476 resultados para .NET Framework


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Dissertation to obtain the degree of Doctor of Philosophy in Electrical and Computer Engineering(Industrial Information Systems)

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Submitted to the graduate faculty Universidade Nova de Lisboa – Faculdade de Ciências e Tecnologia in partial fulfillment of the requirements for the degree of Master in Industrial Engineering

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

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Dissertation presented to Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa for obtaining the master degree in Membrane Engineering

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Research Project submited as partial fulfilment for the Master Degree in Statistics and Information Management

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática

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Dissertação para obtenção do Grau de Doutor em Engenharia Química

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies

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Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores

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Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores

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The Graphics Processing Unit (GPU) is present in almost every modern day personal computer. Despite its specific purpose design, they have been increasingly used for general computations with very good results. Hence, there is a growing effort from the community to seamlessly integrate this kind of devices in everyday computing. However, to fully exploit the potential of a system comprising GPUs and CPUs, these devices should be presented to the programmer as a single platform. The efficient combination of the power of CPU and GPU devices is highly dependent on each device’s characteristics, resulting in platform specific applications that cannot be ported to different systems. Also, the most efficient work balance among devices is highly dependable on the computations to be performed and respective data sizes. In this work, we propose a solution for heterogeneous environments based on the abstraction level provided by algorithmic skeletons. Our goal is to take full advantage of the power of all CPU and GPU devices present in a system, without the need for different kernel implementations nor explicit work-distribution.To that end, we extended Marrow, an algorithmic skeleton framework for multi-GPUs, to support CPU computations and efficiently balance the work-load between devices. Our approach is based on an offline training execution that identifies the ideal work balance and platform configurations for a given application and input data size. The evaluation of this work shows that the combination of CPU and GPU devices can significantly boost the performance of our benchmarks in the tested environments, when compared to GPU-only executions.