James B. White, III P. Sadayappan Ohio Supercomputer Center Ohio State...  Home/Search
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Abstract: We analyze single-node performance of sparse matrix-vector multiplication by investigating issues of data locality and fine-grained parallelism. We examine the data-locality characteristics of the compressedsparse -row representation and consider improvements in locality through matrix permutation. Motivated by potential improvements in fine-grained parallelism, we evaluate modified sparse-matrix representations. The results lead to general conclusions about improving single-node performance of ... (Update)
Context of citations to this paper: More
.... While a detailed performance modeling of this operation can be complex, particularly when data reference patterns are included [14 16], a simplified analysis can still yield upper bounds on the achievable performance of this operation. To illustrate the effect of...
...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,...
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BibTeX entry: (Update)
J. White and P. Sadayappan. On improving the performance of sparse matrix-vector multiplication. In Proceedings of the 4th International Conference on High Performance Computing (HiPC '97), pages 578--587. IEEE Computer Society, 1997. http://citeseer.ist.psu.edu/white97improving.html More
@misc{ white97improving,
author = "J. White and P. Sadayappan",
title = "On improving the performance of sparse matrix-vector multiplication",
text = "J. White and P. Sadayappan. On improving the performance of sparse matrix-vector
multiplication. In Proceedings of the 4th International Conference on High
Performance Computing (HiPC '97), pages 578--587. IEEE Computer Society,
1997.",
year = "1997",
url = "citeseer.ist.psu.edu/white97improving.html" }
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