The increasing importance of multicore processors calls for a reevaluation of established numerical algorithms in view of their ability to profit from this new hardware concept. In order to optimize the existent algorithms, a detailed knowledge of the different performance-limiting factors is mandatory. In this contribution we investigate sparse matrix-vector multiplication, which is the dominant operation in many sparse eigenvalue solvers. Two conceptually different storage schemes and computational kernels have been conceived in the past to target cache-based and vector architectures, respectively. Starting from a series of microbenchmarks we apply the gained insight on optimized sparse MVM implementations, whose serial and OpenMP-parallel performance we review on state-of-the-art multicore systems.
@article{arxiv.0910.4836,
title = {Performance limitations for sparse matrix-vector multiplications on current multicore environments},
author = {Gerald Schubert and Georg Hager and Holger Fehske},
journal= {arXiv preprint arXiv:0910.4836},
year = {2012}
}