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Abstract: Many data mining algorithms rely on eigenvalue computations or iterative linear solvers in which the running time is dominated by sparse matrix-vector products. Sparse matrix-vector multiplication on modern machines often runs one to two orders of magnitude slower than peak hardware performance, and because of their lack of structure, the worst performance is often observed for matrices from text retrieval and other data mining applications. In this paper we explore a set of memory hierarchy... (Update)
Context of citations to this paper: More
...of computational kernels by using compiler technology to automate the production of these SDCGs. Especially of interest are sparse kernels [34, 32]. Sparse matrix algorithms tend to run much more slowly than their dense matrix counterparts. For example, on a 250 MHz Ultrasparc...
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BibTeX entry: (Update)
Eun-Jin Im and Katherine Yelick. Optimization of sparse matrix kernels for data mining. submitted to First SIAM Conf. on Data Mining, 2000. http://citeseer.ist.psu.edu/im00optimization.html More
@misc{ im00optimization,
author = "E. Im and K. Yelick",
title = "Optimization of sparse matrix kernels for data mining",
note = "submitted to First SIAM Conf. on Data Mining.",
year = "2000",
url = "citeseer.ist.psu.edu/im00optimization.html" }
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