{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T02:14:30Z","timestamp":1768529670455,"version":"3.49.0"},"reference-count":46,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T00:00:00Z","timestamp":1717804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2022ZD0118302"],"award-info":[{"award-number":["2022ZD0118302"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Program of National Natural Science Foundation of China","award":["U21A20461, 92055213, 62227808"],"award-info":[{"award-number":["U21A20461, 92055213, 62227808"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61872127"],"award-info":[{"award-number":["61872127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Parallel Comput."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>\n            The amount of scientific data is currently growing at an unprecedented pace, with tensors being a common form of data that display high-order, high-dimensional, and sparse features. While tensor-based analysis methods are effective, the vast increase in data size has made processing the original tensor infeasible. Tensor decomposition offers a solution by decomposing the tensor into multiple low-rank matrices or tensors that can be efficiently utilized by tensor-based analysis methods. One such algorithm is the Tucker decomposition, which decomposes an\n            <jats:italic>N<\/jats:italic>\n            -order tensor into\n            <jats:italic>N<\/jats:italic>\n            low-rank factor matrices and a low-rank core tensor. However, many Tucker decomposition techniques generate large intermediate variables and require significant computational resources, rendering them inadequate for processing high-order and high-dimensional tensors. This article introduces FasterTucker decomposition, a novel approach to tensor decomposition that builds on the FastTucker decomposition, a variant of the Tucker decomposition. We propose an efficient parallel FasterTucker decomposition algorithm, called cuFasterTucker, designed to run on a GPU platform. Our algorithm has low storage and computational requirements and provides an effective solution for high-order and high-dimensional sparse tensor decomposition. Compared to state-of-the-art algorithms, our approach achieves a speedup of approximately 7 to 23 times.\n          <\/jats:p>","DOI":"10.1145\/3648094","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T12:38:14Z","timestamp":1708087094000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["cuFasterTucker: A Stochastic Optimization Strategy for Parallel Sparse FastTucker Decomposition on GPU Platform"],"prefix":"10.1145","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4735-794X","authenticated-orcid":false,"given":"Zixuan","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2115-858X","authenticated-orcid":false,"given":"Yunchuan","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8146-527X","authenticated-orcid":false,"given":"Qi","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2681-7898","authenticated-orcid":false,"given":"Wangdong","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2635-7716","authenticated-orcid":false,"given":"Kenli","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1137\/07070111X"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3058103"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16521"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09916-4"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.crme.2018.04.011"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa140"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2887192"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102634"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2021.3061937"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01053"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-020-03294-y"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1515\/rnam-2020-0020"},{"key":"e_1_3_2_14_2","unstructured":"L. R. Tucker. 1964. The extension of factor analysis to three-dimensional matrices. In Contributions to Mathematical Psychology H. Gulliksen and N. Frederiksen (Eds.). Holt Rinehardt & Winston New York (1964) 110\u2013127."},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1137\/S0895479896305696"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1137\/S0895479898346995"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339583"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2008.917929"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2016.7869677"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1137\/110836067"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1137\/090764189"},{"key":"e_1_3_2_22_2","unstructured":"Jiajia Li Yuchen Ma and Richard Vuduc. 2018. ParTI!: A Parallel Tensor Infrastructure for Multicore CPUs and GPUs. (2018). 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