In today’s research and market, IT applications for health-care are gaining huge interest of both IT and medical researchers. Cardiovascular diseases (CVDs) are considered the largest cause of death for both men and women regardless of ethnic backgrounds. More efficient treatments and most importantly efficient methods of cardiac diagnosis that examine heart diseases are desired. Electrocardiography (ECG) is an essential method used to diagnose heart diseases. However, diagnosing any cardiovascular disease based on the 12-lead ECG printout from an ECG machine using human eye might seriously impair analysis accuracy. To meet this challenge of today’s ECG analysis methodology, a more reliable solution that can analyze huge amount of patient’s data in real-time is desired. The software solution presented in this article is aimed to reduce the risk while diagnosing cardiovascular diseases (CVDs) by human eye, computation of large-scale patient’s data in real-time at the patient’s location and sending the required results or summary to the doctor/nurse. Keeping in mind the importance of real-time analysis of patient’s data, the software system has built upon small individual algorithms/modules designed for multi-core architecture, where each module is supposed to be processed by an individual core/processor in parallel. All the input and output processes to the analysis system are made automated, which reduces operator’s interaction to the system and thus reducing the cost. The outputs/results of the processing are summarized to smaller files in both ASCII and binary formats to meet the requirement of exchanging the data over Voice and Data Networks.

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Frequent-itemset mining is an essential part of the association rule mining process, which has many application areas. It is a computation and memory intensive task with many opportunities for optimization. Many efficient sequential and parallel algorithms were proposed in the recent years. Most of the parallel algorithms, however, cannot cope with the huge number of threads that are provided by large multiprocessor or many-core systems. In this paper, we provide a highly parallel version of the well-known Eclat algorithm. It runs on both, multiprocessor systems and many-core coprocessors, and scales well up to a very large number of threads---244 in our experiments. To evaluate mcEclat's performance, we conducted many experiments on realistic datasets. mcEclat achieves high speedups of up to 11.5x and 100x on a 12-core multiprocessor system and a 61-core Xeon Phi many-core coprocessor, respectively. Furthermore, mcEclat is competitive with highly optimized existing frequent-itemset mining implementations taken from the FIMI repository.

Cette thèse propose un large éventail d'approches afin d'améliorer différents aspects de l'analyse des flux de puissance avec comme fils conducteur l'utilisation du processeurs graphiques (GPU). Si les GPU ont rapidement prouvés leurs efficacités sur des applications régulières pour lesquelles le parallélisme de données était facilement exploitable, il en est tout autrement pour les applications dites irrégulières. Ceci est précisément le cas de la plupart des algorithmes d'analyse de flux de puissance. Pour ce travail, nous nous inscrivons dans cette problématique d'optimisation de l'analyse de flux de puissance à l'aide de coprocesseur de type GPU. L'intérêt est double. Il étend le domaine d'application des GPU à une nouvelle classe de problème et/ou d'algorithme en proposant des solutions originales. Il permet aussi à l'analyse des flux de puissance de rester pertinent dans un contexte de changements continus dans les systèmes énergétiques, et ainsi d'en faciliter leur évolution. Nos principales contributions liées à la programmation sur GPU sont: (i) l'analyse des différentes méthodes de parcours d'arbre pour apporter une réponse au problème de la régularité par rapport à l'équilibrage de charge ; (ii) l'analyse de l'impact du format de représentation sur la performance des implémentations d'arithmétique floue. Nos contributions à l'analyse des flux de puissance sont les suivantes: (ii) une nouvelle méthode pour l'évaluation de l'incertitude dans l'analyse des flux de puissance ; (ii) une nouvelle méthode de point fixe pour l'analyse des flux de puissance, problème que l'on qualifie d'intrinsèquement parallèle. / This thesis addresses the utilization of Graphics Processing Units (GPUs) for improving the Power Flow (PF) analysis of modern power systems. Currently, GPUs are challenged by applications exhibiting an irregular computational pattern, as is the case of most known methods for PF analysis. At the same time, the PF analysis needs to be improved in order to cope with new requirements of efficiency and accuracy coming from the Smart Grid concept. The relevance of GPU-enhanced PF analysis is twofold. On one hand, it expands the application domain of GPU to a new class of problems. On the other hand, it consistently increases the computational capacity available for power system operation and design. The present work attempts to achieve that in two complementary ways: (i) by developing novel GPU programming strategies for available PF algorithms, and (ii) by proposing novel PF analysis methods that can exploit the numerous features present in GPU architectures. Specific contributions on GPU computing include: (i) a comparison of two programming paradigms, namely regularity and load-balancing, for implementing the so-called treefix operations; (ii) a study of the impact of the representation format over performance and accuracy, for fuzzy interval algebraic operations; and (iii) the utilization of architecture-specific design, as a novel strategy to improve performance scalability of applications. Contributions on PF analysis include: (i) the design and evaluation of a novel method for the uncertainty assessment, based on the fuzzy interval approach; and (ii) the development of an intrinsically parallel method for PF analysis, which is not affected by the Amdahl's law.

