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              <p>L’émergence d’accélérateurs comme ressources de calcul standard sur les supercalculateurs et la complexification associée des architectures ressuscite le besoin de paradigmes de programmation de haut niveau. La programmation par tâches séquentielle a montré qu’elle pouvait efficacement répondre à ce besoin sur un seul nœud de calcul multicœur possiblement dotée d’accélérateurs, ce qui a motivé son intégration dans le standard OpenMP 4.0. Dans ce papier, nous montrons que ce paradigme peut également être utilisé pour obtenir de hautes performances sur les supercalculateurs modernes composés de plusieurs de ces nœuds de calcul, tout en limitant à au maximum les changements dans le code utilisateur. Afin de prouver cette affirmation, nous avons ajouté au support d’exécution StarPU une couche avancée de gestion des données inter-nœuds qui supporte ce modèle en émettant automatiquement les communications. Nous illustrons notre discussion avec une factorisation de Cholesky tuilée en tâches que nous avons implémentée sur cette nouvelle couche de support exécutif. Nous montrons que cette méthode permet une grande productivité tout en permettant d’obtenir des performances compétitives tant avec l’implémentation de référence ScaLAPACK de Cholesky, qui est basée purement sur l’Interface de Passage de Message (MPI), qu’avec le code Cholesky de DPLASMA, qui implémente un autre modèle de programmation par tâches (non séquentiel).</p>
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