When solving large sparse linear systems, both the amount of memory needed and the computational cost represent a burden to efficiency. In order to solve larger systems, low-rank strategies are used to reduce the overall complexity of a solver. In this talk, we present a preliminary study of the use of H-Matrix arithmetic in a supernodal solver. We also present a new feature in PaStiX, a reordering strategy to reduce the number of off-diagonal blocks in the symbolic factorization. It allows BLAS kernels to be more efficient, and those ideas could be explored in the context of a low-rank strategy.