Esta dissertação de mestrado aborda formulações computacionais e algoritmos para a busca e extração de padrões em cadeias biológicas. Em particular, o presente texto concentra-se nos dois problemas a seguir, considerando-os sob as distâncias de Hamming e Levenshtein: a) como determinar os locais nos quais um dado padrão ocorre de modo aproximado em uma cadeia fornecida; b) como extrair padrões que ocorram de modo aproximado em um número significativo de cadeias de um conjunto fornecido. O primeiro problema, para o qual já existem diversos algoritmos polinomiais, tem recebido muita atenção desde a década de 60, e ganhou novos ares com o advento da biologia computacional, nos idos dos anos 80, e com a popularização da Internet e seus mecanismos de busca: ambos os fenômenos trouxeram novos obstáculos a serem superados, em razão do grande volume de dados e das bastante justas restrições de tempo inerentes a essas aplicações. O segundo problema, de surgimento um pouco mais recente, é intrinsicamente desafiador, em razão de sua complexidade computacional, do tamanho das entradas tratadas nas aplicações mais comuns e de sua dificuldade de aproximação. Também é de chamar a atenção o seu grande potencial de aplicação. Neste trabalho são apresentadas formulações adequadas dos problemas abordados, assim como algoritmos e estruturas de dados essenciais ao seu estudo. Em especial, estudamos a extremamente versátil árvore dos sufixos, assim como uma de suas generalizações e sua estrutura irmã: o vetor dos sufixos. Grande parte do texto é dedicada aos filtros baseados em q-gramas para a busca aproximada de padrões e algumas de suas mais recentes variações. Estão cobertos os algoritmos bit-paralelos de Myers e Baeza-Yates-Gonnet para a busca de padrões; os algoritmos de Sagot para a extração de padrões; os algoritmos de filtragem de Ukkonen, Jokinen-Ukkonen, Burkhardt-Kärkkäinen, entre outros. / This thesis deals with computational formulations and algorithms for the extraction and search of patterns from biological strings. In particular, the present text focuses on the following problems, both considered under Hamming and Levenshtein distances: 1. How to find the positions where a given pattern approximatelly occurs in a given string; 2. How to extract patterns which approximatelly occurs in a certain number of strings from a given set. The first problem, for which there are many polinomial time algorithms, has been receiving a lot of attention since the 60s and entered a new era of discoveries with the advent of computational biology, in the 80s, and the widespread of the Internet and its search engines: both events brought new challenges to be faced by virtue of the large volume of data usually held by such applications and its time constraints. The second problem, much younger, is very challenging due to its computational complexity, approximation hardness and the size of the input data usually held by the most common applications. This problem is also very interesting due to its potential of application. In this work we show computational formulations, algorithms and data structures for those problems. We cover the bit-parallel algorithms of Myers, Baeza-Yates-Gonnet and the Sagots algorithms for patterns extraction. We also cover here the oustanding versatile suffix tree, its generalised version, and a similar data structure: the suffix array. A significant part of the present work focuses on q-gram based filters designed to solve the approximate pattern search problem. More precisely, we cover the filter algorithms of Ukkonen, Jokinen-Ukkonen and Burkhardt-Kärkkäinen, among others.

