  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.
