{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:50:27Z","timestamp":1784217027672,"version":"3.55.0"},"reference-count":48,"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>High-order, high-dimension, and large-scale sparse tensors (HHLST) have found their origin in various real industrial applications, such as social networks, recommender systems, bioinformatics, and traffic information. To handle these complex tensors, sparse tensor decomposition techniques are employed to project the HHLST into a low-rank space. In this article, we propose a novel sparse tensor decomposition model called Sparse FastTucker Decomposition (SFTD), which is a variant of Sparse Tucker Decomposition (STD). The SFTD utilizes Kruskal approximation for the core tensor, and we present a theorem that reduces the exponential space and computational overhead to a polynomial one. Additionally, we reduce the space overhead of intermediate parameters in the algorithmic process by sampling the intermediate matrix. Furthermore, this method guarantees convergence. To enhance the speed of SFTD, we leverage the compactness of matrix multiplication and parallel access through a stochastic strategy, resulting in GPU-accelerated cuFastTucker. Moreover, we propose a data division and communication strategy for cuFastTucker to accommodate data on Multi-GPU setups. Our proposed cuFastTucker demonstrates faster calculation and convergence speeds, as well as significantly lower space and computational overhead compared to state-of-the-art (SOTA) algorithms such as P-Tucker, Vest, GTA, Bigtensor, and SGD_Tucker.<\/jats:p>","DOI":"10.1145\/3661450","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T11:56:10Z","timestamp":1714478170000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["cuFastTucker: A Novel Sparse FastTucker Decomposition For HHLST on Multi-GPUs"],"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":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4510-5979","authenticated-orcid":false,"given":"Yikun","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2002-0047","authenticated-orcid":false,"given":"Mengquan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,6,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1088\/0264-9381\/26\/14\/143001"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1137\/07070111X"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000059"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000067"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3350875"},{"key":"e_1_3_2_7_2","first-page":"12113","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Hou Ming","year":"2019","unstructured":"Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, and Qibin Zhao. 2019. Deep multimodal multilinear fusion with high-order polynomial pooling. In Proceedings of the Advances in Neural Information Processing Systems. 12113\u201312122."},{"key":"e_1_3_2_8_2","article-title":"Smooth compact tensor ring regression","author":"Liu Jiani","year":"2020","unstructured":"Jiani Liu, Ce Zhu, and Yipeng Liu. 2020. Smooth compact tensor ring regression. IEEE Transactions on Knowledge and Data Engineering 34, 9 (2020), 4439\u20134452.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_9_2","first-page":"1","article-title":"Tensor regression networks","volume":"21","author":"Kossaifi Jean","year":"2020","unstructured":"Jean Kossaifi, Zachary C. Lipton, Arinbj\u00f6rn Kolbeinsson, Aran Khanna, Tommaso Furlanello, and Anima Anandkumar. 2020. Tensor regression networks. Journal of Machine Learning Research 21, 123 (2020), 1\u201321.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2013.2253485"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683383"},{"key":"e_1_3_2_12_2","first-page":"265","volume-title":"Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201916)","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. Tensorflow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201916). 265\u2013283."},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2941716"},{"key":"e_1_3_2_14_2","article-title":"Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors","author":"Luo Xin","year":"2019","unstructured":"Xin Luo, Hao Wu, Huaqiang Yuan, and MengChu Zhou. 2019. Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors. IEEE Transactions on Cybernetics 50, 5 (2019), 1798\u20131809.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3319856"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700360"},{"key":"e_1_3_2_17_2","article-title":"HO-OTSVD: A novel tensor decomposition and its incremental decomposition for cyber-physical-social networks (CPSN)","author":"Wang Puming","year":"2019","unstructured":"Puming Wang, Laurence T. Yang, Gongwei Qian, Jintao Li, and Zheng Yan. 2019. HO-OTSVD: A novel tensor decomposition and its incremental decomposition for cyber-physical-social networks (CPSN). IEEE Transactions on Network Science and Engineering 7, 2 (2019), 713\u2013725.","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2445757"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2892416"},{"issue":"66","key":"e_1_3_2_20_2","first-page":"13","article-title":"Dimensionality reduction: A comparative","volume":"10","author":"Maaten Laurens Van Der","year":"2009","unstructured":"Laurens Van Der Maaten, Eric Postma, and Jaap Van den Herik. 2009. Dimensionality reduction: A comparative. Journal of Machine Learning Research 10, 66-71 (2009), 13.