Optimality, scalability and stability study of partitioning and placement algorithms

This paper studies the optimality, scalability and stability of stateof-the-art partitioning and placement algorithms. We present algorithms to construct two classes of benchmarks, one for partitioning and the other for placement, which have known upper bounds of their optimal solutions, and can match any given net distribution vector. Using these partitioning and placement benchmarks, we studied the optimality of state-of-the-art algorithms by comparing their solutions with the upper bounds of the optimal solutions, and their scalability and stability by varying the sizes and characteristics of the benchmarks. The conclusions from this study are: 1) State-of-the-art, multilevel two way partitioning algorithms scale very well and are able to find solutions very close to the upper bounds of the optimal solutions of our benchmarks. This suggests that existing circuit partitioning techniques are fairly mature. There is not much room for improvement for cutsize minimization for problems o...

• Type: Conference
• Year: 2003
• Where: ISPD
• Authors: Jason Cong, Michail Romesis, Min Xie
• Keywords: Hardware, ISPD 2003, Optimal Solutions, Placement Algorithms, Upper Bounds