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This problem is well-studied in sequential and distributed settings, but not in shared-memory. We close this gap by devising efficient and scalable shared-memory algorithms for all components employed in the best sequential solvers without compromises with regards to solution quality.<\/jats:p>\n          <jats:p>\n            This work presents the scalable and high-quality hypergraph partitioning framework\n            <jats:sans-serif>Mt-KaHyPar<\/jats:sans-serif>\n            . Its most important components are parallel improvement algorithms based on the FM algorithm and maximum flows, as well as a parallel clustering algorithm for coarsening \u2013 which are used in a multilevel scheme with log\n            <jats:italic>(n)<\/jats:italic>\n            levels. As additional components, we parallelize the\n            <jats:italic>n<\/jats:italic>\n            -level partitioning scheme, devise a deterministic version of our algorithm, and present optimizations for plain graphs.\n          <\/jats:p>\n          <jats:p>\n            We evaluate our solver on more than 800 graphs and hypergraphs, and compare it with 25 different algorithms from the literature. Our fastest configuration outperforms almost all existing hypergraph partitioners with regards to both solution quality and running time. Our highest-quality configuration achieves the same solution quality as the best sequential partitioner\n            <jats:sans-serif>KaHyPar<\/jats:sans-serif>\n            , while being an order of magnitude faster with ten threads. Thus, two of our configurations occupy all fronts of the Pareto curve for hypergraph partitioning. Furthermore, our solvers exhibit good speedups, e.g., 29.6x in the geometric mean on 64 cores (deterministic), 22.3x (log\n            <jats:italic>(n)<\/jats:italic>\n            -level), and 25.9x (\n            <jats:italic>n<\/jats:italic>\n            -level).\n          <\/jats:p>","DOI":"10.1145\/3626527","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T12:18:39Z","timestamp":1696853919000},"page":"1-54","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Scalable High-Quality Hypergraph Partitioning"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1895-5828","authenticated-orcid":false,"given":"Lars","family":"Gottesb\u00fcren","sequence":"first","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5399-0496","authenticated-orcid":false,"given":"Tobias","family":"Heuer","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6959-417X","authenticated-orcid":false,"given":"Nikolai","family":"Maas","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3330-9349","authenticated-orcid":false,"given":"Peter","family":"Sanders","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1550-882X","authenticated-orcid":false,"given":"Sebastian","family":"Schlag","sequence":"additional","affiliation":[{"name":"Independent Researcher, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2006.1639360"},{"key":"e_1_3_3_3_2","volume-title":"Parallel and External High Quality Graph Partitioning","author":"Akhremtsev Yaroslav","year":"2019","unstructured":"Yaroslav Akhremtsev. 2019. 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