Computer animation has been limited almost exclusively to 2D and 3D. The tools for 3D computer animation have been largely in place for decades and are well-understood. Existing tools for visualizing 4D geometry include minimal animation features. Few tools have been designed specifically for animation of higher-dimensional objects, phenomena, or spaces. None have been designed to be familiar to 3D animators. A general-purpose 4D animation system can be expected to facilitate more widespread understanding of 4D geometry and space, can become the basis for creating unique 3D visual effects, and may offer new insight into 3D animation concepts. We have developed a software package that facilitates general-purpose animation in four spatial dimensions. Standard features from popular 3D animation software have been included and adapted, where appropriate. Many adaptations are trivial; some have required novel solutions. Several features that are possible only in four or more dimensions have been included. The graphical user interface has been designed to be familiar to experienced 3D animators. Keyframe animation is provided by using a set of curves that defines movement in each dimension or rotation plane. An interactive viewport offers multiple visualization methods including slicing and projection. The viewport allows for both manipulation of 4D objects and navigation through virtual 4D space.

This thesis is focused on analyzing a particular notion of complexity of real valued functions through the lens of analog computers. This report features design changes to Pour-El’s notion of an analog computer that reflect this question of complexity in a concrete way. Additionally, these changes to the analog computer allow an extension of Pour-El’s work in which the complexity of a function can be identified with the order of a differentiably algebraic equation that the function satisfies.

<p>Simulating fluid behavior has proven to be a demanding challenge which requires complex computational models and highly efficient data structures. Smoothed Particle Hydrodynamics (SPH) is a particle based computational model used to simulate fluid behavior that has been found capable of producing convincing results. However, the SPH algorithm is computational heavy which makes it cumbersome to work with.</p><p>This master thesis describes how the SPH algorithm can be accelerated by utilizing the GPU’s computational resources. It describes a model for how to distribute the work load on the GPU and presents a suitable data structure. In addition, it proposes a method to represent and handle moving objects in the fluids surroundings. Finally, the performance gain due to the GPU is evaluated by comparing processing times with an identical implementation running solely on the CPU.</p>

Il existe de nombreux modèles de calcul sur les réels. Ces différents modèles<br />calculent diverses fonctions, certains sont plus puissants que d'autres,<br />certains sont deux à deux incomparables. Le calcul sur les réels est donc de<br />ce point de vue bien différent du calcul sur les entiers qui est unifié par la<br />thèse de Church-Turing qui affirme que tous les modèles raisonnables calculent<br />les mêmes fonctions.<br /><br />Les résultats de cette thèse sont de deux sortes. Premièrement, nous<br />montrons des équivalences entre les fonctions récursivement calculables et une<br />certaine classe de fonctions R-récursives et entre les fonctions<br />GPAC-calculables et les fonctions récursivement calculables. Ces deux<br />résultats ne sont cependant valables que si les fonctions présentent quelques<br />caractéristiques : elles doivent être définies sur un compact et dans le<br />premier cas être de classe C^2. Deuxièmement, nous montrons également une<br />hiérarchie de classes de fonctions R-récursives qui caractérisent les<br />fonctions élémentairement calculables, les fonctions<br />En-calculables pour n?3 (où les En sont les<br />fonctions de la hiérarchie de Grzegorczyk), et des fonctions récursivement<br />calculables. Ce résultat utilise un opérateur de limite dont nous avons prouvé<br />la généralité en montrant qu'il transfère une inclusion sur la partie discrète<br />des fonctions en une inclusion sur les fonctions sur les réels elles-mêmes.<br /><br />Ces résultats constituent donc une avancée vers une éventuelle<br />unification des modèles de calcul sur les réels.

