Multilevel circuit partitioning
DAC '97 Proceedings of the 34th annual Design Automation Conference
Partitioning by iterative deletion
ISPD '99 Proceedings of the 1999 international symposium on Physical design
Iterative improvement based multi-way netlist partitioning for FPGAs
DATE '99 Proceedings of the conference on Design, automation and test in Europe
A new effective and efficient multi-level partitioning algorithm
DATE '00 Proceedings of the conference on Design, automation and test in Europe
Lock-Gain Based Graph Partitioning
Journal of Heuristics
An Effective Multilevel Algorithm for Bisecting Graphs and Hypergraphs
IEEE Transactions on Computers
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Multi-attractor gene reordering for graph bisection
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evaluating the Kernighan-Lin Heuristic for Hardware/Software Partitioning
International Journal of Applied Mathematics and Computer Science
Hi-index | 0.04 |
Iterative improvement partitioning algorithms such as the FM algorithm of Fiduccia and Mattheyses (1982), the algorithm of Krishnamurthy (1984), and Sanchis's extensions of these algorithms to multiway partitioning (1989) all rely on efficient data structures to select the modules to be moved from one partition to the other. The implementation choices for one of these data structures, the gain bucket, is investigated. Surprisingly, selection from gain buckets maintained as last-in-first-out (LIFO) stacks leads to significantly better results than gain buckets maintained randomly (as in previous studies of the FM algorithm or as first-in-first-out (FIFO) queues. In particular, LIFO buckets result in a 36% improvement over random buckets and a 43% improvement over FIFO buckets for minimum-cut bisection. Eliminating randomization from the bucket selection not only improves the solution quality, but has a greater impact on FM performance than adding the Krishnamurthy gain vector. The LIFO selection scheme also results in improvement over random schemes for multiway partitioning and for more sophisticated partitioning strategies such as the two-phase FM methodology. Finally, by combining insights from the LIFO gain buckets with the Krishnamurthy higher-level gain formulation, a new higher-level gain formulation is proposed. This alternative formulation results in a further 22% reduction in the average cut cost when compared directly to the Krishnamurthy formulation for higher-level gains, assuming LIFO organization for the gain buckets