As handheld devices have become increasingly popular, powerful programmable graphics hardware for mobile and handheld devices has been deployed. While many resources on mobile devices are limited, the predominant problem for mobile devices is their limited battery power. Several techniques have been proposed to increase the energy efficiency of mobile applications and improve battery life. In this thesis, we propose a new dynamic voltage and frequency scaling (DVFS) on Graphics Processing Units (GPU). In most cases, cues within the graphics appli- cation can be used to predict portions of a GPU that will be used or unused when the application is run. We partition the GPU into six clock domains that can be clocked at different rates. Specifically, each domain it has its own voltage and frequency set- ting based on its predicted workload to save energy without reducing applications frame rates. In addition, we propose an signature-based algorithm for predicting the workload offered to our six clock domains by a given application to decide voltage and frequency settings. We conduct experiments and compare the results of our new signature based workload prediction algorithm with some other traditional interval based workload prediction algorithms. Our results show that our signature-based prediction can save 30-50% energy without afecting application frame rates.

De nombreux problèmes de calcul numérique demandent parfois à effectuer des calculs très précis. L'étude desystèmes dynamiques chaotiques fournit des exemples très connus: la stabilité du système solaire ou l’itération à longterme de l'attracteur de Lorenz qui constitue un des premiers modèles de prédiction de l'évolution météorologique. Ons'intéresse aussi aux problèmes d'optimisation semi-définie positive mal-posés qui apparaissent dans la chimie oul'informatique quantique.Pour tenter de résoudre ces problèmes avec des ordinateurs, chaque opération arithmétique de base (addition,multiplication, division, racine carrée) demande une plus grande précision que celle offerte par les systèmes usuels(binary32 and binary64). Il existe des logiciels «multi-précision» qui permettent de manipuler des nombres avec unetrès grande précision, mais leur généralité (ils sont capables de manipuler des nombres de millions de chiffres) empêched’atteindre de hautes performances. L’objectif majeur de cette thèse a été de développer un nouveau logiciel à la foissuffisamment précis, rapide et sûr : on calcule avec quelques dizaines de chiffres (quelques centaines de bits) deprécision, sur des architectures hautement parallèles comme les processeurs graphiques et on démontre des bornesd'erreur afin d'être capables d’obtenir des résultats certains. / Many numerical problems require some very accurate computations. Examples can be found in the field ofdynamical systems, like the long-term stability of the solar system or the long-term iteration of the Lorenz attractor thatis one of the first models used for meteorological predictions. We are also interested in ill-posed semi-definite positiveoptimization problems that appear in quantum chemistry or quantum information.In order to tackle these problems using computers, every basic arithmetic operation (addition, multiplication,division, square root) requires more precision than the ones offered by common processors (binary32 and binary64).There exist multiple-precision libraries that allow the manipulation of very high precision numbers, but their generality(they are able to handle numbers with millions of digits) is quite a heavy alternative when high performance is needed.The major objective of this thesis was to design and develop a new arithmetic library that offers sufficient precision, isfast and also certified. We offer accuracy up to a few tens of digits (a few hundred bits) on both common CPU processorsand on highly parallel architectures, such as graphical cards (GPUs). We ensure the results obtained by providing thealgorithms with correctness and error bound proofs.