Optimization of Sparse Matrix-Vector Multiplication by Auto Selecting Storage Schemes on GPU-Reference-Cited by-同舟云学术

Optimization of Sparse Matrix-Vector Multiplication by Auto Selecting Storage Schemes on GPU

Published:2011 Issue: Volume: Page:547-561
ISSN:0302-9743
Container-title:Computational Science and Its Applications - ICCSA 2011
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Author:

Kubota Yuji,Takahashi Daisuke

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-21887-3_42

Reference8 articles.

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

2. Blelloch, G.E., Heroux, M.A., Zagha, M.: Segmented Operations for Sparse Matrix Computation on Vector Multiprocessors. Tech. rep., Tech. Rep. CMU-CS-93-173, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA (1993)

3. Cevahir, A., Nukada, A., Matsuoka, S.: High performance conjugate gradient solver on multi-gpu clusters using hypergraph partitioning. Computer Science - Research and Development 25, 83–91 (2010), http://dx.doi.org/10.1007/s00450-010-0112-6

4. Davis, T.A., Hu, Y.: The University of Florida Sparse Matrix Collection. ACM Trans. Math. Softw. (to appear)

5. Lecture Notes in Computer Science;T. Kajiyama,2006

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