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Autonomous navigation for ground vehicles has many challenges. Autonomous systems must be able to self-localise, avoid obstacles and determine navigable surfaces. This thesis studies several aspects of autonomous navigation with a particular emphasis on vision, motivated by it being a primary component for navigation in many high-level biological organisms.  The key problem of self-localisation or pose estimation can be solved through analysis of the changes in appearance of rigid objects observed from different view points. We therefore describe a system for structure and motion estimation for real-time navigation and obstacle avoidance. With the explicit assumption of a calibrated camera, we have studied several schemes for increasing accuracy and speed of the estimation.The basis of most structure and motion pose estimation algorithms is a good point tracker. However point tracking is computationally expensive and can occupy a large portion of the CPU resources. In thisthesis we show how a point tracker can be implemented efficiently on the graphics processor, which results in faster tracking of points and the CPU being available to carry out additional processing tasks.In addition we propose a novel view interpolation approach, that can be used effectively for pose estimation given previously seen views. In this way, a vehicle will be able to estimate its location by interpolating previously seen data.Navigation and obstacle avoidance may be carried out efficiently using structure and motion, but only whitin a limited range from the camera. In order to increase this effective range, additional information needs to be incorporated, more specifically the location of objects in the image. For this, we propose a real-time object recognition method, which uses P-channel matching, which may be used for improving navigation accuracy at distances where structure estimation is unreliable. / Diplecs

Multi-core, multi-processor, and Graphics Processing Unit (GPU) computer architectures pose significant challenges with respect to the efficient exploitation of parallelism for large-scale, scientific computing simulations. For example, a simulation of the human tonsil at the cellular level involves the computation of the motion and interaction of millions of cells over extended periods of time. Also, the simulation of Radiative Heat Transfer (RHT) effects by the Photon Monte Carlo (PMC) method is an extremely computationally demanding problem. The PMC method is example of the Monte Carlo simulation method—an approach extensively used in wide of application areas. Although the basic algorithmic framework of these Monte Carlo methods is simple, they can be extremely computationally intensive. Therefore, an efficient parallel realization of these simulations depends on a careful analysis of the nature these problems and the development of an appropriate software framework. The overarching goal of this dissertation is develop and understand what the appropriate parallel programming model should be to exploit these disparate architectures, both from the metric of efficiency, as well as from a software engineering perspective. In this dissertation we examine these issues through a performance study of PathSim2, a software framework for the simulation of large-scale biological systems, using two different parallel architectures’ distributed and shared memory. First, a message-passing implementation of a multiple germinal center simulation by PathSim2 is developed and analyzed for distributed memory architectures. Second, a germinal center simulation is implemented on shared memory architecture with two parallelization strategies based on Pthreads and OpenMP. Finally, we present work targeting a complete hybrid, parallel computing architecture. With this work we develop and analyze a software framework for generic Monte Carlo simulations implemented on multiple, distributed memory nodes consisting of a multi-core architecture with attached GPUs. This simulation framework is divided into two asynchronous parts: (a) a threaded, GPU-accelerated pseudo-random number generator (or producer), and (b) a multi-threaded Monte Carlo application (or consumer). The advantage of this approach is that this software framework can be directly used within any Monte Carlo application code, without requiring application-specific programming of the GPU. We examine this approach through a performance study of the simulation of RHT effects by the PMC method on a hybrid computing architecture. We present a theoretical analysis of our proposed approach, discuss methods to optimize performance based on this analysis, and compare this analysis to experimental results obtained from simulations run on two different hybrid, parallel computing architectures. / Ph. D.

