Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2008. / Title from first page of PDF file (viewed June 16, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 84-94).

In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC. A straightforward estimator for the true number of components is given by the most frequent number of non-empty components visited during MCMC sampling. Specifying a shrinkage prior, namely the normal gamma prior, on the component means leads to improved parameter estimates as well as identification of cluster-relevant variables. After estimating the mixture model using MCMC methods based on data augmentation and Gibbs sampling, an identified model is obtained by relabeling the MCMC output in the point process representation of the draws. This is performed using K-centroids cluster analysis based on the Mahalanobis distance. We evaluate our proposed strategy in a simulation setup with artificial data and by applying it to benchmark data sets. (authors' abstract)

This article describes the method of Mobile Process Landscaping by example of a project in which the service processes of a company from the housing industry were analyzed regarding their mobile potential. This analysis was conducted with the aim to organize these processes more efficiently in order to realize cost savings. Therefore, the method of Mobile Process Landscaping, which is introduced in this article, was used. The method refers to the stage of requirements engineering in the software process. It is shown how the initial situation of the company was analyzed, which alternative process models on the basis of mobility supporting technology were developed and how these alternatives were economically evaluated. Furthermore, it is shown how first restrictions for the software and system design were made on the basis of one process model. Finally, it is shown how the Mobile Process Landscaping method can be used to verify whether the adoption of mobility supporting technology is suitable to obtain a defined goal and which requirements such a solution needs to fulfill.

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Today, power consumption costs supercomputer centers millions of dollars annually and the heat produced can reduce system reliability and availability. Achieving high performance while reducing power consumption is challenging since power and performance are inextricably interwoven; reducing power often results in degradation in performance. This thesis aims to address these challenges by providing theories, techniques, and tools to 1) accurately predict performance and improve it in systems with advanced hierarchical memories, 2) understand and evaluate power and its impacts on performance, 3) control power and performance for maximum efficiency. Our theories, techniques, and tools have been applied to high-end computing systems. Our theroetical models can improve algorithm performance by up to 59% and accurately predict the impacts of power on performance. Our techniques can evaluate power consumption of high-end computing systems and their applications with fine granularity and save up to 36% energy with little performance degradation. / Ph. D.

Multicore architectures impose great pressure on resource management. The exploration spaces available for resource management increase explosively, especially for large-scale high end computing systems. The availability of abundant parallelism causes scalability concerns at all levels. Multicore architectures also impose pressure on power management. Growth in the number of cores causes continuous growth in power. In this dissertation, we introduce methods and techniques to enable scalable and energy efficient execution of parallel applications on multicore architectures. We study strategies and methodologies that combine DCT and DVFS for the hybrid MPI/OpenMP programming model. Our algorithms yield substantial energy saving (8.74% on average and up to 13.8%) with either negligible performance loss or performance gain (up to 7.5%). To save additional energy for high-end computing systems, we propose a power-aware MPI task aggregation framework. The framework predicts the performance effect of task aggregation in both computation and communication phases and its impact in terms of execution time and energy of MPI programs. Our framework provides accurate predictions that lead to substantial energy saving through aggregation (64.87% on average and up to 70.03%) with tolerable performance loss (under 5%). As we aggregate multiple MPI tasks within the same node, we have the scalability concern of memory registration for high performance networking. We propose a new memory registration/deregistration strategy to reduce registered memory on multicore architectures with helper threads. We investigate design polices and performance implications of the helper thread approach. Our method efficiently reduces registered memory (23.62% on average and up to 49.39%) and avoids memory registration/deregistration costs for reused communication memory. Our system enables the execution of application input sets that could not run to the completion with the memory registration limitation. / Ph. D.

By 2004, microprocessor design focused on multicore scaling"increasing the number of cores per die in each generation "as the primary strategy for improving performance. These multicore processors typically equip multiple memory subsystems to improve data throughput. In addition, these systems employ heterogeneous processors such as GPUs and heterogeneous memories like non-volatile memory to improve performance, capacity, and energy efficiency. With the increasing volume of hardware resources and system complexity caused by heterogeneity, future systems will require intelligent ways to manage hardware resources. Early research to improve performance and energy efficiency on heterogeneous, multi-core, multi-memory systems focused on tuning a single primitive or at best a few primitives in the systems. The key limitation of past efforts is their lack of a holistic approach to resource management that balances the tradeoff between performance and energy consumption. In addition, the shift from simple, homogeneous systems to these heterogeneous, multicore, multi-memory systems requires in-depth understanding of efficient resource management for scalable execution, including new models that capture the interchange between performance and energy, smarter resource management strategies, and novel low-level performance/energy tuning primitives and runtime systems. Tuning an application to control available resources efficiently has become a daunting challenge; managing resources in automation is still a dark art since the tradeoffs among programming, energy, and performance remain insufficiently understood. In this dissertation, I have developed theories, models, and resource management techniques to enable energy-efficient execution of parallel applications through thread and data management in these heterogeneous multi-core, multi-memory systems. I study the effect of dynamic concurrent throttling on the performance and energy of multi-core, non-uniform memory access (NUMA) systems. I use critical path analysis to quantify memory contention in the NUMA memory system and determine thread mappings. In addition, I implement a runtime system that combines concurrent throttling and a novel thread mapping algorithm to manage thread resources and improve energy efficient execution in multi-core, NUMA systems. In addition, I propose an analytical model based on the queuing method that captures important factors in multi-core, multi-memory systems to quantify the tradeoff between performance and energy. The model considers the effect of these factors in a holistic fashion that provides a general view of performance and energy consumption in contemporary systems. Finally, I focus on resource management of future heterogeneous memory systems, which may combine two heterogeneous memories to scale out memory capacity while maintaining reasonable power use. I present a new memory controller design that combines the best aspects of two baseline heterogeneous page management policies to migrate data between two heterogeneous memories so as to optimize performance and energy. / Ph. D.