The main goal of this thesis project is to identify interesting concepts related to cooling of electrical motors and generators which could be evaluated using suitable computer simulation tools. As the project proceeded it was decided to focus on investigating how the air from a fan flows along the finned frame of a general purpose low voltage electrical machine, how the heat is transferred between the frame and the cooling air and what the temperature distribution looks like. It was also investigated if it is possible to make improvements in the effectiveness of the cooling without adding additional coolers. This investigation focused on varying the fin design and evaluating the resulting temperature distribution. Due to the complex nature of the simulations a segment, and not the full frame, was considered. Simulation model validation was performed through comparing air speed measurements that were performed on two different machines with the corresponding simulated air speed. The validation showed that good agreement between simulated and measured air speeds are obtained. The conclusion from the simulations is that slight modifications to the current fin design could increase the cooling effect of the finned surface. The air velocity measurements also indicate that the cooling of the machines surface could potentially be improved by small changes in the exterior of the frame. / Målet med detta examensarbete var att identifiera intressanta koncept relaterade till kylning av elektriska maskiner och generatorer, som kunde utvärderas med lämplig programvara för datorsimuleringar. Under projektets gång så bestämdes det att fokusera på hur luften från en fläkt flödar längs med en generell lågspänningsmaskin, hur värmen överförs från ramen till den omgivande luften och hur temperaturfördelningen ser ut. Det undersöktes även om det var möjligt att förbättra effektiviteten av kylningen utan att ansluta extra kylanordningar. Undersökningarna fokuserades på olika fendesigner och dess påverkan på värmefördelningen. På grund av simuleringarnas komplexitet så har simuleringarna endast utförts på ett segment istället för hela maskinen. Validering av simuleringarna utfördes genom att jämföra de simulerade lufthastigheterna med verklig lufthastighet som mättes på två maskiner i testmiljö. Valideringen visade att simuleringarna överensstämmer väl med de mätningar som utfördes. Slutsatsen utifrån simuleringarna är att mindre förändringar av fenornas nuvarande design kan förbättra fenornas kylningsförmåga. Mätningarna av lufthastigheten ger även indikationer på att kylningen av maskinens utsida eventuellt kan förbättras genom små förändringar av ramens exteriör.

Nos sistemas computacionais atuais, enquanto é necessário explorar a disponibilidade de múltiplos núcleos, também é obrigatório consumir menos energia. Para acelerar o processo de desenvolvimento de aplicações paralelas e o tornar mais transparente ao programador, Interfaces de Programação Paralela (IPPs) são largamente utilizadas. Entretanto, cada IPP implementa diferentes formas para trocar dados usando regiões compartilhadas da memória. Estas regiões são, geralmente, mais distantes do processador do que regiões privadas da memória e, por consequência, possuem maior tempo de acesso e consumo de energia. Ademais, o sistema de memória dos processadores embarcados é diferente em hierarquia, tamanho, tempo de acesso, consumo de energia, etc., quando comparado aos processadores de propósito geral. Assim, considerando o cenário supracitado, com diferentes IPPs sendo utilizadas em sistemas multicore com diferentes requisitos, neste trabalho será mostrado que cada interface possui comportamento diferente em termos de desempenho, consumo de energia e Energy-Delay Product (EDP), e que este comportamento varia de acordo com a característica da aplicação e o processador utilizado (propósito geral ou embarcado). Por exemplo, Pthreads consome 8% menos energia que o melhor caso de OpenMP; 12% menos que MPI-1; e 8% menos que MPI-2, considerando todos os benchmarks no processador Intel Core i7 (propósito geral). Em contrapartida, no processador ARM Cortex-A9 (sistema embarcado), o melhor caso com OpenMP consumiu 2% menos energia que Pthreads; 6% menos que MPI-1; e 15% menos que MPI-2, para o mesmo conjunto de benchmarks. / In current computer systems, while it is necessary to exploit the availability of multiple cores, it is also mandatory to consume less energy. To accelerate the development of parallel applications and to make it more transparent to the programmer, Parallel APIs (Application Programming Interfaces) are widely used. However, each Parallel API implements different ways to exchange data using shared memory regions. These regions are generally more remote than the private ones, and therefore have greater access time and energy consumption. Furthermore, the memory system of embedded processors is different with regard to hierarchy, size, access time, energy consumption, etc., when compared to general purpose processors. Thus, considering the above scenario, with different Parallel APIs being used in multicore systems with different requirements, this work will show that each interface has different behavior in terms of performance, energy consumption and Energy-Delay Product (EDP), and that this behavior varies according to the characteristic of the application and the processor employed (general purpose or embedded). For example, as a result of this work, we have observed that Pthreads consumes 8% less energy than the best case of OpenMP; 12% less than MPI-1; and 8% less than MPI-2, considering all benchmarks on the Intel Core i7 (general purpose). In contrast, in the ARM Cortex-A9 processor (embedded system), the best case with OpenMP consumed 2% less energy than Pthreads; 6% less than MPI-1; and 15% less than MPI-2 for the same benchmarks set.

