iG-kway: Incremental k-way Graph Partitioning on GPU

Recent advances in GPU-accelerated graph partitioning have achieved significant performance gains but remain limited to full graph partitioning, lacking support for incremental updates. This limitation is critical in CAD applications, where circuit graphs undergo iterative, incremental modifications...

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Veröffentlicht in:2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser: Lee, Wan Luan, Jiang, Shui, Lin, Dian-Lun, Chang, Che, Zhang, Boyang, Chung, Yi-Hua, Schlichtmann, Ulf, Ho, Tsung-Yi, Huang, Tsung-Wei
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 22.06.2025
Schlagworte:
Data structures
Design automation
Graphics processing units
Iterative methods
Kernel
Optimization
Partitioning algorithms
Performance gain
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Zusammenfassung:Recent advances in GPU-accelerated graph partitioning have achieved significant performance gains but remain limited to full graph partitioning, lacking support for incremental updates. This limitation is critical in CAD applications, where circuit graphs undergo iterative, incremental modifications during optimization. We present iG-kway, the first GPU-based incremental k-way graph partitioner. iG-kway features an incrementality-aware data structure and a refinement kernel that efficiently updates only affected vertices with minimal quality loss. Experiments show that iG-kway delivers up to 84 \times speedup over the state-of-the-art G-kway with comparable partitioning quality.
DOI:10.1109/DAC63849.2025.11132904