Cette thèse se concentre sur la résolution de systèmes linéaires creux dans le contexte d’applications massivement parallèles. Ce type de problèmes s’exprime sous la forme AX=B, où A est une matrice creuse d’ordre n x n, i.e. qui possède un nombre d’entrées nulles suffisamment élevé pour pouvoir être exploité, et B et X sont respectivement la matrice de seconds membres et la matrice de solution de taille n x nrhs. Cette résolution par des méthodes dites directes est effectuée grâce à une étape de factorisation qui réduit A en deux matrices triangulaires inférieure et supérieure L et U, suivie de deux résolutions triangulaires pour calculer la solution.Nous nous intéressons à ces résolutions avec une attention particulière apportée à la première, LY=B. Dans beaucoup d’applications, B possède un grand nombre de colonnes (nrhs >> 1) transformant la phase de résolution en un goulot d’étranglement. Elle possède souvent aussi une structure creuse, donnant l’opportunité de réduire la complexité de cette étape.Cette étude aborde sous des angles complémentaires la résolution triangulaire de systèmes linéaires avec seconds membres multiples et creux. Nous étudions dans un premier temps la complexité asymptotique de cette étape dans différents contextes (2D, 3D, facteurs compressés ou non). Nous considérons ensuite l’exploitation de cette structure et présentons de nouvelles approches s’appuyant sur une modélisation du problème par des graphes qui permettent d’atteindre efficacement le nombre minimal d’opérations. Enfin, nous donnons une interprétation concrète de son exploitation sur une application d’électromagnétisme pour la géophysique. Nous adaptons aussi des algorithmes parallèles aux spécificités de la phase de résolution.Nous concluons en combinant l'ensemble des résultats précédents et en discutant des perspectives de ce travail. / We consider direct methods to solve sparse linear systems AX = B, where A is a sparse matrix of size n x n with a symmetric structure and X and B are respectively the solution and right-hand side matrices of size n x nrhs. A is usually factorized and decomposed in the form LU, where L and U are respectively a lower and an upper triangular matrix. Then, the solve phase is applied through two triangular resolutions, named respectively the forward and backward substitutions.For some applications, the very large number of right-hand sides (RHS) in B, nrhs >> 1, makes the solve phase the computational bottleneck. However, B is often sparse and its structure exhibits specific characteristics that may be efficiently exploited to reduce this cost. We propose in this thesis to study the impact of the exploitation of this structural sparsity during the solve phase going through its theoretical aspects down to its actual implications on real-life applications.First, we investigate the asymptotic complexity, in the big-O sense, of the forward substitution when exploiting the RHS sparsity in order to assess its efficiency when increasing the problem size. In particular, we study on 2D and 3D regular problems the asymptotic complexity both for traditional full-rank unstructured solvers and for the case when low-rank approximation is exploited. Next, we extend state-of-the-art algorithms on the exploitation of RHS sparsity, and also propose an original approach converging toward the optimal number of operations while preserving performance. Finally, we show the impact of the exploitation of sparsity in a real-life electromagnetism application in geophysics that requires the solution of sparse systems of linear equations with a large number of sparse right-hand sides. We also adapt the parallel algorithms that were designed for the factorization to solve-oriented algorithms.We validate and combine the previous improvements using the parallel solver MUMPS, conclude on the contributions of this thesis and give some perspectives.

Made available in DSpace on 2016-12-23T14:33:32Z (GMT). No. of bitstreams: 1 Joao Olavo Baiao de Vasconcelos.pdf: 325868 bytes, checksum: 0459e6ca76a321095f4fc0d37ab23f21 (MD5) Previous issue date: 2011-12-21 / Petroleum Engineer activity is constantly enrolled on a series of optimization problems on many contexts, as, for instance, defining efficient and optimized projects on petroleum reserves development. However, there is an extreme difficulty on resolution of exploration and production (P&E) optimization problems, since they are often complex, with high degree of nonlinearity, presenting high uncertain number, and huge computational cost involved. Among them, there is the problem of determining the best throughput distribution among the wells of a petroleum production platform that achieves the biggest financial profitability of an E&P project, here named Petroleum Well Throughput Optimization Problem (PWTOP). In order to deal with PWTOP, some continuous optimization algorithms that deals with linearity restrictions present on the problem were studied, that are the Derivative Free Optimization (DFO), the Generating Set Search (GSS), and the Differential Evolution (DE). DFO is a sequential algorithm, whereas GSS and DE are parallel algorithms. Two case studies are also presented that represents synthetic petroleum fields. The results show how the studied algorithms behave on dealing with PWTOP for the two case studies, comparing experimental results obtained on optimized financial values, execution times and amount of objective function evaluation. Concludes, lastly, that, for the simplest case study, GSS had the best result, and for the most complex case study, more like real reservoirs, DE stood out / A atividade de Engenharia de Petróleo está rotineiramente envolvida em uma série de problemas de otimização em variados contextos, como definir projetos otimizados e eficientes na produção e no desenvolvimento de reservas de petróleo. Entretanto, há uma extrema dificuldade na resolução de problemas de otimização de exploração e produção (E&P), uma vez que são problemas frequentemente complexos, com elevado grau de não-linearidade, que apresentam alto número de incertezas e com enorme custo computacional envolvido. Dentre eles, está o problema de determinar a melhor distribuição de vazões entre os poços de uma plataforma de produção de petróleo capaz de resultar em um projeto de E&P de maior rentabilidade financeira, aqui denominado Problema de Otimização de Vazão de Poços de Petróleo (POVPP). Para tratar o POVPP, foram estudados alguns algoritmos de otimização contínua que possam lidar com as restrições lineares presentes no problema, que são o Otimização sem Derivadas (Derivative Free Optimization DFO), o Busca por Conjunto Gerador (Generating Set Search GSS) e o Evolução Diferencial (Differential Evolution DE). O DFO é um algoritmo sequencial, enquanto que o GSS e o DE são algoritmos paralelos. Também são apresentados dois estudos de caso que representam campos de petróleo sintéticos. Os resultados mostram como os algoritmos estudados se comportam ao tratar o POVPP para os dois estudos de caso, comparando-se dados obtidos de valores financeiros otimizados, tempos de execução e quantidade de avaliações da função objetivo. Conclui-se, por fim, que, para o estudo de caso simples, o GSS teve o melhor resultado, e para o estudo de caso mais complexo, mais semelhante a reservatórios reais, o DE se sobressaiu

