Dissertations / Theses on the topic 'Winding machines Computer programs'
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Biglari-Abhari, Morteza. "Performance improvement through predicated execution in VLIW machines." Title page, contents and abstract only, 2000. http://web4.library.adelaide.edu.au/theses/09PH/09phb593.pdf.
Full textStein, S. C. "Modelling of the filament-winding fabrication process." Thesis, Virginia Tech, 1990. http://hdl.handle.net/10919/41585.
Full textMaster of Science
Doddapaneni, Srinivas P. "Automatic dynamic decomposition of programs on distributed memory machines." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/8158.
Full textAtger, Dominique. "A Turing machines simulator using a Microsoft Windows' interface." Virtual Press, 1993. http://liblink.bsu.edu/uhtbin/catkey/865965.
Full textDepartment of Computer Science
Song, Jun. "Compiling ACE for Distributed-Memory Machines." PDXScholar, 1992. https://pdxscholar.library.pdx.edu/open_access_etds/4568.
Full textShih, Po-Jen. "On-Line Consolidation of Thermoplastic Composites." Diss., A&T Access:, 1997. http://scholar.lib.vt.edu/theses/public/etd-135510112972690/etd-title.html.
Full text"UMI number: 9724066"--T.p. verso. Vita. Includes bibliographical references (p. 182-189). Available electronically via Internet.
Orgill, Gregory Leavitt. "Java Performance of the Rijndael Encryption Algorithm Across Compilers and Virtual Machines." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd681.pdf.
Full textPark, Seongmin. "A hypertext learning system for theory of computation." Virtual Press, 1993. http://liblink.bsu.edu/uhtbin/catkey/897499.
Full textDepartment of Computer Science
Lucas, Divino César Soares 1985. "Modeling the performance impact of hot code misprediction in Cross-ISA virtual machines = Modelagem do impacto de erros de predição de código quente no desempenho de máquinas virtuais." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275628.
Full textDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: Máquinas virtuais (MVs) são sistemas que se propõem a eliminar a incompatibilidade entre duas, em geral diferentes, interfaces e dessa forma habilitar a comunicação entre diferentes sistemas. Nesse sentido, atuando como mediadores, uma MV está em um ponto que a permite fomentar o desenvolvimento de soluções inovadoras para vários problemas. Tais sistemas geralmente utilizam técnicas de emulação, por exemplo, interpretação ou tradução dinâmica de binários, para executar o código da aplicação cliente. Para determinar qual técnica de emulação é a ideal para um trecho de código geralmente é necessário que a MV empregue algum tipo de predição para determinar se o benefício de compilar o código supera os custos. Este problema, na maioria dos casos, resume-se a predizer se o dado trecho de código será frequentemente executado ou não, problema conhecido pelo nome de Predição de Código Quente. Em geral, se o preditor sinalizar um trecho de código como quente, a MV imediatamente toma a decisão de compilá-lo. Contudo, um problema surge nesta estratégia, à resposta do preditor é apenas a decisão de uma heurística e é, portanto, suscetível a erros. Quando o preditor sinaliza como quente um trecho de código que não será frequentemente executado, ou seja, um código que de fato é "frio", ele está fazendo uma predição errônea de código quente. Quando uma predição incorreta é feita, ocorre que a técnica de emulação que a MV utilizará para emular o trecho de código não compensará o seu custo e, portanto a MV gastará mais tempo executando o seu próprio código do que o código da aplicação cliente. Neste trabalho, foi avaliado o impacto de predições incorretas de código quente no desempenho de MVs emulando vários tipos de aplicações. Na análise realizada foi avaliado o preditor de código quente baseado em limiar, uma técnica frequentemente utilizada para identificar regiões de código que serão frequentemente executadas. Para fazer esta análise foi criado um modelo matemático para simular o comportamento de tal preditor e a partir deste modelo uma série de resultados puderam ser explorados. Inicialmente é mostrado que este preditor frequentemente erra a predição e, como conseqüência, o tempo gasto fazendo compilações torna-se o maior componente do tempo de execução da MV. Também é mostrado como diferentes limiares de predição afetam o número de predições incorretas e qual o impacto disto no desempenho da MV. Também são apresentados resultados indicando qual o impacto do custo de compilação, tradução e velocidade do código traduzido no desempenho da MV. Por fim é mostrado que utilizando apenas o conjunto de aplicações do SPEC CPU 2006 para avaliar o desempenho de MVs que utilizam o preditor de código quente baseado em limiar pode levar a resultados imprecisos
