G-kway: Multilevel GPU-Accelerated k-way Graph Partitioner using Task Graph Parallelism

Graph partitioning is important for the design of many CAD algorithms. However, as the graph size continues to grow, graph partitioning becomes increasingly time-consuming. Recent research has introduced parallel graph partitioners using either multi-core CPUs or GPUs. However, the speedup of existing CPU graph partitioners is typically limited to a few cores, while the performance of GPU-based solutions is algorithmically limited by available GPU memory. To overcome these challenges, we propose G-kway, an efficient multilevel GPU-accelerated k -way graph partitioner. G-kway introduces an effective union find-based coarsening and a novel independent set-based refinement algorithm to significantly accelerate both the coarsening and uncoarsening stages. Furthermore, when kernel launch overhead becomes substantial in the refinement algorithm, G-kway employs CUDA Graph-based uncoarsening to reduce the overhead and improve performance. Experimental results have shown that G-kway outperforms both the state-of-the-art CPU-based and GPU-based parallel partitioners with an average speedup of 8.6× and 3.8×, respectively, while achieving comparable partitioning quality. Additionally, G-kway with CUDA Graph-based uncoarsening can further accelerate graph partitioning, achieving up to 1.93× speedup over the default G-kway.