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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract 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.
AbstractList 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.
Author Lee, Wan Luan
Jiang, Shui
Lin, Dian-Lun
Chang, Che
Zhang, Boyang
Ho, Tsung-Yi
Schlichtmann, Ulf
Huang, Tsung-Wei
Chung, Yi-Hua
Author_xml – sequence: 1
  givenname: Wan Luan
  surname: Lee
  fullname: Lee, Wan Luan
  email: wlee329@wisc.edu
  organization: University of Wisconsin-Madison,USA
– sequence: 2
  givenname: Shui
  surname: Jiang
  fullname: Jiang, Shui
  email: sjiang22@cse.cuhk.edu.hk
  organization: The Chinese University of Hong Kong,Hong Kong
– sequence: 3
  givenname: Dian-Lun
  surname: Lin
  fullname: Lin, Dian-Lun
  email: dianlun.lin@wisc.edu
  organization: University of Wisconsin-Madison,USA
– sequence: 4
  givenname: Che
  surname: Chang
  fullname: Chang, Che
  email: cchang289@wisc.edu
  organization: University of Wisconsin-Madison,USA
– sequence: 5
  givenname: Boyang
  surname: Zhang
  fullname: Zhang, Boyang
  email: bzhang523@wisc.edu
  organization: University of Wisconsin-Madison,USA
– sequence: 6
  givenname: Yi-Hua
  surname: Chung
  fullname: Chung, Yi-Hua
  email: yihua.chung@wisc.edu
  organization: University of Wisconsin-Madison,USA
– sequence: 7
  givenname: Ulf
  surname: Schlichtmann
  fullname: Schlichtmann, Ulf
  email: ulf.schlichtmann@tum.de
  organization: Technical University of Munich,Germany
– sequence: 8
  givenname: Tsung-Yi
  surname: Ho
  fullname: Ho, Tsung-Yi
  email: tyho@cse.cuhk.edu.hk
  organization: The Chinese University of Hong Kong,Hong Kong
– sequence: 9
  givenname: Tsung-Wei
  surname: Huang
  fullname: Huang, Tsung-Wei
  email: tsung-wei.huang@wisc.edu
  organization: University of Wisconsin-Madison,USA
BookMark eNo1j8FKAzEURSPoQmv_QCQ_MDXJS2bmuSujjoWCXdh1ecm80dA2U-KA9O-tWFcHzoEL90ZcpiGxEPdazbRW-PA0b0qoLc6MMu6kNBhU9kJMscIaQDsFytbXwsa22H7T8VEuUsi85zTSTm6Lk5JtpsOnXFEe4xiHFNOHHJJsV-tbcdXT7ounZ07E-uX5vXktlm_topkvC9IVjgVw17PqEDF4b5Erhg6C8yWGTgUGNHXnAv3GmkLpAqIBr0vVa6qo8jARd3-7kZk3hxz3lI-b_zPwA_ELQ3o
ContentType Conference Proceeding
DBID 6IE
6IH
CBEJK
RIE
RIO
DOI 10.1109/DAC63849.2025.11132904
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan (POP) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP) 1998-present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9798331503048
EndPage 7
ExternalDocumentID 11132904
Genre orig-research
GroupedDBID 6IE
6IH
CBEJK
RIE
RIO
ID FETCH-LOGICAL-a179t-3edfe0d999cbb49e7e3d3c5b69cd0ce3928d5cacbb48ac65c9923b160f1a7a7b3
IEDL.DBID RIE
IngestDate Wed Oct 01 07:05:15 EDT 2025
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-a179t-3edfe0d999cbb49e7e3d3c5b69cd0ce3928d5cacbb48ac65c9923b160f1a7a7b3
PageCount 7
ParticipantIDs ieee_primary_11132904
PublicationCentury 2000
PublicationDate 2025-June-22
PublicationDateYYYYMMDD 2025-06-22
PublicationDate_xml – month: 06
  year: 2025
  text: 2025-June-22
  day: 22
PublicationDecade 2020
PublicationTitle 2025 62nd ACM/IEEE Design Automation Conference (DAC)
PublicationTitleAbbrev DAC
PublicationYear 2025
Publisher IEEE
Publisher_xml – name: IEEE
Score 2.302396
Snippet Recent advances in GPU-accelerated graph partitioning have achieved significant performance gains but remain limited to full graph partitioning, lacking...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Data structures
Design automation
Graphics processing units
Iterative methods
Kernel
Optimization
Partitioning algorithms
Performance gain
Title iG-kway: Incremental k-way Graph Partitioning on GPU
URI https://ieeexplore.ieee.org/document/11132904
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwMhECbaePCkxhrf4eCVlmWhLN5MtfVgmj3YpLdmGNikqdmautX474XdrcaDB2-ERwgMMF-G-WYIueEcUFgMr590nEkExSBBYKKQaCyIjEMt6Sc9mWSzmclbsnrNhfHe185nvheL9V--W-Emmsr6TVr0GP1zV2vdkLVa1m_CTf_-bhhOk4z0E6F6286_0qbUWmN08M_5Dkn3h39H82_NckR2fHlM5GLMlh_weUvDnW6sevBClyxU0XEMO03zeAxaAytdlXScT7tkOnp4Hj6yNudB2CJtKpZ6V3juAmxDa6Xx2qcuRWUHBh1HH9BM5hRCbMwABwpNQGg2GfAiAQ3apiekU65Kf0poGBOjv6dSQoANMuh-UFAURnkMk3BxRrpxyfPXJqzFfLva8z_qL8h-3NjoJyXEJelU642_Inv4Xi3e1te1ML4Ac8aMMw
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA5SBT2pWPFtDl7TZrNJd-NNqm3FWvbQQm9lMpuFUtmV2ir-e5PdreLBg7eQByGZJPMxmW-GkBvOAYVB9_rJlDOJoBgECExkErUBEXMoJT2MRqN4OtVJTVYvuTDW2tL5zLZ8sfzLTwtce1NZu0qL7qN_bispRVDRtWreb8B1-_6u686T9AQUoVqb7r8Sp5R6o7f_zxkPSPOHgUeTb91ySLZsfkTkvM8WH_B5S92trux68EIXzFXRvg88TRN_EGoTKy1y2k8mTTLpPYy7A1ZnPXCbFOkVC22aWZ464IbGSG0jG6YhKtPRmHK0Ds_EqULwjTFgR6F2GM0EHZ4FEEFkwmPSyIvcnhDqxvj476GU4ICDdNofFGSZVhbdJFyckqZf8uy1Cmwx26z27I_6a7I7GD8PZ8PH0dM52fOb7L2mhLggjdVybS_JDr6v5m_Lq1IwX_Hwj3o
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2025+62nd+ACM%2FIEEE+Design+Automation+Conference+%28DAC%29&rft.atitle=iG-kway%3A+Incremental+k-way+Graph+Partitioning+on+GPU&rft.au=Lee%2C+Wan+Luan&rft.au=Jiang%2C+Shui&rft.au=Lin%2C+Dian-Lun&rft.au=Chang%2C+Che&rft.date=2025-06-22&rft.pub=IEEE&rft.spage=1&rft.epage=7&rft_id=info:doi/10.1109%2FDAC63849.2025.11132904&rft.externalDocID=11132904