In this paper, we propose an efficient linear ordering algorithm for netlist partitioning. The proposed algorithm incrementally merges two segments which are selected based on the proposed cost function until only one segment remains. The final resultant segment then corresponds to the linear order. Compared to the earlier work, the proposed algorithm yields an average of 11.4% improvement for the ten-way scaled cost partitioning.

In this paper, we propose a clustering based linear ordering algorithm which consists of global ordering and local ordering. In the global ordering, the algorithm forms clusters from n given vertices and orders the clusters. In the local ordering, the elements in each cluster are linearly ordered. The linear order, thus produced, is used to obtain optimal κ-way partitioning based on scaled cost objective function. When the number of cluster is one, the proposed algorithm is exactly the same as MELO [2]. But the proposed algorithm has more global partitioning information than MELO by clustering. Experiment with 11 benchmark circuits for κ-way (2 κ 10) partitioning shows that the proposed algorithm yields an average of 10.6% improvement over MELO [2] for the κ-way scaled cost partitioning.

Clustering is almost essential in improving the performance of iterative partitioning algorithms. In this paper, we present a clustering algorithm based on the following observation: if a group of cells is assigned to the same partition in numerous local optimum solutions, it is desirable to merge the group into a cluster. The proposed algorithm finds such a group of cells from randomly generated local optimum solutions and merges it into a cluster. We implemented a multilevel bipartitioning algorithm (MBP) based on the proposed clustering algorithm. For MCNC benchmark netlists, MBP improves the total average cut size by 9% and the total best cut size by 3-4%, compared with the previous state-of-the-art partitioners.