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1522"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972771.32"},{"key":"e_1_3_2_23_2","first-page":"374","volume-title":"Proceedings of the International Conference on Supercomputing","author":"Chakaravarthy Venkatesan T.","year":"2018","unstructured":"Venkatesan T. Chakaravarthy, Jee W. Choi, Douglas J. Joseph, Prakash Murali, Shivmaran S. Pandian, Yogish Sabharwal, and Dheeraj Sreedhar. 2018. On optimizing distributed Tucker decomposition for sparse tensors. In Proceedings of the International Conference on Supercomputing. 374\u2013384."},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00104"},{"key":"e_1_3_2_25_2","first-page":"373","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Yu Rose","year":"2016","unstructured":"Rose Yu and Yan Liu. 2016. Learning from multiway data: Simple and efficient tensor regression. In Proceedings of the International Conference on Machine Learning. PMLR, 373\u2013381."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2705426"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01191"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3074329"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-64203-1_47"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2018.07.018"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3330345.3330367"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00104"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","unstructured":"Moonjeong Park Jun-Gi Jang and Sael Lee. 2021. Vest: Very sparse tucker factorization of large-scale tensors. IEEE International Conference on Big Data and Smart Computing (BigComp). DOI:10.1109\/BigComp51126.2021.00041","DOI":"10.1109\/BigComp51126.2021.00041"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2019.2908639"},{"key":"e_1_3_2_35_2","first-page":"315","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Johnson Rie","year":"2013","unstructured":"Rie Johnson and Tong Zhang. 2013. Accelerating stochastic gradient descent using predictive variance reduction. In Proceedings of the Advances in Neural Information Processing Systems. 315\u2013323."},{"key":"e_1_3_2_36_2","first-page":"7184","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Yu Hao","year":"2019","unstructured":"Hao Yu, Rong Jin, and Sen Yang. 2019. On the linear speedup analysis of communication efficient momentum sgd for distributed non-convex optimization. In Proceedings of the International Conference on Machine Learning. 7184\u20137193."},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-016-1030-6"},{"key":"e_1_3_2_38_2","first-page":"2613","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Nguyen Lam M.","year":"2017","unstructured":"Lam M. Nguyen, Jie Liu, Katya Scheinberg, and Martin Tak\u00e1\u010d. 2017. SARAH: A novel method for machine learning problems using stochastic recursive gradient. In Proceedings of the International Conference on Machine Learning. JMLR. org, 2613\u20132621."},{"key":"e_1_3_2_39_2","volume-title":"Introductory Lectures on Convex Optimization: A Basic Course","author":"Nesterov Yurii","year":"2013","unstructured":"Yurii Nesterov. 2013. Introductory Lectures on Convex Optimization: A Basic Course. Springer Science and Business Media."},{"key":"e_1_3_2_40_2","unstructured":"Benjamin Recht Christopher Re Stephen Wright and Feng Niu. 2011. Hogwild!: A lock-free approach to parallelizing stochastic gradient descent. In Advances in Neural Information Processing Systems Curran Associates Inc. Retrieved from https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2011\/file\/218a0aefd1d1a4be65601cc6ddc1520e-Paper.pdf"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","unstructured":"H. Li Z. Li K. Li J. S. Rellermeyer L. Y. Chen and K. Li. 2021. SGD_Tucker: A novel stochastic optimization strategy for parallel sparse tucker decomposition. IEEE Transactions on Parallel and Distributed Systems 32 7 (2021) 1828\u20131841. DOI:10.1109\/TPDS.2020.3047460","DOI":"10.1109\/TPDS.2020.3047460"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigComp51126.2021.00041"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00104"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2019.2908639"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983332"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1018"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240369"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1248"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.14778\/2732967.2732973"}],"container-title":["ACM Transactions on Parallel Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3661450","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3661450","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:02Z","timestamp":1750291442000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3661450"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,8]]},"references-count":48,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,6,30]]}},"alternative-id":["10.1145\/3661450"],"URL":"https:\/\/doi.org\/10.1145\/3661450","relation":{},"ISSN":["2329-4949","2329-4957"],"issn-type":[{"value":"2329-4949","type":"print"},{"value":"2329-4957","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,8]]},"assertion":[{"value":"2023-05-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-18","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}