G-kway: Multilevel GPU-Accelerated k-way Graph Partitioner
Wan-Luan Lee, Dian-Lun Lin, Tsung-Wei Huang, Shui Jiang, Tsung-Yi Ho, Yibo Lin, Bei Yu
摘要
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. 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. 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.
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
相关 Paper
- iG-kway: Incremental k-way Graph Partitioning on GPUWan-Luan Lee, Shui Jiang, Dian-Lun Lin, Che Chang 等DAC 2025 · 被引用 10 次
- Accelerating k-Core Decomposition by a GPUAkhlaque Ahmad, Lyuheng Yuan, Da Yan, Guimu Guo 等ICDE 2023 · 被引用 22 次
- GPart: A GNN-Enabled Multilevel Graph PartitionerMagi Chen, Ting-Chi WangDAC 2025 · 被引用 1 次
- Accelerating Truss Decomposition on Heterogeneous ProcessorsYulin Che, Zhuohang Lai, Shixuan Sun, Yue Wang 等VLDB 2020 · 被引用 46 次
- DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by ChunksFahao Chen, Peng Li, Celimuge WuSIGMOD 2024 · 被引用 10 次