General Purpose computing on Graphic Processing Unit (GPGPU) gained atten- tion in 2006 with NVIDIA’s first Tesla Graphic Processing Unit (GPU) which could perform high performance computing. Ever since, researchers have been working on software and hardware techniques to improve the efficiency of running general purpose applications on GPUs. The efficiency can be evaluated using metrics such as energy consumption and throughput and is defined based on the requirements of the system. I define it as obtaining high throughput by consuming minimum energy. GPUs are equipped with a large number of processing units, a high memory bandwidth, and different types of on-chip memory and caches. To run efficiently, an application should maximize the utilization of GPU resources. Therefore, a good correspondence between the computing and memory resources of the GPU and those of application is critical. Since an application’s requirements are fixed, the GPU’s configuration should be tuned to these requirements. Having models to study and predict the power consumption and throughput of running a GPGPU application on a given GPU configuration can help achieve high efficiency. The main purpose of this dissertation is to find a GPU configuration that best matches the requirements of a given application. I propose three models that predict a GPU configuration that runs an application with maximum throughput while consuming minimum energy. The first model is a fast, low-cost and effective approach to optimize resource allocation in future GPUs. The model finds the optimal GPU configuration for different available chip real-estate budgets . The second model considers the power consumption and throughput of a GPGPU application as functions of the GPU configuration parameters. The proposed model accurately predicts the power consumption and throughput of the modeled GPGPU application. I then propose to accelerate the process of building the model using optimization techniques and quantum annealing. I use the proposed model to explore the GPU configuration space of different applications. I apply multiobjective optimization technique to find the configurations that offer minimum power consumption and maximum throughput. Finally, using clustering and classification techniques, I develop models to re- late the power consumption and throughput of GPGPU applications to the code attributes. Both models could accurately predict the optimum configuration for any given GPGPU application. To build these models I have used different machine learning techniques and optimization methods such as Pareto Front and Knapsack optimization problem. I validated the model produced results with simulation results and showed that the models make accurate predictions. These models could be used by GPGPU programmers to identify the architectural parameters that most affect an application’s power consumption and throughput. This information could be translated into software optimization opportunities. Also, these models can be implemented as part of a compiler to help it to make the best optimization decisions. Moreover, GPU manufacturers could gain insight on architectural parameters which would profit GPGPU applications the most in terms of power and performance and hence invest on these. / Graduate

Nos sistemas computacionais atuais, enquanto é necessário explorar a disponibilidade de múltiplos núcleos, também é obrigatório consumir menos energia. Para acelerar o processo de desenvolvimento de aplicações paralelas e o tornar mais transparente ao programador, Interfaces de Programação Paralela (IPPs) são largamente utilizadas. Entretanto, cada IPP implementa diferentes formas para trocar dados usando regiões compartilhadas da memória. Estas regiões são, geralmente, mais distantes do processador do que regiões privadas da memória e, por consequência, possuem maior tempo de acesso e consumo de energia. Ademais, o sistema de memória dos processadores embarcados é diferente em hierarquia, tamanho, tempo de acesso, consumo de energia, etc., quando comparado aos processadores de propósito geral. Assim, considerando o cenário supracitado, com diferentes IPPs sendo utilizadas em sistemas multicore com diferentes requisitos, neste trabalho será mostrado que cada interface possui comportamento diferente em termos de desempenho, consumo de energia e Energy-Delay Product (EDP), e que este comportamento varia de acordo com a característica da aplicação e o processador utilizado (propósito geral ou embarcado). Por exemplo, Pthreads consome 8% menos energia que o melhor caso de OpenMP; 12% menos que MPI-1; e 8% menos que MPI-2, considerando todos os benchmarks no processador Intel Core i7 (propósito geral). Em contrapartida, no processador ARM Cortex-A9 (sistema embarcado), o melhor caso com OpenMP consumiu 2% menos energia que Pthreads; 6% menos que MPI-1; e 15% menos que MPI-2, para o mesmo conjunto de benchmarks. / In current computer systems, while it is necessary to exploit the availability of multiple cores, it is also mandatory to consume less energy. To accelerate the development of parallel applications and to make it more transparent to the programmer, Parallel APIs (Application Programming Interfaces) are widely used. However, each Parallel API implements different ways to exchange data using shared memory regions. These regions are generally more remote than the private ones, and therefore have greater access time and energy consumption. Furthermore, the memory system of embedded processors is different with regard to hierarchy, size, access time, energy consumption, etc., when compared to general purpose processors. Thus, considering the above scenario, with different Parallel APIs being used in multicore systems with different requirements, this work will show that each interface has different behavior in terms of performance, energy consumption and Energy-Delay Product (EDP), and that this behavior varies according to the characteristic of the application and the processor employed (general purpose or embedded). For example, as a result of this work, we have observed that Pthreads consumes 8% less energy than the best case of OpenMP; 12% less than MPI-1; and 8% less than MPI-2, considering all benchmarks on the Intel Core i7 (general purpose). In contrast, in the ARM Cortex-A9 processor (embedded system), the best case with OpenMP consumed 2% less energy than Pthreads; 6% less than MPI-1; and 15% less than MPI-2 for the same benchmarks set.

