Accurate cross-architecture performance modeling for sparse matrix-vector multiplication (SpMV) on GPUs-Reference-Cited by-同舟云学术

Accurate cross-architecture performance modeling for sparse matrix-vector multiplication (SpMV) on GPUs

Published:2014-02-12 Issue:13 Volume:27 Page:3281-3294
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat.: Pract. Exper.

Author:

Guo Ping¹,Wang Liqiang¹

Affiliation:

1. Department of Computer Science; University of Wyoming; Laramie, WY 82071 USA

Funder

National Science Foundation

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Reference32 articles.

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2. Bell N Garland M Implementing sparse matrix-vector multiplication on throughput-oriented processors SC '09: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis ACM Portland, USA 2009 18:1 18:11 10.1145/1654059.1654078

3. Sparse matrix solvers on the GPU: conjugate gradients and multigrid;Bolz;ACM Transactions on Graphics,2003

4. Vazquez F Ortega G Fernandez JJ Garzon EM Improving the performance of the sparse matrix vector product with GPUs Proceedings of the 2010 10th IEEE International Conference on Computer and Information Technology, CIT '10 IEEE Computer Society Bradford, UK 2010 1146 1151 10.1109/CIT.2010.208

5. Monakov A Lokhmotov A Avetisyan A Automatically tuning sparse matrix-vector multiplication for GPU architectures Proceedings of the 5th International Conference on High Performance Embedded Architectures and Compilers Springer-Verlag Pisa, Italy 2010 111 125

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