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DTSTAMP:20260402T024533Z
LOCATION:3004\, Level 3
DTSTART;TZID=America/Los_Angeles:20250624T110000
DTEND;TZID=America/Los_Angeles:20250624T111500
UID:dac_DAC 2025_sess144_RESEARCH1114@linklings.com
SUMMARY:GPart: A GNN-Enabled Multilevel Graph Partitioner
DESCRIPTION:Magi Chen and Ting-Chi Wang (National Tsing Hua University)\n\
 nGPart is a scalable multilevel framework for graph partitioning that inte
 grates 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 ben
 chmarks compared to G-kway. Furthermore, experiments on the Titan23 benchm
 arks show that GPart reduces normalized memory usage by 24.6x compared to 
 GAP and 12.4x compared to GenPart. Unlike existing GNN-based methods, whic
 h require large hidden layers and substantial memory, GPart's multilevel a
 rchitecture reduces hidden layer sizes, significantly optimizing memory ef
 ficiency.\n\nTopics: EDA\n\nTracks: EDA7: Physical Design and Verification
 \n\nSession Chairs: Ankur Prasad (Apple Inc.) and Yu-Guang Chen (National 
 Central University)\n\n
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