Massive graph visualization in an immersive environment, such as virtual reality (VR) or Augmented Reality (AR), has the possibility to improve users’ understanding when exploring data in new ways. To make the most of a visualization, such as this, requires interactive components that are fast enough to accommodate interactivity. By rendering the edges of the graph as shaded lines that imitate three‑dimensional (3D) lines or tubes, one can circumvent technical limitations. This method works well enough when using traditional two‑dimensional (2D) monitors, but representing tubes as flat lines in a virtual environment (VE) makes for a less immersive user experience as opposed to visualizing true 3D geometry. In order to accommodate for these requirements i.e., speed and visual fidelity, we need a time efficient way of producing tubular meshes. This thesis project explores how one can generate tubular geometry utilizing compute shaders in the modern game engine, Unreal Engine (UE). Exploiting the parallel computing power of the graphical processing unit (GPU) we use compute shaders to generate a tubular mesh following a predetermined path. The result from the project is an open source plugin for UE, able to generate tubular geometry at rapid rates. While not giving any major advantages when generating smaller models, comparing it to a sequential implementation, the compute shader implementation create and render models > 40× faster when generating 106 tube segments. A secondary effect of generating most of the data on the GPU, is that we avoid bottlenecks that can occur when surpassing the bandwidth of the central processing unit (CPU) to GPU data transfer. Using this tool researches can more easily explore information visualization in a VE. Furthermore, this thesis promotes extended development of mesh generation, using compute shaders in UE. / Att visualisera stora grafer i en immersiv miljö, såsom VR eller AR, kan förbättra en användares förståelse när de utforskar data på nya sätt. För att få ut det mesta av denna typen av visualiseringar krävs interaktiva komponenter som är tillräckligt snabba för att tillgodose interaktivitet. Genom att visa de linjer, som binder samman en grafs noder, som plana linjer som imiterar 3Dlinjer eller rör, kan man undvika att slå i det tak som tekniska begränsningar medför. Denna metoden är acceptabel vid användning av traditionella 2Dskärmar, men att representera rör som plana linjer i VE ger en mindre immersiv användarupplevelse, i kontrast till att visualisera sann 3D -geometri. För att tillgodose dessa krav dvs, tidseffektivitet och visuella kvaliteter, behöver vi ett effektivt sätt att producera 3D-linjer. Denna uppsats undersöker hur man kan generera rörformad geometri med hjälp av compute shaders i den moderna spelmotorn Unreal Engine (UE). Genom att använda compute shaders kan vi utnyttja den parallella beräkningskraften hos en GPU, kan vi generera ett rörformat mesh som följer en förutbestämd bana. Resultatet från projektet är ett open source-plugin för UE, som kan generera rörformad geometri i höga hastigheter. Även om det inte kan visas ge några större fördelar när man genererar mindre modeller, jämfört med en sekventiell implementering, skapar och renderar implementeringen av compute Shaders modeller > 40× snabbare, när de genererar 106 rörsegment. I och med att den större delen av datan skapas på GPU kan vi också undvika den flaskhals som kan uppstå när vi överskrider bandbredden mellan CPU och GPU. Med hjälp av verktyget som skapats i samband med denna uppsats kan människor lättare utforska informationsvisualisering i VE. Dessutom främjar denna uppsats utökad utveckling av mesh-generering med hjälp av compute shaders i UE.

No description available.

No description available.

