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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Vydané v:	IEEE transactions on parallel and distributed systems Ročník 31; číslo 3; s. 595 - 610
Hlavní autori:	Zhang, Tao, Liu, Xiao-Yang, Wang, Xiaodong, Walid, Anwar
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.03.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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