Cache probabilistic modeling for basic sparse algebra kernels involving matrices with a non-uniform distribution

A probabilistic model to estimate the number of misses on a set associative cache with an LRU replacement algorithm is introduced. Such modeling has been used by our group in previous work for sparse matrices with a uniform distribution of the non-zero elements. We present some new results focusing on different types of distributions that usually appear in some well-known real matrices suites, such as the Harwell-Boeing or NEP