Computer Science

First Online:

Received:

Accepted:

DOI:
10.1007/s13369-010-0024-6

Cite this article as:

Sait, S.M., Zaidi, A.M., Ali, M.I. et al. Arab J Sci Eng (2011) 36: 259. doi:10.1007/s13369-010-0024-6

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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.

Keywords

Asynchronous MMCParallel SA schemesMultiobjective cell placementCluster-of-workstations