Directive-based programming of graphics processing units (GPUs) has recently appeared as a viable alternative to using specialized low-level languages such as CUDA C and OpenCL for general-purpose GPU programming. This technique, which uses directive or pragma statements to annotate source codes written in traditional high-level languages, is designed to permit a unified code base to serve multiple computational platforms and to simplify the transition of legacy codes to new architectures. This work analyzes the popular OpenACC programming standard, as implemented by the PGI compiler suite, in order to evaluate its utility and performance potential in computational fluid dynamics (CFD) applications. Of particular interest is the handling of stencil algorithms, which are an important component of finite-difference and finite-volume numerical methods. To this end, the process of applying the OpenACC Fortran API to a preexisting finite-difference CFD code is examined in detail, and all modifications that must be made to the original source in order to run efficiently on the GPU are noted. Optimization techniques for OpenACC are also explored, and it is demonstrated that tuning the code for a particular accelerator architecture can result in performance increases of over 30%. There are also some limitations and programming restrictions imposed by the API: it is observed that certain useful features of modern Fortran (2003/8) are effectively disabled within OpenACC regions. Finally, a combination of OpenACC and OpenMP directives is used to create a truly cross-platform Fortran code that can be compiled for either CPU or GPU hardware. The performance of the OpenACC code is measured on several contemporary NVIDIA GPU architectures, and a comparison is made between double and single precision arithmetic showing that if reduced precision can be tolerated, it can lead to significant speedups. To assess the performance gains relative to a typical CPU implementation, the execution time for a standard benchmark case (lid-driven cavity) is used as a reference. The OpenACC version is compared against the identical Fortran code recompiled to use OpenMP on multicore CPUs, as well as a highly-optimized C++ version of the code that utilizes hardware aware programming techniques to attain higher performance on the Intel Xeon platforms being tested. Low-level optimizations specific to these architectures are analyzed and it is observed that the stencil access pattern required by the structured-grid CFD code sometimes leads to performance degrading conflict misses in the hardware managed CPU caches. The GPU code, which primarily uses software managed caching, is found to be free from these issues. Overall, it is observed that the OpenACC GPU code compares favorably against even the best optimized CPU version: using a single NVIDIA K20x GPU, the Fortran+OpenACC code is seen to outperform the optimized C++ version by 20% and the Fortran+OpenMP version by more than 100% with both CPU codes running on a 16-core Xeon workstation. / Master of Science

Tesis por compendio / [ES] En la última década la utilización de la GPGPU (General Purpose computing in Graphics Processing Units; Computación de Propósito General en Unidades de Procesamiento Gráfico) se ha vuelto tremendamente popular en los centros de datos de todo el mundo. Las GPUs (Graphics Processing Units; Unidades de Procesamiento Gráfico) se han establecido como elementos aceleradores de cómputo que son usados junto a las CPUs formando sistemas heterogéneos. La naturaleza masivamente paralela de las GPUs, destinadas tradicionalmente al cómputo de gráficos, permite realizar operaciones numéricas con matrices de datos a gran velocidad debido al gran número de núcleos que integran y al gran ancho de banda de acceso a memoria que poseen. En consecuencia, aplicaciones de todo tipo de campos, tales como química, física, ingeniería, inteligencia artificial, ciencia de materiales, etc. que presentan este tipo de patrones de cómputo se ven beneficiadas, reduciendo drásticamente su tiempo de ejecución. En general, el uso de la aceleración del cómputo en GPUs ha significado un paso adelante y una revolución. Sin embargo, no está exento de problemas, tales como problemas de eficiencia energética, baja utilización de las GPUs, altos costes de adquisición y mantenimiento, etc. En esta tesis pretendemos analizar las principales carencias que presentan estos sistemas heterogéneos y proponer soluciones basadas en el uso de la virtualización remota de GPUs. Para ello hemos utilizado la herramienta rCUDA, desarrollada en la Universitat Politècnica de València, ya que multitud de publicaciones la avalan como el framework de virtualización remota de GPUs más avanzado de la actualidad. Los resutados obtenidos en esta tesis muestran que el uso de rCUDA en entornos de Cloud Computing incrementa el grado de libertad del sistema, ya que permite crear instancias virtuales de las GPUs físicas totalmente a medida de las necesidades de cada una de las máquinas virtuales. En entornos HPC (High Performance Computing; Computación de Altas Prestaciones), rCUDA también proporciona un mayor grado de flexibilidad de uso de las GPUs de todo el clúster de cómputo, ya que permite desacoplar totalmente