Ce travail vise à étudier le statut, c’est-à-dire la place et le traitement des termes scientifiques et techniques dans le dictionnaire général. On oppose généralement le dictionnaire général au dictionnaire spécialisé. Le premier s’intéresse aux mots (ou lexique de la langue générale) et le deuxième aux termes (ou lexique des langues spécialisées). Or, le dictionnaire général inclut, en plus des mots de la langue générale, un nombre important de termes. Ces termes figurant alors simultanément dans les deux types de dictionnaire font l’objet de cette étude. Celle-ci se propose ainsi d’examiner les deux aspects macrostructurel - choix et emplacement - et mircostructuel - étiquetage, définition, exemplification et, pour les dictionnaires bilingues, correspondants arabes - des termes dans ce type de dictionnaire. Les termes retenus dans cette recherche relèvent de domaines divers. Ils sont extraits d’un corpus trilingue anglais-français-arabe constitué de textes de vulgarisation publiés par l’ONU. Le statut de ces termes est analysé dans deux dictionnaires monolingues français, deux monolingues arabes et deux bilingues français-arabe. / This research aims to study the status - place and treatment - of scientific and technical terms in general dictionaries. General dictionaries concentrate on words (or general language lexicon) whereas specialised dictionaries focus on terms (or specialised language lexicon). However, general dictionaries include, in addition to words, a large number of terms. These terms, which are simultaneously included in both dictionaries types, are the subject of this study. Thus, it proposes to examine the following aspects of the term: macrostructural - choice and location - and mircostructual - labelling, definition and exemplification as well as the term’s equivalent in the Arabic bilingual dictionaries. The terms which are used in this research are from various domains. They are extracted from a trilingual English-French-Arabic corpus which consists of texts published by the UN. The status of these terms is analysed according to two monolingual french dictionaries, two monolingual arabic dictionaries and two bilingual french-arabic dictionaries.

Nos sistemas computacionais atuais, enquanto é necessário explorar a disponibilidade de múltiplos núcleos, também é obrigatório consumir menos energia. Para acelerar o processo de desenvolvimento de aplicações paralelas e o tornar mais transparente ao programador, Interfaces de Programação Paralela (IPPs) são largamente utilizadas. Entretanto, cada IPP implementa diferentes formas para trocar dados usando regiões compartilhadas da memória. Estas regiões são, geralmente, mais distantes do processador do que regiões privadas da memória e, por consequência, possuem maior tempo de acesso e consumo de energia. Ademais, o sistema de memória dos processadores embarcados é diferente em hierarquia, tamanho, tempo de acesso, consumo de energia, etc., quando comparado aos processadores de propósito geral. Assim, considerando o cenário supracitado, com diferentes IPPs sendo utilizadas em sistemas multicore com diferentes requisitos, neste trabalho será mostrado que cada interface possui comportamento diferente em termos de desempenho, consumo de energia e Energy-Delay Product (EDP), e que este comportamento varia de acordo com a característica da aplicação e o processador utilizado (propósito geral ou embarcado). Por exemplo, Pthreads consome 8% menos energia que o melhor caso de OpenMP; 12% menos que MPI-1; e 8% menos que MPI-2, considerando todos os benchmarks no processador Intel Core i7 (propósito geral). Em contrapartida, no processador ARM Cortex-A9 (sistema embarcado), o melhor caso com OpenMP consumiu 2% menos energia que Pthreads; 6% menos que MPI-1; e 15% menos que MPI-2, para o mesmo conjunto de benchmarks. / In current computer systems, while it is necessary to exploit the availability of multiple cores, it is also mandatory to consume less energy. To accelerate the development of parallel applications and to make it more transparent to the programmer, Parallel APIs (Application Programming Interfaces) are widely used. However, each Parallel API implements different ways to exchange data using shared memory regions. These regions are generally more remote than the private ones, and therefore have greater access time and energy consumption. Furthermore, the memory system of embedded processors is different with regard to hierarchy, size, access time, energy consumption, etc., when compared to general purpose processors. Thus, considering the above scenario, with different Parallel APIs being used in multicore systems with different requirements, this work will show that each interface has different behavior in terms of performance, energy consumption and Energy-Delay Product (EDP), and that this behavior varies according to the characteristic of the application and the processor employed (general purpose or embedded). For example, as a result of this work, we have observed that Pthreads consumes 8% less energy than the best case of OpenMP; 12% less than MPI-1; and 8% less than MPI-2, considering all benchmarks on the Intel Core i7 (general purpose). In contrast, in the ARM Cortex-A9 processor (embedded system), the best case with OpenMP consumed 2% less energy than Pthreads; 6% less than MPI-1; and 15% less than MPI-2 for the same benchmarks set.