Abstract: Virtual machines are systems that aim to eliminate the compatibility gap between two, possible distinct, interfaces, thus enabling them to communicate. This way, acting like a mediator, the VM lies at an important position that enables it to foster innovative solutions for many problems. Such systems usually rely on emulation techniques, such as interpretation and dynamic binary translation, to execute guest application code. In order to select the best emulation technique for each code segment, the VM typically needs to predict whether the cost of compiling the code overcome its future execution time. This problem, in the common case, reduce to predicting if the given code region will be frequently executed or not, a problem called Hot Code Prediction. Generally, if the predictor flags a given code region as hot the VM instantly takes the decision to compile it. However, a problem came out from this strategy, the predictor response is only a decision made by means of a heuristic and thus it can be incorrect. Whenever the predictor flags a code region that will be infrequently executed (cold code) as hot code, we say that it is doing a hotness misprediction. Whenever a misprediction happens it means that the technique the VM will use to emulate the code will not have its cost amortized by executing the optimized code and thus the VM will, in fact, spend more time executing its own code rather than the guest application code. In this work we measure the impact of hotness mispredictions in a VM emulating several kinds of applications. In our analysis we evaluate the threshold-based hot code predictor, a technique commonly used to predict hot code fragments. To do so we developed a mathematical model to simulate the behavior of such predictor and we use it to estimate the impact of mispredictions in several benchmarks. We show that this predictor frequently mispredicts the code hotness and as a result the VM emulation performance becomes dominated by miscompilations. Moreover, we show how the threshold choice can affect the number of mispredictions and how this impacts the VM performance. We also show how the compilation, interpretation and steady state execution cost of translated instructions affect the VM performance. At the end we show that using SPEC CPU 2006 benchmarks to measure the performance of a VM using the threshold-based predictor can lead to misleading results
Mestrado
Ciência da Computação
Mestre em Ciência da Computação
Zinsly, Raphael Moreira 1989. "Técnicas de formação de regiões para projetos de máquinas virtuais eficientes." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275615.
Full textDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: O resumo poderá ser visualizado no texto completo da tese digital
Abstract: The complete abstract is available with the full electronic document
Mestrado
Ciência da Computação
Mestre em Ciência da Computação
Paquet, Ulrich. "Training support vector machines with particle swarms." Diss., 2004. http://hdl.handle.net/2263/27064.
Full textDissertation (MSc)--University of Pretoria, 2007.
Computer Science
Unrestricted
Heberle, David D. "Performance and parameter prediction of large synchronous machines from physical dimensions." Thesis, 1993. http://hdl.handle.net/1957/35944.
Full textGraduation date: 1994
Pillai, Satish Jacome Margarida F. "Compiler directed speculation for embedded clustered EPIC machines." 2004. http://repositories.lib.utexas.edu/bitstream/handle/2152/2164/pillais042.pdf.
Full textPillai, Satish. "Compiler directed speculation for embedded clustered EPIC machines." Thesis, 2004. http://hdl.handle.net/2152/2164.
Full textLauffer, Sabine C. "Translating with computer-assisted technology : a study of translator experience /." 2005.
Find full textTypescript. Includes bibliographical references (leaves 88-94). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss &rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR11831
Banitalebi, Dehkordi Ali. "Improved models of electric machines for real-time digital simulation." 2010. http://hdl.handle.net/1993/3935.
Full textIrvine, Allison W. "Computational Analysis of Flow Cytometry Data." 2013. http://hdl.handle.net/1805/3367.
Full textThe objective of this thesis is to compare automated methods for performing analysis of flow cytometry data. Flow cytometry is an important and efficient tool for analyzing the characteristics of cells. It is used in several fields, including immunology, pathology, marine biology, and molecular biology. Flow cytometry measures light scatter from cells and fluorescent emission from dyes which are attached to cells. There are two main tasks that must be performed. The first is the adjustment of measured fluorescence from the cells to correct for the overlap of the spectra of the fluorescent markers used to characterize a cell’s chemical characteristics. The second is to use the amount of markers present in each cell to identify its phenotype. Several methods are compared to perform these tasks. The Unconstrained Least Squares, Orthogonal Subspace Projection, Fully Constrained Least Squares and Fully Constrained One Norm methods are used to perform compensation and compared. The fully constrained least squares method of compensation gives the overall best results in terms of accuracy and running time. Spectral Clustering, Gaussian Mixture Modeling, Naive Bayes classification, Support Vector Machine and Expectation Maximization using a gaussian mixture model are used to classify cells based on the amounts of dyes present in each cell. The generative models created by the Naive Bayes and Gaussian mixture modeling methods performed classification of cells most accurately. These supervised methods may be the most useful when online classification is necessary, such as in cell sorting applications of flow cytometers. Unsupervised methods may be used to completely replace manual analysis when no training data is given. Expectation Maximization combined with a cluster merging post-processing step gives the best results of the unsupervised methods considered.