The main resources for computation are time, space and energy. Reducing them is the main challenge in the field of processor performance.In this thesis, we are interested in a fourth factor which is information. Information has an important and direct impact on these three resources. We show how it contributes to performance optimization. Landauer has suggested that independently on the hardware where computation is run information erasure generates dissipated energy. This is a fundamental result of thermodynamics in physics. Therefore, under this hypothesis, only reversible computations where no information is ever lost, are likely to be thermodynamically adiabatic and do not dissipate power. Reversibility means that data can always be retrieved from any point of the program. Information may be carried not only by the data but also by the process and input data that generate it. When a computation is reversible, information can also be retrieved from other already computed data and reverse computation. Hence reversible computing improves information locality.This thesis develops these ideas in two directions. In the first part, we address the issue of making a computation DAG (directed acyclic graph) reversible in terms of spatial complexity. We define energetic garbage as the additional number of registers needed for the reversible computation with respect to the original computation. We propose a reversible register allocator and we show empirically that the garbage size is never more than 50% of the DAG size. In the second part, we apply this approach to the trade-off between recomputing (direct or reverse) and storage in the context of supercomputers such as the recent vector and parallel coprocessors, graphical processing units (GPUs), IBM Cell processor, etc., where the gap between processor cycle time and memory access time is increasing. We show that recomputing in general and reverse computing in particular helps reduce register requirements and memory pressure. This approach of reverse rematerialization also contributes to the increase of instruction-level parallelism (Cell) and thread-level parallelism in multicore processors with shared register/memory file (GPU). On the latter architecture, the number of registers required by the kernel limits the number of running threads and affects performance. Reverse rematerialization generates additional instructions but their cost can be hidden by the parallelism gain. Experiments on the highly memory demanding Lattice QCD simulation code on Nvidia GPU show a performance gain up to 11%.

Les trois principales ressources du calcul sont le temps, l'espace et l'énergie, les minimiser constitue un des défis les plus importants de la recherche de la performance des processeurs.Dans cette thèse, nous nous intéressons à un quatrième facteur qui est l'information. L'information a un impact direct sur ces trois facteurs, et nous montrons comment elle contribue ainsi à l'optimisation des performances. Landauer a montré que c’est la destruction - logique - d’information qui coûte de l’énergie, ceci est un résultat fondamental de la thermodynamique en physique. Sous cette hypothèse, un calcul ne consommant pas d’énergie est donc un calcul qui ne détruit pas d’information. On peut toujours retrouver les valeurs d’origine et intermédiaires à tout moment du calcul, le calcul est réversible. L'information peut être portée non seulement par une donnée mais aussi par le processus et les données d’entrée qui la génèrent. Quand un calcul est réversible, on peut aussi retrouver une information au moyen de données déjà calculées et du calcul inverse. Donc, le calcul réversible améliore la localité de l'information. La thèse développe ces idées dans deux directions. Dans la première partie, partant d'un calcul, donné sous forme de DAG (graphe dirigé acyclique), nous définissons la notion de « garbage » comme étant la taille mémoire – le nombre de registres - supplémentaire nécessaire pour rendre ce calcul réversible. Nous proposons un allocateur réversible de registres, et nous montrons empiriquement que le garbage est au maximum la moitié du nombre de noeuds du graphe.La deuxième partie consiste à appliquer cette approche au compromis entre le recalcul (direct ou inverse) et le stockage dans le contexte des supercalculateurs que sont les récents coprocesseurs vectoriels et parallèles, cartes graphiques (GPU, Graphics Processing Unit), processeur Cell d’IBM, etc., où le fossé entre temps d’accès à la mémoire et temps de calcul ne fait que s'aggraver. Nous montons comment le recalcul en général, et le recalcul inverse en particulier, permettent de minimiser la demande en registres et par suite la pression sur la mémoire. Cette démarche conduit également à augmenter significativement le parallélisme d’instructions (Cell BE), et le parallélisme de threads sur un multicore avec mémoire et/ou banc de registres partagés (GPU), dans lequel le nombre de threads dépend de manière importante du nombre de registres utilisés par un thread. Ainsi, l’ajout d’instructions du fait du calcul inverse pour la rematérialisation de certaines variables est largement compensé par le gain en parallélisme. Nos expérimentations sur le code de Lattice QCD porté sur un GPU Nvidia montrent un gain de performances atteignant 11%. / The main resources for computation are time, space and energy. Reducing them is the main challenge in the field of processor performance.In this thesis, we are interested in a fourth factor which is information. Information has an important and direct impact on these three resources. We show how it contributes to performance optimization. Landauer has suggested that independently on the hardware where computation is run information erasure generates dissipated energy. This is a fundamental result of thermodynamics in physics. Therefore, under this hypothesis, only reversible computations where no information is ever lost, are likely to be thermodynamically adiabatic and do not dissipate power. Reversibility means that data can always be retrieved from any point of the program. Information may be carried not only by the data but also by the process and input data that generate it. When a computation is reversible, information can also be retrieved from other already computed data and reverse computation. Hence reversible computing improves information locality.This thesis develops these ideas in two directions. In the first part, we address the issue of making a computation DAG (directed acyclic graph) reversible in terms of spatial complexity. We define energetic garbage as the additional number of registers needed for the reversible computation with respect to the original computation. We propose a reversible register allocator and we show empirically that the garbage size is never more than 50% of the DAG size. In the second part, we apply this approach to the trade-off between recomputing (direct or reverse) and storage in the context of supercomputers such as the recent vector and parallel coprocessors, graphical processing units (GPUs), IBM Cell processor, etc., where the gap between processor cycle time and memory access time is increasing. We show that recomputing in general and reverse computing in particular helps reduce register requirements and memory pressure. This approach of reverse rematerialization also contributes to the increase of instruction-level parallelism (Cell) and thread-level parallelism in multicore processors with shared register/memory file (GPU). On the latter architecture, the number of registers required by the kernel limits the number of running threads and affects performance. Reverse rematerialization generates additional instructions but their cost can be hidden by the parallelism gain. Experiments on the highly memory demanding Lattice QCD simulation code on Nvidia GPU show a performance gain up to 11%.