Software for Sparse Tensor Decomposition on Emerging Computing Architectures

DOI10.1137/18M1210691WikidataQ127653016 ScholiaQ127653016MaRDI QIDQ5230593

Eric T. Phipps, Tamara G. Kolda

Publication date: 28 August 2019

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1809.09175

zbMATH Keywords

tensor decompositionmanycorecanonical polyadicgpukokkosmttkrp

Mathematics Subject Classification ID

Vector and tensor algebra, theory of invariants (15A72) Software, source code, etc. for problems pertaining to linear algebra (15-04) Numerical algorithms for specific classes of architectures (65Y10)

Related Items (3)

Stochastic Gradients for Large-Scale Tensor Decomposition ⋮ Genten ⋮ Quantifiability: a concurrent correctness condition modeled in vector space

Uses Software

• Matlab
• TensorToolbox
• Algorithm 862
• Kokkos
• Vc
• GitHub
• ParTI
• FROSTT
• SPLATT
• Rubik
• DFacTo

Cites Work

• Tensor Decompositions and Applications
• Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of ``Eckart-Young decomposition
• Algorithm 862
• Efficient MATLAB Computations with Sparse and Factored Tensors
• Parallel Candecomp/Parafac Decomposition of Sparse Tensors Using Dimension Trees
• Generalized Canonical Polyadic Tensor Decomposition
• Embedded Ensemble Propagation for Improving Performance, Portability, and Scalability of Uncertainty Quantification on Emerging Computational Architectures

This page was built for publication: Software for Sparse Tensor Decomposition on Emerging Computing Architectures