La simulation est de plus en plus utilisée dans le domaine industriel du Contrôle Non Destructif. Elle est employée tout au long du processus de contrôle, que ce soit pour en accélérer la mise au point ou en comprendre les résultats. Les travaux menés au cours de cette thèse présentent une méthode de calcul rapide de champ ultrasonore rayonné par un capteur multi-éléments dans une pièce isotrope, permettant un usage interactif des simulations. Afin de tirer parti des architectures parallèles communément disponibles, un modèle régulier (qui limite au maximum les branchements divergents) dérivé du modèle générique présent dans la plateforme logicielle CIVA a été mis au point. Une première implémentation de référence a permis de le valider par rapport aux résultats CIVA et d'analyser son comportement en termes de performances. Le code a ensuite été porté et optimisé sur trois classes d'architectures parallèles aujourd'hui disponibles dans les stations de calcul : le processeur généraliste central (GPP), le coprocesseur manycore (Intel MIC) et la carte graphique (nVidia GPU). Concernant le processeur généraliste et le coprocesseur manycore, l'algorithme a été réorganisé et le code implémenté afin de tirer parti des deux niveaux de parallélisme disponibles, le multithreading et les instructions vectorielles. Sur la carte graphique, les différentes étapes de simulation de champ ont été découpées en une série de noyaux CUDA. Enfin, des bibliothèques de calculs spécifiques à ces architectures, Intel MKL et nVidia cuFFT, ont été utilisées pour effectuer les opérations de Transformées de Fourier Rapides. Les performances et la bonne adéquation des codes produits ont été analysées en détail pour chaque architecture. Dans plusieurs cas, sur des configurations de contrôle réalistes, des performances autorisant l'interactivité ont été atteintes. Des perspectives pour traiter des configurations plus complexes sont dressées. Enfin la problématique de l'industrialisation de ce type de code dans la plateforme logicielle CIVA est étudiée. / The Non Destructive Testing field increasingly uses simulation.It is used at every step of the whole control process of an industrial part, from speeding up control development to helping experts understand results. During this thesis, a simulation tool dedicated to the fast computation of an ultrasonic field radiated by a phase array probe in an isotropic specimen has been developped. Its performance enables an interactive usage. To benefit from the commonly available parallel architectures, a regular model (aimed at removing divergent branching) derived from the generic CIVA model has been developped. First, a reference implementation was developped to validate this model against CIVA results, and to analyze its performance behaviour before optimization. The resulting code has been optimized for three kinds of parallel architectures commonly available in workstations: general purpose processors (GPP), manycore coprocessors (Intel MIC) and graphics processing units (nVidia GPU). On the GPP and the MIC, the algorithm was reorganized and implemented to benefit from both parallelism levels, multhreading and vector instructions. On the GPU, the multiple steps of field computing have been divided in multiple successive CUDA kernels.Moreover, libraries dedicated to each architecture were used to speedup Fast Fourier Transforms, Intel MKL on GPP and MIC and nVidia cuFFT on GPU. Performance and hardware adequation of the produced algorithms were thoroughly studied for each architecture. On multiple realistic control configurations, interactive performance was reached. Perspectives to adress more complex configurations were drawn. Finally, the integration and the industrialization of this code in the commercial NDT plateform CIVA is discussed.

É muito comum modelar problemas em finanças com processos estocásticos, dada a incerteza de suas variáveis de análise. Além disso, problemas reais nesse domínio são, em geral, de grande custo computacional, o que sugere a utilização de plataformas de alto desempenho (HPC) em sua implementação. As novas gerações de arquitetura de hardware gráfico (GPU) possibilitam a programação de propósito geral enquanto mantêm alta banda de memória e grande poder computacional. Assim, esse tipo de arquitetura vem se mostrando como uma excelente alternativa em HPC. Com isso, a proposta principal desse trabalho é estudar o ferramental matemático e computacional necessário para modelagem estocástica em finanças com a utilização de GPUs como plataforma de aceleração. Para isso, apresentamos a GPU como uma plataforma de computação de propósito geral. Em seguida, analisamos uma variedade de geradores de números aleatórios, tanto em arquitetura sequencial quanto paralela. Além disso, apresentamos os conceitos fundamentais de Cálculo Estocástico e de método de Monte Carlo para simulação estocástica em finanças. Ao final, apresentamos dois estudos de casos de problemas em finanças: \"Stops Ótimos\" e \"Cálculo de Risco de Mercado\". No primeiro caso, resolvemos o problema de otimização de obtenção do ganho ótimo em uma estratégia de negociação de ações de \"Stop Gain\". A solução proposta é escalável e de paralelização inerente em GPU. Para o segundo caso, propomos um algoritmo paralelo para cálculo de risco de mercado, bem como técnicas para melhorar a solução obtida. Nos nossos experimentos, houve uma melhora de 4 vezes na qualidade da simulação estocástica e uma aceleração de mais de 50 vezes. / Given the uncertainty of their variables, it is common to model financial problems with stochastic processes. Furthermore, real problems in this area have a high computational cost. This suggests the use of High Performance Computing (HPC) to handle them. New generations of graphics hardware (GPU) enable general purpose computing while maintaining high memory bandwidth and large computing power. Therefore, this type of architecture is an excellent alternative in HPC and comptutational finance. The main purpose of this work is to study the computational and mathematical tools needed for stochastic modeling in finance using GPUs. We present GPUs as a platform for general purpose computing. We then analyze a variety of random number generators, both in sequential and parallel architectures, and introduce the fundamental mathematical tools for Stochastic Calculus and Monte Carlo simulation. With this background, we present two case studies in finance: ``Optimal Trading Stops\'\' and ``Market Risk Management\'\'. In the first case, we solve the problem of obtaining the optimal gain on a stock trading strategy of ``Stop Gain\'\'. The proposed solution is scalable and with inherent parallelism on GPU. For the second case, we propose a parallel algorithm to compute market risk, as well as techniques for improving the quality of the solutions. In our experiments, there was a 4 times improvement in the quality of stochastic simulation and an acceleration of over 50 times.