O uso de microalgas e cianobactérias para a produção de biocombustíveis e outros produtos e matérias-primas de interesse comercial tem sido amplamente divulgado como uma tecnologia sustentável bastante promissora, em função das elevadas produtividades areais, potencial para fixação de CO2, uso de terras não adequadas para cultivo e possibilidade de utilizar fontes alternativas de nutrientes, tais como água salobra ou efluentes agroindustriais. A produção comercial de cianobactérias em tanques abertos em formato de pista foi estudada combinando-se a modelagem matemática do crescimento nos tanques com a avaliação técnica, econômica e de sustentabilidade do processo. Construiu-se um macromodelo para a simulação dos tanques, que permitiu determinar o impacto de variáveis ambientais como, por exemplo, temperatura e luminosidade, e otimizar condições de operação e coleta. A análise econômica detalhada demonstrou o impacto dos custos de capital, operação e consumo de energia pelo processo, também destacando a importância da receita de produtos de alto valor agregado para a viabilidade do sistema, com base na tecnologia atual. Os valores de transformidade e índices de sustentabilidade e carga ambiental, obtidos através da análise emergética, são comparáveis com outros processos para obtenção de biocombustíveis de segunda geração, mas os elevados custos de construção e operação e grande consumo de energia nas etapas de coleta e extração representam ainda grandes desafios à sua sustentabilidade. A análise de sensibilidade para as principais variáveis de processo e estudos de caso para melhorias e modelos de negócio alternativos permitiram priorizar áreas para pesquisa futura com base no impacto econômico e ambiental. / The use of microalgae and cyanobacteria for the production of biofuels and other substances of commercial interest has been widely advertised as an extremely promising sustainable technology, due to the high areal productivity, potential for fixation of CO2, possibility of using non-arable land and alternative sources of nutrients such as brackish water and agricultural and industrial effluents. The commercial production of cyanobacteria in open raceway ponds was studied through the combination of a mathematical model for the algal growth with technical, economical and sustainability evaluations. A macromodel was developed to simulate the ponds, and it was used to assess the impact of environmental variables, such as light and temperature, and to optimize the process conditions for operation and harvesting. A detailed economic analysis demonstrated the impact of capital, operation costs and energy consumption, also highlighting the importance of revenue from high value products to process viability, considering the current technology. The transformity, emergy sustainability and environmental loading indices obtained by emergy analysis are comparable to other second generation biofuels, but the high construction and operation costs and energy consumption by the harvesting and extraction steps still represent major challenges to sustainability. The sensitivity analysis and evaluation of both technology improvements and alternative business models enabled the prioritization of future research areas, based on economic and environmental impact.

O uso de microalgas e cianobactérias para a produção de biocombustíveis e outros produtos e matérias-primas de interesse comercial tem sido amplamente divulgado como uma tecnologia sustentável bastante promissora, em função das elevadas produtividades areais, potencial para fixação de CO2, uso de terras não adequadas para cultivo e possibilidade de utilizar fontes alternativas de nutrientes, tais como água salobra ou efluentes agroindustriais. A produção comercial de cianobactérias em tanques abertos em formato de pista foi estudada combinando-se a modelagem matemática do crescimento nos tanques com a avaliação técnica, econômica e de sustentabilidade do processo. Construiu-se um macromodelo para a simulação dos tanques, que permitiu determinar o impacto de variáveis ambientais como, por exemplo, temperatura e luminosidade, e otimizar condições de operação e coleta. A análise econômica detalhada demonstrou o impacto dos custos de capital, operação e consumo de energia pelo processo, também destacando a importância da receita de produtos de alto valor agregado para a viabilidade do sistema, com base na tecnologia atual. Os valores de transformidade e índices de sustentabilidade e carga ambiental, obtidos através da análise emergética, são comparáveis com outros processos para obtenção de biocombustíveis de segunda geração, mas os elevados custos de construção e operação e grande consumo de energia nas etapas de coleta e extração representam ainda grandes desafios à sua sustentabilidade. A análise de sensibilidade para as principais variáveis de processo e estudos de caso para melhorias e modelos de negócio alternativos permitiram priorizar áreas para pesquisa futura com base no impacto econômico e ambiental. / The use of microalgae and cyanobacteria for the production of biofuels and other substances of commercial interest has been widely advertised as an extremely promising sustainable technology, due to the high areal productivity, potential for fixation of CO2, possibility of using non-arable land and alternative sources of nutrients such as brackish water and agricultural and industrial effluents. The commercial production of cyanobacteria in open raceway ponds was studied through the combination of a mathematical model for the algal growth with technical, economical and sustainability evaluations. A macromodel was developed to simulate the ponds, and it was used to assess the impact of environmental variables, such as light and temperature, and to optimize the process conditions for operation and harvesting. A detailed economic analysis demonstrated the impact of capital, operation costs and energy consumption, also highlighting the importance of revenue from high value products to process viability, considering the current technology. The transformity, emergy sustainability and environmental loading indices obtained by emergy analysis are comparable to other second generation biofuels, but the high construction and operation costs and energy consumption by the harvesting and extraction steps still represent major challenges to sustainability. The sensitivity analysis and evaluation of both technology improvements and alternative business models enabled the prioritization of future research areas, based on economic and environmental impact.