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              <p>The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity, of the computing resources. The pressure to maintain reasonable levels of performance and portability, forces the application developers to leave the traditional programming paradigms and explore alternative solutions. PaStiX is a parallel sparse direct solver, based on a dynamic scheduler for modern hierarchical architectures. In this paper, we study the replacement of the highly specialized internal scheduler in PaStiX by two generic runtime frameworks: PaRSEC and StarPU. The tasks graph of the factorization step is made available to the two runtimes, providing them with the opportunity to optimize it in order to maximize the algorithm efficiency for a predefined execution environment. A comparative study of the performance of the PaStiX solver with the three schedulers - native PaStiX, StarPU and PaRSEC schedulers - on different execution contexts is performed. The analysis highlights the similarities from a performance point of view between the different execution supports. These results demonstrate that these generic DAG-based runtimes provide a uniform and portable programming interface across heterogeneous environments, and are, therefore, a sustainable solution for hybrid environments.</p>
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              <p>Les architectures de calcul intègrent de plus en plus de coeurs de calcul partageant une même mémoire nécessairement hiérarchique. Les algorithmes, en particulier ceux relatifs à l'algèbre linéaire, nécessitent d'être adaptés à ces nouvelles architectures pour être efficaces. PaStIX est un solveur direct parallèle pour matrices creuses qui intègre un ordonnanceur dynamique pour des architectures hiérarchiques de grande taille. Dans ce papier, nous étudions la possibilité de remplacer cette stratégie interne d'ordonnancement par deux supports d'exécution génériques~: PaRSEC et StarPU. Ces supports d'exécution offrent la possibilité de dérouler le graphe de tâches de la factorisation numérique sur des noeuds de calcul disposant d'accélérateurs. Nous présentons une étude comparative des performances de notre solveur supernodal avec ces trois ordonnanceurs sur des architectures multicoeurs, et en particulier les gains obtenus avec plusieurs accélérateurs GPU. Ces résultats montrent qu'une approche basée sur un \DAG{} offre une interface de programmation uniforme pour réaliser du calcul haute performance sur des problèmes irréguliers comme ceux de l'algèbre linéaire creuse.</p>
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