Statistical analysis of parallel randomized algorithms for VLSI placement and implementation on workstation networks

A massively parallel optimization approach based on simple neighbourhood search techniques is developed and applied to the problem of VLSI cell placement. Statistical models are developed to analyse the performance of the approach in general, and to derive statistical bounds on the quality of obtain...

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Veröffentlicht in:Microprocessors and microsystems Jg. 19; H. 6; S. 341 - 349
1. Verfasser: Efe, Kemal
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.08.1995
Schlagworte:
density estimation
randomized algorithms
simulated annealing
density estimation
randomized algorithms
simulated annealing
ISSN:0141-9331, 1872-9436
Online-Zugang:Volltext
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Zusammenfassung:A massively parallel optimization approach based on simple neighbourhood search techniques is developed and applied to the problem of VLSI cell placement. Statistical models are developed to analyse the performance of the approach in general, and to derive statistical bounds on the quality of obtainable results. Specific questions addressed are: (1) Given a solution with a known cost, how can we measure its quality? (2) Given a target cost for the solution, how likely is the algorithm to generate a solution with that cost for better? (3) Are there any performance bounds for the solutions obtainable by neighbourhood search methods? (4) How can we measure for quantify the performance of different neighbourhood search methods? The results of these analyses suggest a simple framework for approximate solution of difficult problems. The approach is inherently parallel, and it can be implemented on any type of parallel computer. We implemented it on the PVM environment running on a network of workstations connected by Ethernet. The method is empirically verified by testing its performance on a number of sample problems and by comparing the results found to earlier results reported in the literature.
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ISSN:0141-9331
1872-9436
DOI:10.1016/0141-9331(95)91156-X