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Abstract: Sparse matrix-vector multiplication (SpMxV) is one of the most important computational kernels in scientific computing. It often suffers from poor cache utilization and extra load operations because of memory indirections used to exploit sparsity. We propose alternative data structures, along with reordering algorithms to increase effectiveness of these data structures, to reduce the number of memory indirections. Toledo proposed handling the 1x2 blocks of a matrix separately, doing only one... (Update)
Context of citations to this paper: More
...due to their particular sparsity patterns, exhibit more conflicts or reduced spatial locality. Some form of matrix reordering [30, 19, 24, 15], or the use of multiple rc block sizes are likely to be the most e#ective way to address this performance issue. On the Power3,...
...of numerous efforts. Some of them [32,33] target convergence properties of iterative solvers in a parallel setting while others [34 38] optimize the most common numerical kernels or provide intelligent algorithmic choices for the entire problem solv ing process [39,40] In...
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0.1: Optimizing Sparse Matrix-Vector Product Computations.. - Mellor-Crummey, Garvin (2003) (Correct)
0.1: Self-adapting Numerical Software for Next Generation.. - Dongarra, Eijkhout (2002) (Correct)
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0.3: Implementing Efficient Indirections - Maisonneuve, Shapiro (1993) (Correct)
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BibTeX entry: (Update)
Pinar, A. and M. T. Heath, Improving Performance of Sparse Matrix-Vector Multiplication, Proceedings of Supercomputing 99, Portland, OR, November 1999. http://citeseer.ist.psu.edu/pinar99improving.html More
@inproceedings{ pinar99improving,
author = "Ali Pinar and Michael T. Heath",
title = "Improving Performance of Sparse Matrix-vector Multiplication",
year = "1999",
url = "citeseer.ist.psu.edu/pinar99improving.html" }
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