GPart: A GNN-Enabled Multilevel Graph Partitioner

This paper introduces GPart, a scalable multilevel framework for graph partitioning that integrates GNN embeddings with efficient coarsening and refinement techniques. On the Titan23 benchmarks, GPart achieves a cut size reduction of 34.13% to 42.92% over METIS and improves cut size by 9.30% on sele...

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Bibliographic Details
Published in:2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors: Chen, Magi, Wang, Ting-Chi
Format: Conference Proceeding
Language:English
Published: IEEE 22.06.2025
Subjects:
Benchmark testing
Design automation
graph neural network
Graph neural networks
graph partitioning
Memory architecture
Memory management
Scalability
Training
Online Access:Get full text
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Summary:This paper introduces GPart, a scalable multilevel framework for graph partitioning that integrates GNN embeddings with efficient coarsening and refinement techniques. On the Titan23 benchmarks, GPart achieves a cut size reduction of 34.13% to 42.92% over METIS and improves cut size by 9.30% on selected DIMACS benchmarks compared to G-kway. Furthermore, experiments on the Titan23 benchmarks show that GPart reduces normalized memory usage by 24.6x compared to GAP and 12.4x compared to GenPart. Unlike existing GNN-based methods, which require large hidden layers and substantial memory, GPart's multilevel architecture reduces hidden layer sizes, significantly optimizing memory efficiency.
DOI:10.1109/DAC63849.2025.11132100