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NAA
2004
Springer

Performance Optimization and Evaluation for Linear Codes

10 years 3 months ago
Performance Optimization and Evaluation for Linear Codes
In this paper, we develop a probabilistic model for estimation of the numbers of cache misses during the sparse matrix-vector multiplication (for both general and symmetric matrices) and the Conjugate Gradient algorithm for 3 types of data caches: direct mapped, s-way set associative with random or with LRU replacement strategies. Using HW cache monitoring tools, we compare the predicted number of cache misses with real numbers on Intel x86 architecture with L1 and L2 caches. The accuracy of our analytical model is around 96%.
Pavel Tvrdík, Ivan Simecek
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where NAA
Authors Pavel Tvrdík, Ivan Simecek
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