In this thesis, in-situ instantaneous cutting rates of boom type, medium-weight milling type roadheaders (Mk-2B) at &Ccedil / ayirhan Coal Mine are determined by studying previous performance tests and carrying out additional underground cutting tests. Some rock properties such as uniaxial compressive strength, tensile strength, Cone Indenter hardness, Shore hardness, Schmidt hammer rebound hardness and laboratory cutting specific energies are determined by laboratory tests for the rock and coal types encountered in the drivage of roadways. The relations between the instantaneous cutting rates and the above rock characteristics and the laboratory cutting specific energies are established. The results show that instantaneous cutting rates can be best predicted using laboratory cutting specific energy which provides the highest correlation (R2 = 0.8411) as compared to other rock properties. The model developed for the medium-weight machine to predict instantaneous cutting rate is compared with those developed earlier for the light-weight and heavy-weight machines. It is determined that improvements in cutting performance with the medium-weight machines as compared to light-weight machines is achieved for the rocks requiring laboratory cutting specific energy greater than 5 MJ/m3.

This dissertation investigates the dynamics that underlie enterprise performance and takes a significant step toward showing how it might be predicted. In a novel approach, a comprehensive Enterprise System Architecture (ESA) is developed that introduces separate layers for strategic and operational processes, respectfully. We identify four broad dimensions that contribute to and influence enterprise performance: (1) enterprise processes, (2) technology-based support of enterprise processes [denoted information systems], (3) technology structure and deployment [denoted information technology], and (4) Enterprise Architecture (EA). Detailed interviews were conducted with ten executives, mostly from the aerospace and defense industry, along with a web-based survey of aerospace and defense industry executives. We empirically determine a value for each dimension of maturity and individually assess it as a predictor of enterprise performance. ESA maturity is calculated as the weighted summation of each of dimensional maturity, and is also evaluated as a predictor of enterprise performance. Results indicate that ESA maturity, the weighted summation of process maturity, information systems maturity, information technology maturity, and enterprise architecture maturity, is a good predictor of enterprise performance. In order to provide some practical utility to our empirical results, we outline an ESA maturity assessment framework to enable decision-makers to assess the overall maturity of an enterprise system. Two other extensions of our research results, the development of a strategic layer analysis / portrayal tool, and enterprise system simulation, are also briefly described.

A complexidade dos dispositivos embarcados propõe novos desafios para o desenvolvimento de software embarcado, além das tradicionais restrições físicas. Então, a avaliação da qualidade do software embarcado e seu impacto nessas propriedades tradicionais torna-se mais importante. Conceitos como reúso abstração, coesão, acoplamento, entre outros atributos de software têm sido usados como métricas de qualidade no domínio da engenharia de software. No entanto, elas não têm sido usadas no domínio do software embarcado. No desenvolvimento de sistemas embarcados outro conjunto de ferramentas é usado para estimar as propriedades físicas, tais como: consumo de energia, ocupação de memória e desempenho. Essas ferramentas geralmente envolvem custosos processos de síntese e simulação. Nos complexos dispositivos embarcados atuais deve-se confiar em ferramentas que possam ajudar na exploração do espaço de projeto ainda nos níveis mais altos de abstração, identificando a solução que representa a melhor estratégia de projeto em termos da qualidade de software, enquanto, simultaneamente, atenda aos requisitos físicos. Neste trabalho é apresentada uma análise da correlação entre métricas de qualidade de software, que podem ser extraídas antes do sistema ser sintetizado, e as métricas físicas do software embarcado. Usando uma rede neural nós investigamos o uso dessas correlações para predizer o impacto que uma determinada modificação no software trará às métricas físicas do mesmo software. Esta estimativa pode ser usada para guiar decisões em direção a melhoria das propriedades físicas dos sistemas embarcados, além de manter um equilíbrio em relação às métricas de software. / The complexity of embedded devices poses new challenges to embedded software development in addition to the traditional physical requirements. Therefore, the evaluation of the quality of embedded software and its impact on these traditional properties becomes increasingly relevant. Concepts such as reuse, abstraction, cohesion, coupling, and other software attributes have been used as quality metrics in the software engineering domain. However, they have not been used in the embedded software domain. In embedded systems development, another set of tools is used to estimate physical properties such as power consumption, memory footprint, and performance. These tools usually require costly synthesis-and-simulation design cycles. In current complex embedded devices, one must rely on tools that can help design space exploration at the highest possible level, identifying a solution that represents the best design strategy in terms of software quality, while simultaneously meeting physical requirements. We present an analysis of the cross-correlation between software quality metrics, which can be extracted before the final system is synthesized, and physical metrics for embedded software. Using a neural network, we investigate the use of these cross-correlations to predict the impact that a given modification on the software solution will have on embedded software physical metrics. This estimation can be used to guide design decisions towards improving physical properties of embedded systems, while maintaining an adequate trade-off regarding software quality.

