  We evaluate optimized parallel sparse matrix-vector operations for several
representative application areas on widespread multicore-based cluster
configurations. First the single-socket baseline performance is analyzed and
modeled with respect to basic architectural properties of standard multicore
chips. Beyond the single node, the performance of parallel sparse matrix-vector
operations is often limited by communication overhead. Starting from the
observation that nonblocking MPI is not able to hide communication cost using
standard MPI implementations, we demonstrate that explicit overlap of
communication and computation can be achieved by using a dedicated
communication thread, which may run on a virtual core. Moreover we identify
performance benefits of hybrid MPI/OpenMP programming due to improved load
balancing even without explicit communication overlap. We compare performance
results for pure MPI, the widely used "vector-like" hybrid programming
strategies, and explicit overlap on a modern multicore-based cluster and a Cray
XE6 system.
