摘要
This article introduces GenPart 2.0, an enhanced version of the hypergraph partitioner GenPart. While GenPart was limited to handling only unit vertex weights, GenPart 2.0 extends capabilities to include varying vertex weights. This extension is achieved through a variational graph neural network-based generative model and new feature preparation techniques. GenPart 2.0 addresses hypergraph partitioning challenges by establishing an embedding space that adheres to normalized cut and balance constraints. The generative model in GenPart 2.0 is designed to explore various partitioning configurations within this embedding space. Further, it enhances partitioning solutions using the V-cycle method. Testing on VLSI circuit benchmarks, including ISPD98, ISPD2005, and Titan23, under various balance constraints, has demonstrated improved performance by GenPart 2.0.