A Machine Learning-Based Approach for Selecting SpMV Kernels and Matrix Storage Formats-Reference-Cited by-同舟云学术

A Machine Learning-Based Approach for Selecting SpMV Kernels and Matrix Storage Formats

Published:2018-09-01 Issue:9 Volume:E101.D Page:2307-2314
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

CUI Hang¹,HIRASAWA Shoichi²,KOBAYASHI Hiroaki¹,TAKIZAWA Hiroyuki¹³

Affiliation:

1. Graduate School of Information Sciences, Tohoku University

2. Information Systems Architecture Science Research Division, National Institute of Informatics

3. Cyberscience Center, Tohoku University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E101.D/9/E101.D_2017EDP7176/_pdf

Reference33 articles.

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3. [3] W. Abu-Sufah and A.A. Karim, “Auto-tuning of sparse matrix-vector multiplication on graphics processors,” Supercomputing, Lecture Notes in Computer Science, vol.7905, pp.151-164, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013. 10.1007/978-3-642-38750-0_12

4. [4] N. Bell and M. Garland, “Implementing sparse matrix-vector multiplication on throughput-oriented processors,” Proc. Conference on High Performance Computing Networking, Storage and Analysis, Article No. 18, Nov. 2009. 10.1145/1654059.1654078

5. [5] S. Bhowmick, V. Eijkhout, Y. Freund, E. Fuentes, and D. Keyes, “Application of machine learning in selecting sparse linear solvers,” International Journal of High Performance Computing Applications, 2006 (submitted).

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