  Matrix-vector multiplication forms the basis of many iterative solution
algorithms and as such is an important algorithm also for hierarchical
matrices. However, due to its low computational intensity, its performance is
typically limited by the available memory bandwidth. By optimizing the storage
representation of the data within such matrices, this limitation can be lifted
and the performance increased. This applies not only to hierarchical matrices
but for also for other low-rank approximation schemes, e.g. block low-rank
matrices.