A complexidade dos dispositivos embarcados propõe novos desafios para o desenvolvimento de software embarcado, além das tradicionais restrições físicas. Então, a avaliação da qualidade do software embarcado e seu impacto nessas propriedades tradicionais torna-se mais importante. Conceitos como reúso abstração, coesão, acoplamento, entre outros atributos de software têm sido usados como métricas de qualidade no domínio da engenharia de software. No entanto, elas não têm sido usadas no domínio do software embarcado. No desenvolvimento de sistemas embarcados outro conjunto de ferramentas é usado para estimar as propriedades físicas, tais como: consumo de energia, ocupação de memória e desempenho. Essas ferramentas geralmente envolvem custosos processos de síntese e simulação. Nos complexos dispositivos embarcados atuais deve-se confiar em ferramentas que possam ajudar na exploração do espaço de projeto ainda nos níveis mais altos de abstração, identificando a solução que representa a melhor estratégia de projeto em termos da qualidade de software, enquanto, simultaneamente, atenda aos requisitos físicos. Neste trabalho é apresentada uma análise da correlação entre métricas de qualidade de software, que podem ser extraídas antes do sistema ser sintetizado, e as métricas físicas do software embarcado. Usando uma rede neural nós investigamos o uso dessas correlações para predizer o impacto que uma determinada modificação no software trará às métricas físicas do mesmo software. Esta estimativa pode ser usada para guiar decisões em direção a melhoria das propriedades físicas dos sistemas embarcados, além de manter um equilíbrio em relação às métricas de software. / The complexity of embedded devices poses new challenges to embedded software development in addition to the traditional physical requirements. Therefore, the evaluation of the quality of embedded software and its impact on these traditional properties becomes increasingly relevant. Concepts such as reuse, abstraction, cohesion, coupling, and other software attributes have been used as quality metrics in the software engineering domain. However, they have not been used in the embedded software domain. In embedded systems development, another set of tools is used to estimate physical properties such as power consumption, memory footprint, and performance. These tools usually require costly synthesis-and-simulation design cycles. In current complex embedded devices, one must rely on tools that can help design space exploration at the highest possible level, identifying a solution that represents the best design strategy in terms of software quality, while simultaneously meeting physical requirements. We present an analysis of the cross-correlation between software quality metrics, which can be extracted before the final system is synthesized, and physical metrics for embedded software. Using a neural network, we investigate the use of these cross-correlations to predict the impact that a given modification on the software solution will have on embedded software physical metrics. This estimation can be used to guide design decisions towards improving physical properties of embedded systems, while maintaining an adequate trade-off regarding software quality.