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Computer Science, 2009. / Includes bibliographical references.

Esta dissertação de mestrado aborda formulações computacionais e algoritmos para a busca e extração de padrões em cadeias biológicas. Em particular, o presente texto concentra-se nos dois problemas a seguir, considerando-os sob as distâncias de Hamming e Levenshtein: a) como determinar os locais nos quais um dado padrão ocorre de modo aproximado em uma cadeia fornecida; b) como extrair padrões que ocorram de modo aproximado em um número significativo de cadeias de um conjunto fornecido. O primeiro problema, para o qual já existem diversos algoritmos polinomiais, tem recebido muita atenção desde a década de 60, e ganhou novos ares com o advento da biologia computacional, nos idos dos anos 80, e com a popularização da Internet e seus mecanismos de busca: ambos os fenômenos trouxeram novos obstáculos a serem superados, em razão do grande volume de dados e das bastante justas restrições de tempo inerentes a essas aplicações. O segundo problema, de surgimento um pouco mais recente, é intrinsicamente desafiador, em razão de sua complexidade computacional, do tamanho das entradas tratadas nas aplicações mais comuns e de sua dificuldade de aproximação. Também é de chamar a atenção o seu grande potencial de aplicação. Neste trabalho são apresentadas formulações adequadas dos problemas abordados, assim como algoritmos e estruturas de dados essenciais ao seu estudo. Em especial, estudamos a extremamente versátil árvore dos sufixos, assim como uma de suas generalizações e sua estrutura irmã: o vetor dos sufixos. Grande parte do texto é dedicada aos filtros baseados em q-gramas para a busca aproximada de padrões e algumas de suas mais recentes variações. Estão cobertos os algoritmos bit-paralelos de Myers e Baeza-Yates-Gonnet para a busca de padrões; os algoritmos de Sagot para a extração de padrões; os algoritmos de filtragem de Ukkonen, Jokinen-Ukkonen, Burkhardt-Kärkkäinen, entre outros. / This thesis deals with computational formulations and algorithms for the extraction and search of patterns from biological strings. In particular, the present text focuses on the following problems, both considered under Hamming and Levenshtein distances: 1. How to find the positions where a given pattern approximatelly occurs in a given string; 2. How to extract patterns which approximatelly occurs in a certain number of strings from a given set. The first problem, for which there are many polinomial time algorithms, has been receiving a lot of attention since the 60s and entered a new era of discoveries with the advent of computational biology, in the 80s, and the widespread of the Internet and its search engines: both events brought new challenges to be faced by virtue of the large volume of data usually held by such applications and its time constraints. The second problem, much younger, is very challenging due to its computational complexity, approximation hardness and the size of the input data usually held by the most common applications. This problem is also very interesting due to its potential of application. In this work we show computational formulations, algorithms and data structures for those problems. We cover the bit-parallel algorithms of Myers, Baeza-Yates-Gonnet and the Sagots algorithms for patterns extraction. We also cover here the oustanding versatile suffix tree, its generalised version, and a similar data structure: the suffix array. A significant part of the present work focuses on q-gram based filters designed to solve the approximate pattern search problem. More precisely, we cover the filter algorithms of Ukkonen, Jokinen-Ukkonen and Burkhardt-Kärkkäinen, among others.

In this paper we introduce a new class library to model transformations of parallel algorithms. SYMPAD serves as a basis to develop automated tools and methods to generate efficient implementations of such algorithms. The paper gives an overview over the general structure, as well as features of the library. We further describe the fundamental design process that is controlled by our developed methods.