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UID:dac_DAC 2026_sess317_RESEARCH1292@linklings.com
SUMMARY:IMPart: Integration of Memetic Operations into Multi-Level Framewo
 rk for Large-k-Way Hypergraph Partitioning
DESCRIPTION:Yugao Zhu, Zhicheng Guo, Shang Liu, Mengming Li, Jing Wang, an
 d Zhiyao Xie (Hong Kong University of Science and Technology (HKUST))\n\nT
 he problem of k-way hypergraph partitioning is fundamental with significan
 t applications in various fields, including VLSI design and scientific com
 puting. State-of-the-art hypergraph partitioners commonly employ a multi-l
 evel framework encompassing coarsening, initial partitioning, uncoarsening
 , and refinement phases. However, many existing methods do not scale well 
 to problems requiring a large number of partitions (i.e., large k). In pur
 suit of exceptionally high solution quality, existing memetic approaches o
 ften execute their two key operations, recombination and mutation, by invo
 king separate, standalone multi-level partitioners. This design choice, ho
 wever, renders them significantly more time-consuming than standard multi-
 level partitioners. To make such memetic approaches more practical, we pro
 pose an advanced memetic framework, IMPart, which introduces novel recombi
 nation and mutation operators and integrates them directly into the uncoar
 sening phase of a single multi-level framework. This transforms the local 
 searches of different granularities in the traditional multi-level framewo
 rk into a sophisticated, collaborative search. Experimental results on mul
 tiple standard benchmarks demonstrate our framework more effectively escap
 es local optima and explores the global solution space for higher-quality 
 solutions, substantially outperforming all existing hypergraph partitioner
 s for large-k-way hypergraph partitioning. Our framework highlights a new 
 paradigm for the development of advanced hypergraph partitioners.\n\nTrack
 : EDA7-II. Physical Design and Verification, Design\n\nSession Chairs: Chr
 istian Pilato (Politecnico di Milano); Mohsen Imani (University of Califor
 nia, Irvine); Flavio Ponzina (San Diego State University); Amin Farshidi (
 Cadence Design Systems, Inc.); Yonggan Fu (Georgia Institute of Technology
 , NVIDIA); Dimitrios Tsaras (Huawei, Hong Kong University of Science and T
 echnology (HKUST)); Anh Nguyen (Fujitsu Limited); Ruiyang Qin (Villanova U
 niversity); Jiliang Zhang (Hunan University); Meng Li (Peking University);
  and Rassul Bairamkulov (Advanced Micro Devices (AMD))\n\n
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