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dc.contributor.author Lee, Jihye -
dc.contributor.author Han, Donghyoung -
dc.contributor.author Kwon, Oh-Kyoung -
dc.contributor.author Chon, Kang-Wook -
dc.contributor.author Kim, Min-Soo -
dc.date.accessioned 2023-10-27T16:10:23Z -
dc.date.available 2023-10-27T16:10:23Z -
dc.date.created 2023-09-22 -
dc.date.issued 2024-03 -
dc.identifier.issn 0957-4174 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/46563 -
dc.description.abstract Tucker decomposition is used extensively for modeling multi-dimensional data represented as tensors. Owing to the increasing magnitude of nonzero values in real-world tensors, a growing demand has emerged for expeditious and scalable Tucker decomposition techniques. Several graphics processing unit (GPU)-accelerated techniques have been proposed for Tucker decomposition to decrease the decomposition speed. However, these approaches often encounter difficulties in handling extensive tensors owing to their huge memory demands, which exceed the available capacity of GPU memory. This study presents an expandable GPU-based technique for Tucker decomposition called GPUTucker. The proposed method meticulously partitions sizable tensors into smaller sub-tensors, which are referred to as tensor blocks, and effectively implements the GPU-based data pipeline by handling these tensor blocks asynchronously. Extensive experiments demonstrate that GPUTucker outperforms state-of-the-art Tucker decomposition methods in terms of the decomposition speed and scalability. © 2023 Elsevier Ltd -
dc.language English -
dc.publisher Elsevier -
dc.title GPUTucker: Large-Scale GPU-Based Tucker Decomposition Using Tensor Partitioning -
dc.type Article -
dc.identifier.doi 10.1016/j.eswa.2023.121445 -
dc.identifier.scopusid 2-s2.0-85170643569 -
dc.identifier.bibliographicCitation Expert Systems with Applications, v.237, no.Part A -
dc.description.isOpenAccess FALSE -
dc.subject.keywordAuthor Tensor decomposition -
dc.subject.keywordAuthor Big data -
dc.subject.keywordAuthor Graphics processing unit(GPU) -
dc.subject.keywordAuthor Scalable algorithm -
dc.subject.keywordAuthor Memory-efficient method -
dc.subject.keywordPlus APPROXIMATION -
dc.subject.keywordPlus ALGORITHMS -
dc.citation.number Part A -
dc.citation.title Expert Systems with Applications -
dc.citation.volume 237 -
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