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.

É muito comum modelar problemas em finanças com processos estocásticos, dada a incerteza de suas variáveis de análise. Além disso, problemas reais nesse domínio são, em geral, de grande custo computacional, o que sugere a utilização de plataformas de alto desempenho (HPC) em sua implementação. As novas gerações de arquitetura de hardware gráfico (GPU) possibilitam a programação de propósito geral enquanto mantêm alta banda de memória e grande poder computacional. Assim, esse tipo de arquitetura vem se mostrando como uma excelente alternativa em HPC. Com isso, a proposta principal desse trabalho é estudar o ferramental matemático e computacional necessário para modelagem estocástica em finanças com a utilização de GPUs como plataforma de aceleração. Para isso, apresentamos a GPU como uma plataforma de computação de propósito geral. Em seguida, analisamos uma variedade de geradores de números aleatórios, tanto em arquitetura sequencial quanto paralela. Além disso, apresentamos os conceitos fundamentais de Cálculo Estocástico e de método de Monte Carlo para simulação estocástica em finanças. Ao final, apresentamos dois estudos de casos de problemas em finanças: \"Stops Ótimos\" e \"Cálculo de Risco de Mercado\". No primeiro caso, resolvemos o problema de otimização de obtenção do ganho ótimo em uma estratégia de negociação de ações de \"Stop Gain\". A solução proposta é escalável e de paralelização inerente em GPU. Para o segundo caso, propomos um algoritmo paralelo para cálculo de risco de mercado, bem como técnicas para melhorar a solução obtida. Nos nossos experimentos, houve uma melhora de 4 vezes na qualidade da simulação estocástica e uma aceleração de mais de 50 vezes. / Given the uncertainty of their variables, it is common to model financial problems with stochastic processes. Furthermore, real problems in this area have a high computational cost. This suggests the use of High Performance Computing (HPC) to handle them. New generations of graphics hardware (GPU) enable general purpose computing while maintaining high memory bandwidth and large computing power. Therefore, this type of architecture is an excellent alternative in HPC and comptutational finance. The main purpose of this work is to study the computational and mathematical tools needed for stochastic modeling in finance using GPUs. We present GPUs as a platform for general purpose computing. We then analyze a variety of random number generators, both in sequential and parallel architectures, and introduce the fundamental mathematical tools for Stochastic Calculus and Monte Carlo simulation. With this background, we present two case studies in finance: ``Optimal Trading Stops\'\' and ``Market Risk Management\'\'. In the first case, we solve the problem of obtaining the optimal gain on a stock trading strategy of ``Stop Gain\'\'. The proposed solution is scalable and with inherent parallelism on GPU. For the second case, we propose a parallel algorithm to compute market risk, as well as techniques for improving the quality of the solutions. In our experiments, there was a 4 times improvement in the quality of stochastic simulation and an acceleration of over 50 times.

La problématique de cette thèse se place à l’interface entre le domaine scientifique du contrôle non destructif par ultrasons (CND US) et l’adéquation algorithme architecture. Le CND US comprend un ensemble de techniques utilisées pour examiner un matériau, qu’il soit en production ou maintenance. Afin de détecter d’éventuels défauts, de les positionner et les dimensionner, des méthodes d’imagerie et de reconstruction ont été développées au CEA-LIST, dans la plateforme logicielle CIVA.L’évolution du matériel d’acquisition entraine une augmentation des volumes de données et par conséquent nécessite toujours plus de puissance de calcul pour parvenir à des reconstructions en temps interactif. L’évolution multicoeurs des processeurs généralistes (GPP), ainsi que l’arrivée de nouvelles architectures comme les GPU rendent maintenant possible l’accélération de ces algorithmes.Le but de cette thèse est d’évaluer les possibilités d’accélération de deux algorithmes de reconstruction sur ces architectures. Ces deux algorithmes diffèrent dans leurs possibilités de parallélisation. Pour un premier, la parallélisation sur GPP est relativement immédiate, contrairement à celle sur GPU qui nécessite une utilisation intensive des instructions atomiques. Quant au second, le parallélisme est plus simple à exprimer, mais l’ordonnancement des nids de boucles sur GPP, ainsi que l’ordonnancement des threads et une bonne utilisation de la mémoire partagée des GPU sont nécessaires pour obtenir un fonctionnement efficace. Pour ce faire, OpenMP, CUDA et OpenCL ont été utilisés et comparés. L’intégration de ces prototypes dans la plateforme CIVA a mis en évidence un ensemble de problématiques liées à la maintenance et à la pérennisation de codes sur le long terme. / This thesis work is placed between the scientific domain of ultrasound non-destructive testing and algorithm-architecture adequation. Ultrasound non-destructive testing includes a group of analysis techniques used in science and industry to evaluate the properties of a material, component, or system without causing damage. In order to characterize possible defects, determining their position, size and shape, imaging and reconstruction tools have been developed at CEA-LIST, within the CIVA software platform.Evolution of acquisition sensors implies a continuous growth of datasets and consequently more and more computing power is needed to maintain interactive reconstructions. General purprose processors (GPP) evolving towards parallelism and emerging architectures such as GPU allow large acceleration possibilities than can be applied to these algorithms.The main goal of the thesis is to evaluate the acceleration than can be obtained for two reconstruction algorithms on these architectures. These two algorithms differ in their parallelization scheme. The first one can be properly parallelized on GPP whereas on GPU, an intensive use of atomic instructions is required. Within the second algorithm, parallelism is easier to express, but loop ordering on GPP, as well as thread scheduling and a good use of shared memory on GPU are necessary in order to obtain efficient results. Different API or libraries, such as OpenMP, CUDA and OpenCL are evaluated through chosen benchmarks. An integration of both algorithms in the CIVA software platform is proposed and different issues related to code maintenance and durability are discussed.

