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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Bibliographic Details
Published in:2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors: Lee, Wan Luan, Jiang, Shui, Lin, Dian-Lun, Chang, Che, Zhang, Boyang, Chung, Yi-Hua, Schlichtmann, Ulf, Ho, Tsung-Yi, Huang, Tsung-Wei
Format: Conference Proceeding
Language:English
Published: IEEE 22.06.2025
Subjects:
Data structures
Design automation
Graphics processing units
Iterative methods
Kernel
Optimization
Partitioning algorithms
Performance gain
Online Access:Get full text
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Summary: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