L'analyse et la compréhension du comportement d'applications parallèles sur des plates-formes de calcul variées est un problème récurent de la communauté du calcul scientifique. Lorsque les environnements d'exécution ne sont pas disponibles, la simulation devient une approche raisonnable pour obtenir des indicateurs de performance objectifs et pour explorer plusieurs scénarios ``what-if?''. Dans cette thèse, nous présentons un environnement pour la simulation off-line d'applications écrites avec MPI. La principale originalité de notre travail par rapport aux travaux précédents réside dans la définition de traces indépendantes du temps. Elles permettent d'obtenir une extensibilité maximale puisque des ressources hétérogènes et distribuées peuvent être utilisées pour obtenir une trace. Nous proposons un format dans lequel pour chaque événement qui apparaît durant l'exécution d'une application, nous récupérons les informations sur le volume d'instructions pour une phase de calcul ou le nombre d'octets et le type d'une communication. Pour obtenir des traces indépendantes du temps lors de l'exécution d'applications MPI, nous devons les instrumenter pour récupérer les données requises. Il existe plusieurs outils d'instrumentation qui peuvent instrumenter une application. Nous proposons un système de notation qui correspond aux besoins de notre environnement et nous évaluons les outils d'instrumentation selon lui. De plus, nous introduisons un outil original appelé Minimal Instrumentation qui a été conçu pour répondre au besoins de notre environnement. Nous étudions plusieurs méthodes d'instrumentation et plusieurs stratégies d'acquisition. Nous détaillons les outils qui extraient les traces indépendantes du temps à partir des traces d'instrumentations de quelques outils de profiling connus. Enfin nous évaluons la procédure d'acquisition complète et présentons l'acquisition d'instances à grande échelle. Nous décrivons en détail la procédure pour fournir un fichier de plateforme simulée réaliste à notre outil d'exécution de traces qui prend en compte la topologie de la plateforme cible ainsi que la procédure de calibrage par rapport à l'application qui va être simulée. De plus, nous montrons que notre simulateur peut prédire les performances de certains benchmarks MPI avec moins de 11% d'erreur relative entre l'exécution réelle et la simulation pour les cas où il n'y a pas de problème de performance. Enfin, nous identifions les causes de problèmes de performances et nous proposons des solutions pour y remédier. / Analyzing and understanding the performance behavior of parallel applicationson various compute infrastructures is a long-standing concern in the HighPerformance Computing community. When the targeted execution environments arenot available, simulation is a reasonable approach to obtain objectiveperformance indicators and explore various ``what-if?'' scenarios. In thiswork we present a framework for the off-line simulation of MPIapplications. The main originality of our work with regard to the literature is to rely on\tit execution traces. This allows for an extreme scalability as heterogeneousand distributed resources can be used to acquire a trace. We propose a formatwhere for each event that occurs during the execution of an application we logthe volume of instructions for a computation phase or the bytes and the type ofa communication. To acquire time-independent traces of the execution of MPI applications, wehave to instrument them to log the required data. There exist many profilingtools which can instrument an application. We propose a scoring system thatcorresponds to our framework specific requirements and evaluate the mostwell-known and open source profiling tools according to it. Furthermore weintroduce an original tool called Minimal Instrumentation that was designed tofulfill the requirements of our framework. We study different instrumentationmethods and we also investigate several acquisition strategies. We detail thetools that extract the \tit traces from the instrumentation traces of somewell-known profiling tools. Finally we evaluate the whole acquisition procedureand we present the acquisition of large scale instances. We describe in detail the procedure to provide a realistic simulated platformfile to our trace replay tool taking under consideration the topology of thereal platform and the calibration procedure with regard to the application thatis going to be simulated. Moreover we present the implemented trace replaytools that we used during this work. We show that our simulator can predictthe performance of some MPI benchmarks with less than 11\% relativeerror between the real execution and simulation for the cases that there is noperformance issue. Finally, we identify the reasons of the performance issuesand we propose solutions.

