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Storage"],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>Existing in-memory graph storage systems that rely on DRAM have scalability issues because of the limited capacity and volatile nature of DRAM. The emerging persistent memory (PMEM) offers us a chance to solve these issues through its larger capacity and non-volatile characteristics. However, simply adapting existing DRAM-based graph storage systems to PMEM would result in inefficient PMEM stores and accesses, including high read and write amplification to PMEM, imbalanced work division for PMEM accesses, and costly remote PMEM access across NUMA nodes. These issues severely limit the performance of large graph processing.<\/jats:p>\n          <jats:p>\n            In this article, we aim at achieving scalable and high-performance graph processing in PMEM. We first propose an\n            <jats:italic>XPLine-friendly graph storage model<\/jats:italic>\n            that uses vertex-centric graph buffering, hierarchical vertex buffer managing, and in-place vertex block merging to optimize PMEM graph storage. Furthermore, we develop a\n            <jats:italic>scalable graph processing model<\/jats:italic>\n            that leverages multi-threaded work dividing and NUMA-friendly graph accessing to optimize PMEM graph accesses. Based on these techniques, we implement\n            <jats:sans-serif>XPGraph<\/jats:sans-serif>\n            , a PMEM-based graph storage system for large-scale evolving graphs, and several variants for different system settings. Our experiments demonstrate that\n            <jats:sans-serif>XPGraph<\/jats:sans-serif>\n            surpasses the state-of-the-art in-memory graph storage system on a PMEM-based system by 3.07\u00d7 to 4.99\u00d7 in update performance and up to 5.87\u00d7 in query performance, and performs much better in highly parallel multi-threaded scenarios.\n          <\/jats:p>","DOI":"10.1145\/3715332","type":"journal-article","created":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T11:43:39Z","timestamp":1737805419000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Scalable and High-Performance Large-Scale Dynamic Graph Storage and Processing System"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8915-4169","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"},{"name":"Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3894-0427","authenticated-orcid":false,"given":"Weixu","family":"Zong","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7075-4153","authenticated-orcid":false,"given":"Shuibing","family":"He","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"},{"name":"Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3743-8511","authenticated-orcid":false,"given":"Yongkun","family":"Li","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9586-0561","authenticated-orcid":false,"given":"Yinlong","family":"Xu","sequence":"additional","affiliation":[{"name":"Computer Science, University of Science and Technology of China, Heifei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,2,11]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"125","volume-title":"Proceedings of the 2017 USENIX Annual Technical Conference","author":"Ai Zhiyuan","year":"2017","unstructured":"Zhiyuan Ai, Mingxing Zhang, Yongwei Wu, Xuehai Qian, Kang Chen, and Weimin Zheng. 2017. 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