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<dc:title xml:lang="fr">Génération automatique de codes adaptatifs</dc:title>
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<dcterms:abstract xml:lang="fr">Dans cette thèse nous proposons une interface de programmation pour aider les développeurs dans leur tâche d'optimisation de programme par calcul approché. Cette interface prend la forme d'extensions aux langages de programmation pour indiquer au compilateur quelles parties du programme peuvent utiliser ce type de calcul. Le compilateur se charge alors de transformer les parties du programme visées pour rendre l'application adaptative, allouant plus de ressources aux endroits où une précision importante est requise et utilisant des approximations où la précision peut être moindre. Nous avons automatisé la découverte des paramètres d'optimisation que devrait fournir l'utilisateur pour les codes à stencil, qui sont souvent rencontrés dans des applications de traitement du signal, traitement d'image ou simulation numérique. Nous avons exploré des techniques de compression automatique de données pour compléter la génération de code adaptatif. Nous utilisons la transformée en ondelettes pour compresser les données et obtenir d'autres informations qui peuvent être utilisées pour trouver les zones avec des besoins en précision plus importantes.</dcterms:abstract>
<dcterms:abstract xml:lang="en">In this thesis we introduce a new application programming interface to help developers to optimize an application with approximate computing techniques. This interface is provided as a language extension to advise the compiler about the parts of the program that may be optimized with approximate computing and what can be done about them. The code transformations of the targeted regions are entirely handled by the compiler to produce an adaptive software. The produced adaptive application allocates more computing power to the locations where more precision is required, and may use approximations where the precision is secondary. We automate the discovery of the optimization parameters for the special class of stencil programs which are common in signal/image processing and numerical simulations. Finally, we explore the possibility of compressing the application data using the wavelet transform and we use information found in this basis to locate the areas where more precision may be needed.</dcterms:abstract>
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