A complexidade dos dispositivos embarcados propõe novos desafios para o desenvolvimento de software embarcado, além das tradicionais restrições físicas. Então, a avaliação da qualidade do software embarcado e seu impacto nessas propriedades tradicionais torna-se mais importante. Conceitos como reúso abstração, coesão, acoplamento, entre outros atributos de software têm sido usados como métricas de qualidade no domínio da engenharia de software. No entanto, elas não têm sido usadas no domínio do software embarcado. No desenvolvimento de sistemas embarcados outro conjunto de ferramentas é usado para estimar as propriedades físicas, tais como: consumo de energia, ocupação de memória e desempenho. Essas ferramentas geralmente envolvem custosos processos de síntese e simulação. Nos complexos dispositivos embarcados atuais deve-se confiar em ferramentas que possam ajudar na exploração do espaço de projeto ainda nos níveis mais altos de abstração, identificando a solução que representa a melhor estratégia de projeto em termos da qualidade de software, enquanto, simultaneamente, atenda aos requisitos físicos. Neste trabalho é apresentada uma análise da correlação entre métricas de qualidade de software, que podem ser extraídas antes do sistema ser sintetizado, e as métricas físicas do software embarcado. Usando uma rede neural nós investigamos o uso dessas correlações para predizer o impacto que uma determinada modificação no software trará às métricas físicas do mesmo software. Esta estimativa pode ser usada para guiar decisões em direção a melhoria das propriedades físicas dos sistemas embarcados, além de manter um equilíbrio em relação às métricas de software. / The complexity of embedded devices poses new challenges to embedded software development in addition to the traditional physical requirements. Therefore, the evaluation of the quality of embedded software and its impact on these traditional properties becomes increasingly relevant. Concepts such as reuse, abstraction, cohesion, coupling, and other software attributes have been used as quality metrics in the software engineering domain. However, they have not been used in the embedded software domain. In embedded systems development, another set of tools is used to estimate physical properties such as power consumption, memory footprint, and performance. These tools usually require costly synthesis-and-simulation design cycles. In current complex embedded devices, one must rely on tools that can help design space exploration at the highest possible level, identifying a solution that represents the best design strategy in terms of software quality, while simultaneously meeting physical requirements. We present an analysis of the cross-correlation between software quality metrics, which can be extracted before the final system is synthesized, and physical metrics for embedded software. Using a neural network, we investigate the use of these cross-correlations to predict the impact that a given modification on the software solution will have on embedded software physical metrics. This estimation can be used to guide design decisions towards improving physical properties of embedded systems, while maintaining an adequate trade-off regarding software quality.

Orientador: Michel Eduardo Beleza Yamagishi / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Biologia / Made available in DSpace on 2018-08-15T17:21:19Z (GMT). No. of bitstreams: 1 Herai_RobertoHirochi_D.pdf: 3625854 bytes, checksum: 3f19d10a9b0bb7f77091197cd302f66e (MD5) Previous issue date: 2010 / Resumo: A grande quantidade de dados biológicos gerados recentemente permitiu verificar que os genomas são repletos de seqüências repetitivas (SR), como microsatélites e elementos genéticos móveis, altamente improváveis de ocorrer estatisticamente se os genomas fossem gerados a partir de uma distribuição aleatória de nucleotídeos. Tal comprovação motivou a classificação de tais seqüências e também a construção de diversas ferramentas de bioinformática, além de mecanismos de armazenamento baseados em sistemas de gerenciamento de bancos de dados (SGBD) para permitir localizá-las e armazená-las para posterior estudo. Entretanto, foi com a comprovação biológica da importância das SR, como no mecanismo de interferência por RNAi (SR reversa complementar), que as SR despertaram maior interesse por parte da comunidade científica. Atualmente, já há fortes evidências que associam as SR com fenômenos biológicos bastante interessantes, como o processamento de RNA por cis-splicing e a formação de transcritos quiméricos, freqüentes em organismos inferiores e muito raro em organismos superiores. Tais tipos de transcritos podem ser gerados a partir de trans-splicing ou, como conjecturamos nesse trabalho, pela transposição de elementos genéticos móveis (como por exemplo transposons ou retrotransposons). Em virtude disso, este projeto propõe a construção de metodologias de Bioinformática, disponibilizadas na WEB, para detectar transcritos quiméricos em genomas de organismos, tanto em versões draft ou em alta qualidade, e também estudar