Sparsity-Aware Tensor Decomposition

Author:

Kurt Sureyya Emre1,Raje Saurabh1,Sukumaran-Rajam Aravind2,Sadayappan P.1

Affiliation:

1. School of Computing, University of Utah,Salt Lake City,Utah

2. Washington State University,Department of EECS,Pullman,Washington

Publisher

IEEE

Reference22 articles.

1. A sparse iteration space transformation framework for sparse tensor algebra

2. On optimizing distributed non-negative Tucker decomposition

3. Scalable Tensor Decompositions for Multi-aspect Data Mining

4. Scalable sparse tensor decompositions in dis-tributed memory systems;kaya;SC'15 Proceedings of the International Conference for High Performance Computing Networking Storage and Analysis,2015

5. Efficient Parallel Sparse Symmetric Tucker Decomposition for High-Order Tensors

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1. Efficient Differentially Private Tensor Factorization in the Parallel and Distributed Computing Paradigm;2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom);2023-12-21

2. Dynasor: A Dynamic Memory Layout for Accelerating Sparse MTTKRP for Tensor Decomposition on Multi-core CPU;2023 IEEE 35th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD);2023-10-17

3. Sgap: towards efficient sparse tensor algebra compilation for GPU;CCF Transactions on High Performance Computing;2023-05-08

4. Towards Optimized Streaming Tensor Completion on multiple GPUs;2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys);2022-12

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