As computers keeping advancing, many difficult problems which were unable to compute formerly now have the chance to get answered. It is always the goal of mathematicians and computer scientists to compute and get the answers of the linear systems. Since 1950s, there have been a lot of published papers discussing the issue. As the linear systems larger and larger, the computer efficiency required is higher and higher, so that it is very difficult to get the answers of large linear systems. Now, the problems are showing aurora. In this dissertation, several mathematical calculations to compute the linear systems will be discussed, as well as their background and theory. Moreover, they will also be practiced.

Les nouvelles architectures de calcul intensif intègrent de plus en plus de microprocesseurs qui eux-mêmes intègrent un nombre croissant de cœurs de calcul. Cette multiplication des unités de calcul dans les architectures ont fait apparaître des topologies fortement hiérarchiques. Ces architectures sont dites NUMA. Les algorithmes de simulation numérique et les solveurs de systèmes linéaires qui en sont une brique de base doivent s'adapter à ces nouvelles architectures dont les accès mémoire sont dissymétriques. Nous proposons dans cette thèse d'introduire un ordonnancement dynamique adapté aux architectures NUMA dans le solveur PaStiX. Les structures de données du solveur, ainsi que les schémas de communication ont dû être modifiés pour répondre aux besoins de ces architectures et de l'ordonnancement dynamique. Nous nous sommes également intéressés à l'adaptation dynamique du grain de calcul pour exploiter au mieux les architectures multi-cœurs et la mémoire partagée. Ces développements sont ensuite validés sur un ensemble de cas tests sur différentes architectures. / New supercomputers incorporate many microprocessors which include themselves one or many computational cores. These new architectures induce strongly hierarchical topologies. These are called NUMA architectures. Sparse direct solvers are a basic building block of many numerical simulation algorithms. They need to be adapted to these new architectures with Non Uniform Memory Accesses. We propose to introduce a dynamic scheduling designed for NUMA architectures in the PaStiX solver. The data structures of the solver, as well as the patterns of communication have been modified to meet the needs of these architectures and dynamic scheduling. We are also interested in the dynamic adaptation of the computation grain to use efficiently multi-core architectures and shared memory. Experiments on several numerical test cases will be presented to prove the efficiency of the approach on different architectures.