as SR que ocorrem no locus gênico dos transcritos envolvidos na formação de uma seqüência quimérica. As ferramentas propostas permitiram identificar, a partir de bibliotecas de transcritos de full-length cDNA, tanto de humanos quanto de bovinos, novos transcritos quiméricos provenientes de células de tecidos normais, e que não seguem splice-sites canônicos na região de fusão dos transcritos envolvidos. Além disso, as seqüências encontradas apresentam uma elevada taxa de concentração de pares de SR do tipo reverso complementar no locus gênico dos dois transcritos que formam a seqüência quimérica. As ferramentas propostas podem ser utilizadas para outros organismos e direcionar trabalhos experimentais para tentar comprovar em bancada novos transcritos quiméricos, tanto em organismos inferiores quanto em superiores / Abstract: The recent availability of a huge amount of biological data allowed to know about the high concentration of repetitive sequences (SR) like microsatellites and genetic mobile elements in different genomes. Repetitive sequences are improbable to occur statistically if genome data were generated by a random distribution of nucleotides. Such observation motivated the classification of repetitive sequences, and the construction of several bioinformatics tools. Furthermore, several mechanisms to store repetitive sequences, which are based on data base management systems (DBMS) were proposed and created. They can be used to search for specific sequences to make a posteriori study. However, it was with the biological confirmation of the importance of repetitive sequences, like by the RNA interference (reverse complement, or inverted repeat) mechanism, that the scientific community gained more interest by such sequences. Actually, there is strong evidence that associates the repetitive sequences with some interesting biological phenomena, like in RNA processing by cis-splicing, and in chimeric transcript formation mechanism. This last one is very frequently in inferior organism, but rare in superior organisms. Such types of transcripts can be generated by trans-splicing, or like conjectured in this work, by the retrotransposition of mobile genetic elements (like transposons or retrotransposons). In this way, this work proposed the construction of several Bioinformatics methodologies, available in the WEB, to detect new evidences of chimeric transcripts in genomes of different organisms, both in draft genome and in high quality genome assemblage. We also studied repetitive sequences in gene loci of the involved transcripts in a chimeric sequence formation. The proposed tools allowed us to identify, using a full-length cDNA databank, new chimeric transcript candidates in human and in bovine genome. They are from cells of normal tissues, and do not follow canonical splice-sites in the fusion region of the involved transcripts. Moreover, it was possible to show that the detected sequences have high concentration pairs of reverse complement type of repetitive sequences in gene loci of the two involved transcripts, which originated a new chimeric transcript candidate. The created bioinformatics tools can be used in other organisms in addition to the one used in this work, leading to the proposition of new experimental work to try to prove in vivo new chimeric transcripts, both in superior organism and in inferior organism / Doutorado / Bioinformatica / Doutor em Genetica e Biologia Molecular

The parallel and distributed platforms of High Performance Computing available today have became more and more heterogeneous (CPUs, GPUs, FPGAs, etc). Graphics Processing Units (GPU) are specialized co-processor to accelerate and improve the performance of parallel vector operations. GPUs have a high degree of parallelism and can execute thousands or millions of threads concurrently and hide the latency of the scheduler. GPUs have a deep hierarchical memory of different types as well as different configurations of these memories. Performance prediction of applications executed on these devices is a great challenge and is essential for the efficient use of resources in machines with these co-processors. There are different approaches for these predictions, such as analytical modeling and machine learning techniques. In this thesis, we present an analysis and characterization of the performance of applications executed on GPUs. We propose a simple and intuitive BSP-based model for predicting the CUDA application execution times on different GPUs. The model is based on the number of computations and memory accesses of the GPU, with additional information on cache usage obtained from profiling. We also compare three different Machine Learning (ML) approaches: Linear Regression, Support Vector Machines and Random Forests with BSP-based analytical model. This comparison is made in two contexts, first, data input or features for ML techniques were the same than analytical model, and, second, using a process of feature extraction, using correlation analysis and hierarchical clustering. We show that GPU applications that scale regularly