An algorithm that remains in use at the core of many partitioning systems is the Kernighan-Lin algorithm and a variant the Fidducia-Matheysses (FM) algorithm. To understand the FM algorithm we applied principles of data engineering where visualization and statistical analysis are used to analyze the run-time behavior. We identified two improvements to the algorithm which, without clustering or an improved heuristic function, bring the performance of the algorithm near that of more sophisticated algorithms. One improvement is based on the observation, explored empirically, that the full passes in the FM algorithm appear comparable to a stochastic local restart in the search. We motivate this observation with a discussion of recent improvements in Monte Carlo Markov Chain methods in statistics. The other improvement is based on the observation that when an FM-like algorithm is run 20 times and the best run chosen, the performance trace of the algorithm on earlier runs is useful data for learning when to abort a later run. These improvements, implemented with a simple adaptive scheme, are orthogonal to techniques used in state-of-the-art implementations, and therefore should be applicable to other VLSI optimization algorithms.
|Number of pages||8|
|Journal||IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers|
|Publication status||Published - 1 Dec 1997|
|Event||Proceedings of the 1997 IEEE/ACM International Conference on Computer-Aided Design, ICCAD - San Jose, CA, USA, United States of America|
Duration: 9 Nov 1997 → 13 Nov 1997