Two-way partitioning based on direction vector

In the spectral method, the vertices in a graph can be mapped into the vectors in d-dimensional space, thus the vectors are partitioned instead of vertices to obtain graph partitioning. In this paper, we show a method to obtain optimal two-way vector partitioning based on an optimal direction vector...

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Bibliographic Details
Published inEuropean Design and Test Conference: Proceedings of the 1997 European conference on Design and Test; 17-20 Mar. 1997 p. 306
Main Authors Seong, K. S., Kyung, C. M.
Format Conference Proceeding
LanguageEnglish
Published Washington, DC, USA IEEE Computer Society 17.03.1997
SeriesACM Conferences
Subjects
PDV algorithm
optimal direction vector
netlist
two-way partitioning
local optimization
Fiduccia-Mattheyses algorithm
efficient heuristic
NP-problem
spectral method
cutsize reduction
eigenvectors
graph partitioning
circuit layout CAD
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Summary:In the spectral method, the vertices in a graph can be mapped into the vectors in d-dimensional space, thus the vectors are partitioned instead of vertices to obtain graph partitioning. In this paper, we show a method to obtain optimal two-way vector partitioning based on an optimal direction vector. As the problem to find the optimal direction vector is NP-problem, we propose an efficient heuristic to obtain high quality direction vector. As we approximate a given netlist into the graph and only use ten eigenvectors in practice, there is a chance to improve the solution quality by local optimization. Fiduccia-Mattheyses algorithm is employed as a post processing. Compared with FM and MELO, the proposed algorithm PDV reduces cutsize on the average 40% and 20.5%, respectively.
Bibliography:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:0818677864
9780818677861
DOI:10.5555/787260.787681