Matrix Factorization on GPUs with Memory Optimization and Approximate Computing-Reference-Cited by-同舟云学术

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

Published:2018-08-13 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 47th International Conference on Parallel Processing
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Author:

Tan Wei¹,Chang Shiyu²,Fong Liana²,Li Cheng³,Wang Zijun²,Cao LiangLiang⁴

Affiliation:

1. Citadel, Chicago, Illinois

2. IBM Research, Yorktown Heights, New York

3. University of Illinois at Urbana-Champaign, Urbana, Illinois

4. HelloVera.AI, New York, New York

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3225058.3225096

Reference39 articles.

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2. Machine learning at the limit

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4. Adam Coates Brody Huval Tao Wang David Wu Bryan Catanzaro and Ng Andrew. 2013. Deep learning with COTS HPC systems. In ICML. 1337--1345. Adam Coates Brody Huval Tao Wang David Wu Bryan Catanzaro and Ng Andrew. 2013. Deep learning with COTS HPC systems. In ICML. 1337--1345.

5. Henggang Cui James Cipar Qirong Ho Jin Kyu Kim Seunghak Lee Abhimanu Kumar Jinliang Wei Wei Dai Gregory R. Ganger Phillip B. Gibbons Garth A. Gibson and Eric P. Xing. 2014. Exploiting Bounded Staleness to Speed Up Big Data Analytics. In USENIX ATC. 37--48. Henggang Cui James Cipar Qirong Ho Jin Kyu Kim Seunghak Lee Abhimanu Kumar Jinliang Wei Wei Dai Gregory R. Ganger Phillip B. Gibbons Garth A. Gibson and Eric P. Xing. 2014. Exploiting Bounded Staleness to Speed Up Big Data Analytics. In USENIX ATC. 37--48.

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