In this thesis, we focus on the parallel solving of large sparse linear systems. Our main interest is on direct-iterative hybrid solvers such as HIPS, MAPHYS, PDSLIN or SHYLU, which rely on domain decomposition and Schur complement approaches. Althrough these solvers are not as time and space consuming as direct methods, they still suffer from serious overheads. In a first part, we thus present the existing techniques for reducing the memory consumption, and we present a new method which does not impact the numerical robustness of the preconditioner. This technique reduces the memory peak by doing a special scheduling of computation, allocation, and freeing tasks in particular in the Schur coupling blocks of the matrix. In a second part, we focus on the load balancing of the domain decomposition in a parallel context. This problem consists in partitioning the adjacency graph of the matrix in as many domains as desired. We point out that a good load balancing for the most expensive steps of an hybrid solver such as MAPHYS relies on the balancing of both interior nodes and interface nodes of the domains. Through, until now, graph partitioners such as METIS or SCOTCH used to optimize only the first criteria (i.e. the balancing of interior nodes) in the context of sparse matrix ordering. We propose different variations of the existing algorithms to improve the balancing of interface nodes and interior nodes simultaneously. All our changes are implemented in the SCOTCH partitioner. We present our results on large collection of matrices coming from real industrial cases.