Optimization of Sparse Matrix-Vector Multiplication for CRS Format on NVIDIA Kepler Architecture GPUs-Reference-Cited by-同舟云学术

Optimization of Sparse Matrix-Vector Multiplication for CRS Format on NVIDIA Kepler Architecture GPUs

Published:2013 Issue: Volume: Page:211-223
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Mukunoki Daichi,Takahashi Daisuke

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-39640-3_15

Reference14 articles.

1. Baskaran, M.M., Bordawekar, R.: Optimizing Sparse Matrix-Vector Multiplication on GPUs. IBM Research Report RC24704 (2009)

2. Bell, N., Garland, M.: Efficient Sparse Matrix-Vector Multiplication on CUDA. NVIDIA Technical Report NVR-2008-004 (2008)

3. NVIDIA Corporation: Whitepaper NVIDIAs Next Generation CUDA Compute Architecture: Kepler GK110. itepaper.pdf (2012), http://www.nvidia.com/content/PDF/kepler/NVIDIA-Kepler-GK110-Architecture-Wh

4. Davis, J.D., Chung, E.S.: SpMV: A Memory-Bound Application on the GPU Stuck Between a Rock and a Hard Place. Microsoft Technical Report MSR–TR–2012–95 (2012)

5. Davis, T., Hu, Y.: The University of Florida Sparse Matrix Collection, http://www.cise.ufl.edu/research/sparse/matrices/

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