A gradient method on the initial partition of Fiduccia-Mattheyses algorithm

Authors:
Lung-Tien Liu;Ming-Ter Kuo;Shih-Chen Huang;Chung-Kuan Cheng
Affiliations:
AT&T Bell Laboratories, Murray Hill, NJ;Computer Science and Engineering University of California, San Diego;Department of Computer Science, New York University;Computer Science and Engineering, University of California, San Diego
Venue:
ICCAD '95 Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design
Year:
1995

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Abstract

In this paper, a Fiduccia-Mattheyses (FM) algorithm incorporating a novel initial partition generating method is proposed. The proposed algorithm applies to both bipartitioning and multi-way partitioning problems with or without replication. The initial partition generating method is based on a gradient decent algorithm. On partitioning without replication, our algorithm achieves an average of 17% improvement over the analytical method, PARABOLI, on bipartitioning, 10% better than Primal-Dual method on 4-way partitioning and 51% better than net-based method. On partitioning allowing replication, our algorithm achieves an average of 23% improvement over the directed Fiduccia-Mattheyses algorithm on Replication Graph (FMRG) method on bipartitioning.