la parte CPU de la parte GPU de las aplicaciones. Además, las GPUs pueden estar en cualquier nodo del clúster, independientemente del nodo en el que se está ejecutando la parte CPU de la aplicación. En general, tanto para Cloud Computing como en el caso de HPC, este mayor grado de flexibilidad se traduce en un aumento hasta 2x de la productividad de todo el sistema al mismo tiempo que se reduce el consumo energético en un 15%. Finalmente, también hemos desarrollado un mecanismo de migración de trabajos de la parte GPU de las aplicaciones que ha sido integrado dentro del framework rCUDA. Este mecanismo de migración ha sido evaluado y los resultados muestran claramente que, a cambio de una pequeña sobrecarga, alrededor de 400 milisegundos, en el tiempo de ejecución de las aplicaciones, es una potente herramienta con la que, de nuevo, aumentar la productividad y reducir el gasto energético del sistema. En resumen, en esta tesis se analizan los principales problemas derivados del uso de las GPUs como aceleradores de cómputo, tanto en entornos HPC como de Cloud Computing, y se demuestra cómo a través del uso del framework rCUDA, estos problemas pueden solucionarse. Además se desarrolla un potente mecanismo de migración de trabajos GPU, que integrado dentro del framework rCUDA, se convierte en una herramienta clave para los futuros planificadores de trabajos en clusters heterogéneos. / [CA] En l'última dècada la utilització de la GPGPU(General Purpose computing in Graphics Processing Units; Computació de Propòsit General en Unitats de Processament Gràfic) s'ha tornat extremadament popular en els centres de dades de tot el món. Les GPUs (Graphics Processing Units; Unitats de Processament Gràfic) s'han establert com a elements acceleradors de còmput que s'utilitzen al costat de les CPUs formant sistemes heterogenis. La naturalesa massivament paral·lela de les GPUs, destinades tradicionalment al còmput de gràfics, permet realitzar operacions numèriques amb matrius de dades a gran velocitat degut al gran nombre de nuclis que integren i al gran ample de banda d'accés a memòria que posseeixen. En conseqüència, les aplicacions de tot tipus de camps, com ara química, física, enginyeria, intel·ligència artificial, ciència de materials, etc. que presenten aquest tipus de patrons de còmput es veuen beneficiades reduint dràsticament el seu temps d'execució. En general, l'ús de l'acceleració del còmput en GPUs ha significat un pas endavant i una revolució, però no està exempt de problemes, com ara poden ser problemes d'eficiència energètica, baixa utilització de les GPUs, alts costos d'adquisició i manteniment, etc. En aquesta tesi pretenem analitzar les principals mancances que presenten aquests sistemes heterogenis i proposar solucions basades en l'ús de la virtualització remota de GPUs. Per a això hem utilitzat l'eina rCUDA, desenvolupada a la Universitat Politècnica de València, ja que multitud de publicacions l'avalen com el framework de virtualització remota de GPUs més avançat de l'actualitat. Els resultats obtinguts en aquesta tesi mostren que l'ús de rCUDA en entorns de Cloud Computing incrementa el grau de llibertat del sistema, ja que permet crear instàncies virtuals de les GPUs físiques totalment a mida de les necessitats de cadascuna de les màquines virtuals. En entorns HPC (High Performance Computing; Computació d'Altes Prestacions), rCUDA també proporciona un major grau de flexibilitat en l'ús de les GPUs de tot el clúster de còmput, ja que permet desacoblar totalment la part CPU de la part GPU de les aplicacions. A més, les GPUs poden estar en qualsevol node del clúster, sense importar el node en el qual s'està executant la part CPU de l'aplicació. En general, tant per a Cloud Computing com en el cas del HPC, aquest major grau de flexibilitat es tradueix en un augment fins 2x de la productivitat de tot el sistema al mateix temps que es redueix el consum energètic en aproximadament un 15%. Finalment, també hem desenvolupat un mecanisme de migració de treballs de la part GPU de les aplicacions que ha estat integrat dins del framework rCUDA. Aquest mecanisme de migració ha estat avaluat i els resultats mostren clarament que, a canvi d'una petita sobrecàrrega, al voltant de 400 mil·lisegons, en el temps d'execució de les aplicacions, és una potent eina amb la qual, de nou, augmentar la productivitat i reduir la despesa energètica de sistema. En resum, en aquesta tesi s'analitzen els principals problemes derivats de l'ús de les GPUs com acceleradors de còmput, tant en entorns HPC com de Cloud Computing, i es demostra com a través de l'ús del framework rCUDA, aquests problemes poden solucionar-se. A més es desenvolupa un potent mecanisme de migració de treballs GPU, que integrat dins del framework rCUDA, esdevé una eina clau per als futurs planificadors de treballs en clústers heterogenis. / [EN] In the last decade the use of GPGPU (General Purpose computing in Graphics Processing Units) has become extremely popular in data centers around the world. GPUs (Graphics Processing Units) have been established as computational accelerators that are used alongside CPUs to form heterogeneous systems. The massively parallel nature of GPUs, traditionally intended for graphics computing, allows to perform