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                <term xml:lang="en">Load-Balancing</term>
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                <term xml:lang="en">Heterogeneous Platforms</term>
                <term xml:lang="en">Different-Speed Processors</term>
                <term xml:lang="en">Data Redistribution</term>
                <term xml:lang="fr">Plateformes de calcul hétérogènes</term>
                <term xml:lang="fr">Processus de vitesses différentes</term>
                <term xml:lang="fr">Equilibrage de charge</term>
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              <p>In this paper, we deal with redistribution issues for dense linear algebra kernels on heterogeneous platforms. In this context, processors speeds may well vary during the execution of a large kernel, which requires efficient strategies for redistributing the data along the computations. The strategy that we propose is to redistribute data after some well identified static phases and therefore, it is neither fully static nor fully dynamic. We present an optimal algorithm (under some assumptions) for redistributing data when performing matrix matrix multiplication.</p>
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              <p>Dans ce rapport, nous nous intéressons qu problème des redistributions de données pour les noyaux d'algèbre linéaire adaptés aux plateformes hétérogènes. la vitesses des différents processeurs pouvant varier au cours du temps sur ce type de plateformes, il est important de mettre en œuvre des stratégistes de redistributions efficaces afin de maintenir un bon équilibrage de charge tout a long du calcul. La stratégie hybride (ni complètement statique ni complètement dynamique) que nous proposons consiste à redistribuer les données après des phrases d'équilibrages statiques bine délimitées. Nous présentons également un algorithme optimal (sous certaines hypothèses) pour la redistribution des données lors du calcul d'un produit de matrices.</p>
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