Submitted by Hellen Luz (hellencrisluz@gmail.com) on 2017-07-04T18:45:28Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_EstrategiasParalelismoGpgpu.pdf: 1964519 bytes, checksum: 90e88c79511a80729d175e52be5bc30b (MD5) / Approved for entry into archive by Irvana Coutinho (irvana@ufpa.br) on 2017-08-18T13:25:09Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_EstrategiasParalelismoGpgpu.pdf: 1964519 bytes, checksum: 90e88c79511a80729d175e52be5bc30b (MD5) / Made available in DSpace on 2017-08-18T13:25:09Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_EstrategiasParalelismoGpgpu.pdf: 1964519 bytes, checksum: 90e88c79511a80729d175e52be5bc30b (MD5) Previous issue date: 2017-03-23 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O cálculo do fluxo de carga provê informações fundamentais em um sistema elétrico de potência, informações necessárias para que sejam realizados estudos nos sistemas. No entanto, o fluxo de carga só pode ser realizado em estado de regime permanente. Caso o sistema sofra alguma alteração, seja por variação nas cargas ou modificações dos equipamentos de controle, este cálculo é necessário ser refeito. Por essa necessidade, de constantemente ter que realizar o controle no fluxo de carga, começou-se uma busca por otimizar o tempo necessário desta tarefa. Uma das soluções abordadas para isso foi a utilização de computação paralela, a qual começou a ser utilizada a General Purpose Graphics Processing Unit (GPGPU) como uma alternativa de melhor custo benefício para execuções em arquiteturas paralelas, que consiste na utilização de Graphic Processing Units (GPU) não somente para processamento gráfico, mas também para propósitos gerais. Diversos trabalhos têm tirado proveito da utilização de GPGPU nos cálculos do fluxo de carga, contudo, não há um consenso sobre qual estratégia utilizar para paralelizar neste tipo de hardware, ficando a cargo de cada autor o trabalho de desenvolver seu próprio método, dificultando a utilização da arquitetura para a implementação desses cálculos, tanto para fins acadêmicos, quanto para o mercado. Pela falta de um consenso e palas divergências encontradas nos trabalhos, esta dissertação visa analisar as etapas do fluxo de carga, identificando quais estão mais aptas a paralelização em GPGPU com o intuito de realizar múltiplos cálculos do fluxo de carga simultâneos e reduzir o tempo necessário para o processamento, difundindo uma estratégia eficiente para sistemas de larga escala no mercado e no meio acadêmico, facilitando a replicação para trabalhos futuros com utilização de metaheurísticas para otimização de sistemas elétricos de potência. / The load flow calculation provides fundamental information for an electric power system. However, the load flow can only be carried out in the steady state, in the event of a system suffering any change, by variation in the loads or modifications of the control equipment, this calculation is necessary to be redone. Because of this need, frequently have to perform the load flow, a research has begun to optimize the time needed for this task. A General-Purpose Graphic Processing Unit (GPGPU) as a cost-effective alternative to parallel architecture runs, which has a GPU not only for graphics purposes but also for general purposes. Several works were taken for the use of GPGPU in load flow calculations, there is no consensus on the content of the material, being in charge of each one of the work of its own method, making it difficult to use the architecture for an implementation of calculations, both for academic purposes and for the market. Due to the lack of consensus and differences found in the work, this dissertation aims to analyze the steps of the load flow, identifying which is more suitable to parallelize in GPGPU in order to perform simultaneous load flow calculations and reduces the time required for the processing, an efficient strategy for large scale systems in the market and not academic environment, facilitate the replication for future works using metaheuristics for optimization of power electrical systems.

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.