numerical operations with data arrays at high speed. This is achieved thanks to the large number of cores GPUs integrate and the large bandwidth of memory access. Consequently, applications of all kinds of fields, such as chemistry, physics, engineering, artificial intelligence, materials science, and so on, presenting this type of computational patterns are benefited by drastically reducing their execution time. In general, the use of computing acceleration provided by GPUs has meant a step forward and a revolution, but it is not without problems, such as energy efficiency problems, low utilization of GPUs, high acquisition and maintenance costs, etc. In this PhD thesis we aim to analyze the main shortcomings of these heterogeneous systems and propose solutions based on the use of remote GPU virtualization. To that end, we have used the rCUDA middleware, developed at Universitat Politècnica de València. Many publications support rCUDA as the most advanced remote GPU virtualization framework nowadays. The results obtained in this PhD thesis show that the use of rCUDA in Cloud Computing environments increases the degree of freedom of the system, as it allows to create virtual instances of the physical GPUs fully tailored to the needs of each of the virtual machines. In HPC (High Performance Computing) environments, rCUDA also provides a greater degree of flexibility in the use of GPUs throughout the computing cluster, as it allows the CPU part to be completely decoupled from the GPU part of the applications. In addition, GPUs can be on any node in the cluster, regardless of the node on which the CPU part of the application is running. In general, both for Cloud Computing and in the case of HPC, this greater degree of flexibility translates into an up to 2x increase in system-wide throughput while reducing energy consumption by approximately 15%. Finally, we have also developed a job migration mechanism for the GPU part of applications that has been integrated within the rCUDA middleware. This migration mechanism has been evaluated and the results clearly show that, in exchange for a small overhead of about 400 milliseconds in the execution time of the applications, it is a powerful tool with which, again, we can increase productivity and reduce energy foot print of the computing system. In summary, this PhD thesis analyzes the main problems arising from the use of GPUs as computing accelerators, both in HPC and Cloud Computing environments, and demonstrates how thanks to the use of the rCUDA middleware these problems can be addressed. In addition, a powerful GPU job migration mechanism is being developed, which, integrated within the rCUDA framework, becomes a key tool for future job schedulers in heterogeneous clusters. / This work jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grants (20524/PDC/18, 20813/PI/18 and 20988/PI/18) and by the Spanish MEC and European Commission FEDER under grants TIN2015-66972-C5-3-R, TIN2016-78799-P and CTQ2017-87974-R (AEI/FEDER, UE). We also thank NVIDIA for hardware donation under GPU Educational Center 2014-2016 and Research Center 2015-2016. The authors thankfully acknowledge the computer resources at CTE-POWER and the technical support provided by Barcelona Supercomputing Center - Centro Nacional de Supercomputación (RES-BCV-2018-3-0008). Furthermore, researchers from Universitat Politècnica de València are supported by the Generalitat Valenciana under Grant PROMETEO/2017/077. Authors are also grateful for the generous support provided by Mellanox Technologies Inc. Prof. Pradipta Purkayastha, from Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, is acknowledged for kindly providing the initial ligand and DNA structures. / Prades Gasulla, J. (2021). Improving Performance and Energy Efficiency of Heterogeneous Systems with rCUDA [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/168081 / Compendio

<p>Examensarbetet handlar om en jämförelsestudie av beräkningseffektivitet med avseende på energi- och tidsförbrukning mellan grafikkort och processorer i persondatorer och PlayStation 3.</p><p>Problemet studeras för att göra allmänheten uppmärksam på att det går att lösa en del av energiproblematiken med beräkningar genom att öka energieffektiviteten av beräkningsenheterna.</p><p>Undersökningen har genomförts på ett explorativt sätt och studerar förhållandet mellan processorer, grafikkort och vilken som presterar bäst i vilket sammanhang. Prestandatest genomförs med molekylberäkningsprogrammet F@H och med filkomprimeringsprogrammet WinRAR. Testerna utförs på MultiCore- och SingleCorePCs och PS3s av olika karaktär. I vissa test mäts effektförbrukning för att kunna räkna ut hur energieffektiva vissa system är.</p><p>Resultatet visar tydligt hur den genomsnittliga effektförbrukningen och energieffektiviteten för olika testsystem skiljer sig vid belastning, viloläge och olika typer beräkningar.</p> / <p>The thesis is a comparative study of computational efficiency in terms of energy and time consumption of graphics cards and processors in personal computers and Playstation3’s.</p><p>The problem is studied in order to make the public aware that it is possible to solve some of the energy problems with computations by increasing energy efficiency of the computational units.</p><p>The audit was conducted in an exploratory way, studying the relationship between the processors, graphics cards and which one performs best in which context. Performance tests are carried out by the molecule calculating F@H-program and the file compression program WinRAR. Tests performed on MultiCore and SingleCore PC’s and PS3’s with different characteristics. In some tests power consumption is measured in order to figure out how energy-efficient certain systems are.</p><p>The results clearly show how the average power consumption and energy efficiency for various test systems at differ at load, sleep and various calculations.</p><p> </p>

