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ACM Manag. Data"],"published-print":{"date-parts":[[2026,5,18]]},"abstract":"<jats:p>On-disk graph-based approximate nearest neighbor search (ANNS) is essential for large-scale, high-dimensional vector retrieval, yet its performance is widely recognized to be limited by the prohibitive I\/O costs. Interestingly, we observed that the performance of on-disk graph-based index systems is compute-bound, not I\/O-bound, with the rising of the vector data dimensionality (e.g., hundreds or thousands). This insight uncovers a significant optimization opportunity: existing on-disk graph-based index systems universally target I\/O reduction and largely overlook computational overhead, which leaves a substantial performance improvement space.<\/jats:p>\n                  <jats:p>In this work, we propose Laser, an efficient on-disk graph-based index system for large-scale high-dimensional vector similarity search. In particular, we first conduct performance analysis on existing on-disk graph-based index systems via the adapted roofline model, then we devise a novel on-disk data layout in Laser to effectively alleviate the compute-bound, which is revealed by the above roofline model analysis, by exploiting SIMD instructions on modern CPUs. We next design a suite of optimization techniques (e.g., degree-based node cache, cluster-based entry point selection, and early dispatch strategy) to further improve the performance of Laser. We last conduct extensive experimental studies on a wide range of large-scale high-dimensional vector datasets to verify the superiority of Laser. Specifically, Laser not only surpasses existing on-disk graph-based index systems but also matches or even exceeds the performance of in-memory index systems.<\/jats:p>","DOI":"10.1145\/3802045","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:19:16Z","timestamp":1779128356000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Index Layout and Search Strategy for Large-scale High-dimensional Vector Similarity Search"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1622-0071","authenticated-orcid":false,"given":"Weijian","family":"Chen","sequence":"first","affiliation":[{"name":"Southern University of Science and Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8784-8711","authenticated-orcid":false,"given":"Haotian","family":"Liu","sequence":"additional","affiliation":[{"name":"AlayaDB AI, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9487-1455","authenticated-orcid":false,"given":"Yangshen","family":"Deng","sequence":"additional","affiliation":[{"name":"University of Edinburgh, Edinburgh, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6357-3049","authenticated-orcid":false,"given":"Long","family":"Xiang","sequence":"additional","affiliation":[{"name":"AlayaDB AI, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9670-3518","authenticated-orcid":false,"given":"Liang","family":"Huang","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8424-0092","authenticated-orcid":false,"given":"Bo","family":"Tang","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Similarity search in the blink of an eye with compressed indices. arXiv preprint arXiv:2304.04759","author":"Aguerrebere Cecilia","year":"2023","unstructured":"Cecilia Aguerrebere, Ishwar Bhati, Mark Hildebrand, Mariano Tepper, and Ted Willke. 2023. 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