Optimizing Sparse Matrix–Vector Multiplications on an ARMv8-based Many-Core Architecture-Reference-Cited by-同舟云学术

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Optimizing Sparse Matrix–Vector Multiplications on an ARMv8-based Many-Core Architecture

Published:2019-01-01 Issue:3 Volume:47 Page:418-432
ISSN:0885-7458
Container-title:International Journal of Parallel Programming
language:en
Short-container-title:Int J Parallel Prog

Author:

Chen Donglin,Fang Jianbin^ORCID,Chen Shizhao,Xu Chuanfu,Wang Zheng

Funder

the National Key R&D Program of China

the National Natural Science Foundation of China

the Open Research Program of China State Key Laboratory of Aerodynamics

the UK Engineering and Physical Sciences Research Council

the Royal Society International Collaboration Grant

Publisher

Springer Science and Business Media LLC

Subject

Information Systems,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s10766-018-00625-8.pdf

Reference52 articles.

1. FT-2000. Phytium Technology Co. Ltd. (2017). http://www.phytium.com.cn/Product/detail?language=1&product_id=7

2. Bell, N., Garland, M.: Implementing sparse matrix-vector multiplication on throughput-oriented processors. In: SC (2009)

3. Che, Y., Xu, C., Fang, J., Wang, Y., Wang, Z.: Realistic performance characterization of CFD applications on intel many integrated core architecture. Comput. J. 58(12), 3279–3294 (2015)

4. Chen, J., Fang, J., Liu, W., Tang, T., Chen, X., Yang, C.: Efficient and portable ALS matrix factorization for recommender systems. In: IPDPS (2017)

5. Chen, J., Fang, J., Liu, W., Tang, T., Yang, C.: clmf: A fine-grained and portable alternating least squares algorithm for parallel matrix factorization. FGCS (2018a). https://doi.org/10.1016/j.future.2018.04.071

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