can be predicted with simple analytical models, and an adjusting parameter. This parameter can be used to predict these applications in other GPUs. We also demonstrate that ML approaches provide reasonable predictions for different cases and ML techniques required no detailed knowledge of application code, hardware characteristics or explicit modeling. Consequently, whenever a large data set with information about similar applications are available or it can be created, ML techniques can be useful for deploying automated on-line performance prediction for scheduling applications on heterogeneous architectures with GPUs. / As plataformas paralelas e distribuídas de computação de alto desempenho disponíveis hoje se tornaram mais e mais heterogêneas (CPUs, GPUs, FPGAs, etc). As Unidades de processamento gráfico são co-processadores especializados para acelerar operações vetoriais em paralelo. As GPUs têm um alto grau de paralelismo e conseguem executar milhares ou milhões de threads concorrentemente e ocultar a latência do escalonador. Elas têm uma profunda hierarquia de memória de diferentes tipos e também uma profunda configuração da memória hierárquica. A predição de desempenho de aplicações executadas nesses dispositivos é um grande desafio e é essencial para o uso eficiente dos recursos computacionais de máquinas com esses co-processadores. Existem diferentes abordagens para fazer essa predição, como técnicas de modelagem analítica e aprendizado de máquina. Nesta tese, nós apresentamos uma análise e caracterização do desempenho de aplicações executadas em Unidades de Processamento Gráfico de propósito geral. Nós propomos um modelo simples e intuitivo fundamentado no modelo BSP para predizer a execução de funções kernels de CUDA sobre diferentes GPUs. O modelo está baseado no número de computações e acessos à memória da GPU, com informação adicional do uso das memórias cachês obtidas do processo de profiling. Nós também comparamos três diferentes enfoques de aprendizado de máquina (ML): Regressão Linear, Máquinas de Vetores de Suporte e Florestas Aleatórias com o nosso modelo analítico proposto. Esta comparação é feita em dois diferentes contextos, primeiro, dados de entrada ou features para as técnicas de aprendizado de máquinas eram as mesmas que no modelo analítico, e, segundo, usando um processo de extração de features, usando análise de correlação e clustering hierarquizado. Nós mostramos que aplicações executadas em GPUs que escalam regularmente podem ser preditas com modelos analíticos simples e um parâmetro de ajuste. Esse parâmetro pode ser usado para predizer essas aplicações em outras GPUs. Nós também demonstramos que abordagens de ML proveem predições aceitáveis para diferentes casos e essas abordagens não exigem um conhecimento detalhado do código da aplicação, características de hardware ou modelagens explícita. Consequentemente, sempre e quando um banco de dados com informação de \\textit esteja disponível ou possa ser gerado, técnicas de ML podem ser úteis para aplicar uma predição automatizada de desempenho para escalonadores de aplicações em arquiteturas heterogêneas contendo GPUs.

Context. Today’s parallel systems are widely used in different computational tasks. Developing parallel programs to make maximum use of the computing power of parallel systems is tricky and efficient tuning of parallel programs is often very hard. Objectives. In this study we present a performance prediction and visualization tool named VPPB for a Linux environment, which had already been introduced by Broberg et.al, [1] for a Solaris2.x environment. VPPB shows the predicted behavior of a multithreaded program using any number of processors and the behavior is shown on two different graphs. The prediction is based on a monitored uni-processor execution. Methods. An experimental evaluation was carried out to validate the prediction reliability of the developed tool. Results. Validation of prediction is conducted, using an Intel multiprocessor with 8 processors and PARSEC 2.0 benchmark suite application programs. The validation shows that the speed-up predictions are +/-7% of a real execution. Conclusions. The experimentation of the VPPB tool showed that the prediction of VPPB is reliable and the incurred overhead into the application programs is low. / contact: +46(0)736368336

In this work we present a novel approach to network embedding for neural architecture search - info-NAS. The model learns to predict the output fea- tures of a trained convolutional neural network on a set of input images. We use the NAS-Bench-101 search space as the neural architecture dataset, and the CIFAR-10 as the image dataset. For the purpose of this task, we extend an existing unsupervised graph variational autoencoder, arch2vec, by jointly training on unlabeled and labeled neural architectures in a semi-supervised manner. To evaluate our approach, we analyze how our model learns on the data, compare it to the original arch2vec, and finally, we evaluate both mod- els on the NAS-Bench-101 search task and on the performance prediction task. 1