Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures-Reference-Cited by-同舟云学术

Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures

Published:2010 Issue: Volume: Page:111-125
ISSN:0302-9743
Container-title:High Performance Embedded Architectures and Compilers
language:
Short-container-title:

Author:

Monakov Alexander,Lokhmotov Anton,Avetisyan Arutyun

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-11515-8_10.pdf

Reference11 articles.

1. Asanovic, K., Bodik, R., Catanzaro, B.C., Gebis, J.J., Husbands, P., Keutzer, K., Patterson, D.A., Plishker, W.L., Shalf, J., Williams, S.W., Yelick, K.A.: The landscape of parallel computing research: A view from Berkeley. Technical Report UCB/EECS-2006-183, EECS Department, University of California, Berkeley (December 2006)

2. Baskaran, M.M., Bordawekar, R.: Optimizing sparse matrix-vector multiplication on GPUs. Technical report, IBM TJ Watson Research Center (2009)

3. Bell, N., Garland, M.: Efficient sparse matrix-vector multiplication on CUDA. NVIDIA Technical Report NVR-2008-004 (2008)

4. Lecture Notes in Computer Science;L. Buatois,2007

5. Kincaid, D.R., Oppe, T.C., Young, D.M.: ITPACKV 2D User’s Guide

Cited by 167 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing the Sparse Matrix Storage Using Reordering Techniques;Communications in Computer and Information Science;2024

2. Trajectory-based Metaheuristics for Improving Sparse Matrix Storage;2023 IEEE Latin American Conference on Computational Intelligence (LA-CCI);2023-10-29

3. Estimating the Effect of Indices Compression in the CSR-like Data Storage Formats for Matrix-Vector Multiplications and Solving Linear Systems;Lobachevskii Journal of Mathematics;2023-08

4. Structured Matrices and Their Application in Neural Networks: A Survey;New Generation Computing;2023-07-26

5. Optimizing Sparse Linear Algebra Through Automatic Format Selection and Machine Learning;2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2023-05