cuTensor-Tubal: Efficient Primitives for Tubal-Rank Tensor Learning Operations on GPUs

Tensors are the cornerstone data structures in high-performance computing, big data analysis and machine learning. However, tensor computations are compute-intensive and the running time increases rapidly with the tensor size. Therefore, designing high-performance primitives on parallel architecture...

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Veröffentlicht in:	IEEE transactions on parallel and distributed systems Jg. 31; H. 3; S. 595 - 610
Hauptverfasser:	Zhang, Tao, Liu, Xiao-Yang, Wang, Xiaodong, Walid, Anwar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Computation Computational modeling Computer architecture cuTensor-tubal library Data analysis Data processing Data structures Data transfer (computers) Frequency domain analysis GPU Graphics processing units Libraries Linear algebra Low-tubal-rank tensor decomposition Machine learning Mathematical analysis Matrix decomposition Parallel processing Subroutine libraries (computers) Subroutines t-SVD tensor completion Tensors Video compression
ISSN:	1045-9219, 1558-2183
Online-Zugang:	Volltext
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