Cache Misses Prediction for High Performance Sparse Algorithms

. Many scientific applications handle compressed sparse matrices. Cache behavior during the execution of codes with irregular access patterns, such as those generated by this type of matrices, has not been widely studied. In this work a probabilistic model for the prediction of the number of misses on a direct mapped cache memory considering sparse matrices with an uniform distribution is presented. As an example of the potential usability of such types of models, and taking into account the state of the art with respect to high performance superscalar and/or superpipelined CPUs with a multilevel memory hierarchy, we have modeled the cache behavior of an optimized sparse matrix-dense matrix product algorithm including blocking at the memory and register levels. 1 Introduction Nowadays superscalar and/or superpipelined CPUs provide high speed processing, but global performances are constrained due to memory latencies even despite the fact that a multilevel memory organization is usual...