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These tasks are both computation-heavy and I\/O-intensive as they repeatedly invoke costly graph pattern matching, and produce a large amount of a large volume of intermediate results, among other things. In light of these, no existing single-machine system is able to carry out these tasks even on not-too-large graphs, even using GPUs. Thus we develop MiniClean, a single-machine system for cleaning large graphs. It proposes (1) a workflow that better fits a single machine by pipelining CPU, GPU and I\/O operations; (2) memory footprint reduction with bundled processing and data compression; and (3) a multi-mode parallel model for SIMD, pipelined and independent parallelism, and their scheduling to maximize CPU--GPU synergy. Using real-life graphs, we empirically verify that MiniClean outperforms the SOTA single-machine systems by at least 65.34\u00d7 and multi-machine systems with 32 nodes by at least 8.09\u00d7.<\/jats:p>","DOI":"10.1145\/3725303","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:23:29Z","timestamp":1750281809000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["Rule-Based Graph Cleaning with GPUs on a Single Machine"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1632-4347","authenticated-orcid":false,"given":"Wenchao","family":"Bai","sequence":"first","affiliation":[{"name":"Southeast University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5149-2656","authenticated-orcid":false,"given":"Wenfei","family":"Fan","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Computing Sciences, Shenzhen, Guangdong, China, University of Edinburgh, Edinburgh, Scotland, United Kingdom, and Beihang University, Beijing, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4892-0979","authenticated-orcid":false,"given":"Shuhao","family":"Liu","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Computing Sciences, Shenzhen, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4086-1421","authenticated-orcid":false,"given":"Kehan","family":"Pang","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5701-5279","authenticated-orcid":false,"given":"Xiaoke","family":"Zhu","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9570-1456","authenticated-orcid":false,"given":"Jiahui","family":"Jin","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"2024. 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