Nous présentons dans cette thèse les évolutions apportées au solveur Galerkin Discontinu Teta-CLAC, issu de la collaboration IRMA-AxesSim, au cours du projet HOROCH (2015-2018). Ce solveur permet de résoudre les équations de Maxwell en 3D, en parallèle sur un grand nombre d'accélérateurs OpenCL. L'objectif du projet HOROCH était d'effectuer des simulations de grande envergure sur un modèle numérique complet de corps humain. Ce modèle comporte 24 millions de mailles hexaédriques pour des calculs dans la bande de fréquences des objets connectés allant de 1 à 3 GHz (Bluetooth). Les applications sont nombreuses : téléphonie et accessoires, sport (maillots connectés), médecine (sondes : gélules, patchs), etc. Les évolutions ainsi apportées comprennent, entre autres : l'optimisation des kernels OpenCL à destination des CPU dans le but d'utiliser au mieux les architectures hybrides ; l'expérimentation du runtime StarPU ; le design d'un schéma d'intégration à pas de temps local ; et bon nombre d'optimisations permettant au solveur de traiter des simulations de plusieurs millions de mailles. / In this thesis, we present the evolutions made to the Discontinuous Galerkin solver Teta-CLAC – resulting from the IRMA-AxesSim collaboration – during the HOROCH project (2015-2018). This solver allows to solve the Maxwell equations in 3D and in parallel on a large amount of OpenCL accelerators. The goal of the HOROCH project was to perform large-scale simulations on a complete digital human body model. This model is composed of 24 million hexahedral cells in order to perform calculations in the frequency band of connected objects going from 1 to 3 GHz (Bluetooth). The applications are numerous: telephony and accessories, sport (connected shirts), medicine (probes: capsules, patches), etc. The changes thus made include, among others: optimization of OpenCL kernels for CPUs in order to make the best use of hybrid architectures; StarPU runtime experimentation; the design of an integration scheme using local time steps; and many optimizations allowing the solver to process simulations of several millions of cells.

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.

Made available in DSpace on 2014-12-17T15:47:58Z (GMT). No. of bitstreams: 1 ItaloMSR_DISSERT.pdf: 4133389 bytes, checksum: 575496a3d8aa350df8e3e86992d9b27b (MD5) Previous issue date: 2011-03-02 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The vascular segmentation is important in diagnosing vascular diseases like stroke and is hampered by noise in the image and very thin vessels that can pass unnoticed. One way to accomplish the segmentation is extracting the centerline of the vessel with height ridges, which uses the intensity as features for segmentation. This process can take from seconds to minutes, depending on the current technology employed. In order to accelerate the segmentation method proposed by Aylward [Aylward & Bullitt 2002] we have adapted it to run in parallel using CUDA architecture. The performance of the segmentation method running on GPU is compared to both the same method running on CPU and the original Aylward s method running also in CPU. The improvemente of the new method over the original one is twofold: the starting point for the segmentation process is not a single point in the blood vessel but a volume, thereby making it easier for the user to segment a region of interest, and; the overall gain method was 873 times faster running on GPU and 150 times more fast running on the CPU than the original CPU in Aylward / A segmenta??o vascular ? importante no diagn?stico de doen?as como o acidente vascular cerebral e ? dificultada por ru?dos na imagem e vasos muito finos que n?o s?o vistos. Uma maneira de realizar a segmenta??o ? extraindo a centerline do vaso com height ridges, que usa a intensidade como caracter?sticas para a segmenta??o. Este processo pode levar de segundos a minutos, dependendo da tecnologia atual empregada. O m?todo ? implementado em GPU, ou seja, ? executado de maneira paralela em placa gr?fica. O desempenho do m?todo de segmenta??o executado em GPU ? comparado com o mesmo m?todo em CPU e o m?todo original de Aylward em execu??o tamb?m na CPU. O melhoramento do novo m?todo sobre o original ? dupla. O ponto de partida para o processo de segmenta??o n?o ? um ?nico ponto no vaso sangu?neo, mas um volume, tornando assim mais f?cil para o usu?rio a sele??o de uma regi?o de interesse, e, o ganho do m?todo proposto foi 873 vezes mais r?pido sendo executado em GPU e 150 vezes mais r?pido sendo executado em CPU do que o original de Aylward em CPU