The fast Fourier transform (FFT) plays an important role in digital signal processing (DSP) applications, and its implementation involves a large number of computations. Many DSP designers have been working on implementations of the FFT algorithms on different devices, such as central processing unit (CPU), Field programmable gate array (FPGA), and graphical processing unit (GPU), in order to accelerate the performance.          We selected the GPU device for the implementations of the FFT algorithm because the hardware of GPU is designed with highly parallel structure. It consists of many hundreds of small parallel processing units. The programming of such a parallel device, can be done by a parallel programming language CUDA (Compute Unified Device Architecture).           In this thesis, we propose different implementations of the FFT algorithm on the NVIDIA GPU using CUDA programming language. We study and analyze the different approaches, and use different techniques to accelerate the computations of the FFT. We also discuss the results and compare different approaches and techniques. Finally, we compare our best cases of results with the CUFFT library, which is a specific library to compute the FFT on NVIDIA GPUs.

Understanding the motion of a camera from only the image(s) it captures is a di cult problem. At best we might hope to estimate the relative motion between camera and scene if we assume a static subject, but once we start considering scenes with dynamic content it becomes di cult to di↵erentiate between motion due to the observer or motion due to scene movement. In this thesis we show how the invaluable cues provided by inertial sensor data can be used to simplify motion analysis and relax requirements for several computer vision problems. This work was funded by the University of Bath.

Orientador: Siang Wun Song / Dissertação (mestrado) - Universidade Federal do ABC. Programa de Pós-Graduação em Ciência da Computação, 2013

Orientador: Raphael Yokoingawa de Camargo / Dissertação (mestrado) - Universidade Federal do ABC. Programa de Pós-Graduação em Ciências da Computação, 2013

Orientador: Luiz Carlos da Silva Rozante / Dissertação (mestrado) - Universidade Federal do ABC. Programa de Pós-Graduação em Ciência da Computação, 2013

En esta tesis doctoral se ha desarrollado una serie de métodos numéricos para el análisis de dispositivos ópticos difractivos. Los dispositivos ópticos difractivos básicamente consisten en medios cuyas características físicas varían de forma periódica. Las aplicaciones de este tipo de elementos son diversas y parten desde filtros ópticos, fabricación de lentes, redes de difracción holográficas hasta aplicaciones de energía solar fotovoltaica. En particular, en esta Tesis Doctoral, se han analizado redes de difraccion holográficas de volumen (tanto en reflexión como en transmisión), redes de difracción basadas en aperturas, así como filtros dieléctricos de capas delgadas. Para el análisis riguroso y completo de estos medios en longitudes de onda ópticas se ha recurrido al método de las Diferencias Finitas en el Dominio del Tiempo (DFDT), el cual permite resolver las ecuaciones de Maxwell que modelan el campo electromagnético, en función del tiempo y del espacio. El estudio mediante el método numérico de las DFDT ha sido contrastado con las teorías clásicas que modelan el comportamiento de estos dispositivos, obteniendo resultados satisfactorios. La aplicación del método de las DFDT en longitudes de onda ópticas implica unas resoluciones temporales y espaciales muy reducidas, por lo que la simulación de mallas de varios órdenes de magnitud de la longitud de onda de trabajo repercute en un aumento del coste computacional y de memoria. Por ello, este método se ha acelarado mediante diferentes técnicas con el propósito de obtener el mayor rendimiento posible en las plataformas de cálculo más comunes en la actualidad: Unidades de Proceso Central (UPC) disponibles en los microprocesadores modernos, y Unidades de Procesado Gráfico (UPG) las cuales están presentes en las tarjetas gráficas de los computadores actuales.

Les risques liés aux débris spatiaux sont à présent considérés comme critiques par les gouvernements et les agences spa-tiales internationales. Durant la dernière décennie les agences spatiales ont développé des logiciels pour simuler la rentrée atmosphérique des satellites et des stations orbitales afin de déterminer les risques et possibles dommages au sol. Néan-moins les outils actuels fournissent des résultats déterministes alors que les modèles employés utilisent des valeurs de paramètres qui sont mal connues. De plus les résultats obtenus dépendent fortement des hypothèses qui sont faites. Une solution pour obtenir des résultats pertinents et exploitables est de prendre en considération les incertitudes que l’on a sur les différents paramètres de la modélisation afin d’effectuer des analyses de type Monte-Carlo. Mais une telle étude est particulièrement gourmande en temps de calcul à cause du grand espace des paramètres à explorer (ce qui nécessite des centaines de milliers de simulations numériques). Dans le cadre de ces travaux de thèse nous proposons un nouveau logiciel de simulation numérique de rentrée atmosphérique de satellite, permettant de façon native de prendre en consi-dération les incertitudes sur les différents paramètres de modélisations pour effectuer des analyses statistiques. Afin de maitriser les temps de calculs cet outil tire avantage de la méthode de Taguchi pour réduire le nombre de paramètres à étudier et aussi des accélérateurs de calculs de type Graphics Processing Units (GPUs) et Intel Xeon Phi. / The risk of space debris is now perceived as primordial by government and international space agencies. Since the last decade, international space agencies have developed tools to simulate the re-entry of satellites and orbital stations in order to assess casualty risk on the ground. Nevertheless , all current tools provide deterministic solutions, though models include various parameters that are not well known. Therefore, the provided results are strongly dependent on the as-sumptions made. One solution to obtain relevant and exploitable results is to include uncertainties around those parame-ters in order to perform Monte-Carlo analysis. But such a study is very time consuming due to the large parameter space to explore (that necessitate hundreds of thousands simulations). As part of this thesis work we propose a new satellite atmospheric reentry simulation to perform statistical analysis. To master computing time this tool takes advantage of Taguchi method to restrain the amount of parameter to study and also takes advantage of computing accelerators like Graphic Processing Units (GPUs) and Intel Xeon Phi.

<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>

Graphics Processing Units (GPUs) have become a competitive accelerator for non-graphics applications, mainly driven by the improvements in GPU programmability. Although the Compute Unified Device Architecture (CUDA) is a simple C-like interface for programming NVIDIA GPUs, porting applications to CUDA remains a challenge to average programmers. In particular, CUDA places on the programmer the burden of packaging GPU code in separate functions, of explicitly managing data transfer between the host and GPU memories, and of manually optimizing the utilization of the GPU memory. We have designed hiCUDA, a high-level directive-based language for CUDA programming. It allows programmers to perform these tedious tasks in a simpler manner, and directly to the sequential code. We have also prototyped a compiler that translates a hiCUDA program to a CUDA program and can handle real-world applications. Experiments using seven standard CUDA benchmarks show that the simplicity hiCUDA provides comes at no expense to performance.

Graphics Processing Units (GPUs) have become a competitive accelerator for non-graphics applications, mainly driven by the improvements in GPU programmability. Although the Compute Unified Device Architecture (CUDA) is a simple C-like interface for programming NVIDIA GPUs, porting applications to CUDA remains a challenge to average programmers. In particular, CUDA places on the programmer the burden of packaging GPU code in separate functions, of explicitly managing data transfer between the host and GPU memories, and of manually optimizing the utilization of the GPU memory. We have designed hiCUDA, a high-level directive-based language for CUDA programming. It allows programmers to perform these tedious tasks in a simpler manner, and directly to the sequential code. We have also prototyped a compiler that translates a hiCUDA program to a CUDA program and can handle real-world applications. Experiments using seven standard CUDA benchmarks show that the simplicity hiCUDA provides comes at no expense to performance.