{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:15:39Z","timestamp":1779174939806,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/100008428","name":"Department of Energy and Climate Change","doi-asserted-by":"publisher","award":["DE-AC02-05CH11231"],"award-info":[{"award-number":["DE-AC02-05CH11231"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/100008428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,3,2]]},"DOI":"10.1145\/3627535.3638499","type":"proceedings-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T14:22:41Z","timestamp":1708438961000},"page":"364-376","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Gallatin: A General-Purpose GPU Memory Manager"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4233-9796","authenticated-orcid":false,"given":"Hunter","family":"Mccoy","sequence":"first","affiliation":[{"name":"University of Utah, Salt Lake City, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5576-0320","authenticated-orcid":false,"given":"Prashant","family":"Pandey","sequence":"additional","affiliation":[{"name":"University of Utah, Salt Lake City, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,20]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2023. Kinectica. https:\/\/www.kinetica.com\/"},{"key":"e_1_3_2_1_2_1","unstructured":"2023. NVIDIA Documentation Hub-NVIDIA Docs. https:\/\/docs.nvidia.com\/cuda\/cuda-runtime-api\/group__CUDART__MEMORY.html"},{"key":"e_1_3_2_1_3_1","unstructured":"2023. RAPIDS. https:\/\/rapids.ai\/"},{"key":"e_1_3_2_1_4_1","volume-title":"Adinetz and Dirk Pleiter","author":"Andrew","year":"2014","unstructured":"Andrew V. Adinetz and Dirk Pleiter. 2014. Halloc: a High-Throughput Dynamic Memory Allocator for GPGPU Architectures. https:\/\/github.com\/canonizer\/halloc https:\/\/github.com\/canonizer\/halloc."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2018.00052"},{"key":"e_1_3_2_1_6_1","unstructured":"Scott Beamer Krste Asanovi\u0107 and David Patterson. 2017. The GAP Benchmark Suite. arXiv:1508.03619 [cs.DC]"},{"key":"e_1_3_2_1_7_1","volume-title":"Schultze","author":"Becker Matthias","year":"2020","unstructured":"Matthias Becker, Umesh Worlikar, Shobhit Agrawal, Hartmut Schultze, Thomas Ulas, Sharad Singhal, and Joachim L. Schultze. 2020. Scaling Genomics Data Processing with Memory-Driven Computing to Accelerate Computational Biology. In High Performance Computing, Ponnuswamy Sadayappan, Bradford L. Chamberlain, Guido Juckeland, and Hatem Ltaief (Eds.). Springer International Publishing, Cham, 328--344."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPEC.2018.8547541"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13015-017-0097-9"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPEC.2016.7761622"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIT.2010.206"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476311.3476378"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977714.19"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3572848.3577507"},{"key":"e_1_3_2_1_15_1","unstructured":"Hunter Mccoy and Prashant Pandey. 2024. Gallatin Source Code. https:\/\/github.com\/saltsystemslab\/gallatin"},{"key":"e_1_3_2_1_16_1","unstructured":"Hunter Mccoy and Prashant Pandey. 2024. Modified Memory Manager Survey Source Code. https:\/\/github.com\/saltsystemslab\/memmansurvey"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS49936.2021.00061"},{"key":"e_1_3_2_1_18_1","unstructured":"NVIDIA. 2023. CUDA Cooperative Groups. https:\/\/docs.nvidia.com\/cuda\/cuda-c-programming-guide\/index.html#cooperative-groups"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457313"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","unstructured":"Markus Steinberger Michael Kenzel Bernhard Kainz and Dieter Schmalstieg. 2012. ScatterAlloc: Massively parallel dynamic memory allocation for the GPU. In 2012 Innovative Parallel Computing (InPar). 1--10. 10.1109\/InPar.2012.6339604","DOI":"10.1109\/InPar.2012.6339604"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/0020-0190(77)90031-X"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12666"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3392717.3392742"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2018.00063"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3293883.3295701"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437801.3441612"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPEC.2017.8091058"},{"key":"e_1_3_2_1_28_1","unstructured":"Jianting Zhang and Le Gruenwald. 2019. Efficient Quadtree Construction for Indexing Large-Scale Point Data on GPUs: Bottom-Up vs. Top-Down. In ADMS@VLDB. https:\/\/api.semanticscholar.org\/CorpusID:198162358"}],"event":{"name":"PPoPP '24: 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","location":"Edinburgh United Kingdom","acronym":"PPoPP '24","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing","SIGPLAN ACM Special Interest Group on Programming Languages"]},"container-title":["Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627535.3638499","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627535.3638499","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:04Z","timestamp":1750287004000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627535.3638499"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,20]]},"references-count":28,"alternative-id":["10.1145\/3627535.3638499","10.1145\/3627535"],"URL":"https:\/\/doi.org\/10.1145\/3627535.3638499","relation":{},"subject":[],"published":{"date-parts":[[2024,2,20]]},"assertion":[{"value":"2024-02-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}