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Current literature emphasizes poor long-term weight loss maintenance (WTLM) outcomes, and the need for inexpensive, practical solutions for effective WTLM is evident. Individuals successful at WTLM utilize similar behaviors but in varying amounts and combinations, seemingly choosing behaviors that best fit their preferences. Researchers have attempted to identify characteristics of individuals that may predict successful WTLM in order to develop flexible WTLM treatments based on individuals' lifestyle and preferences. The purpose of this analysis was to examine sex differences in WTLM outcomes and to identify potential behaviors related to WTLM success. In a 12-month study targeting WTLM, weight-reduced middle-aged and older men and women (n=39) were assigned behavioral goals for body weight, fruit and vegetable intake, water consumption, and physical activity and were asked to daily self-monitor body weight and these behaviors. Sex difference in clinically significant WL ≥5% WL) at 12 months was determined. A growth curve model assessed interactions of sex and WTLM predictors, and a crisp set qualitative comparative analysis (QCA) characterized individuals' weight changes and behaviors. No sex difference was found in clinically significant WL or in the interaction of sex and behaviors on weight change; however, QCA evidence suggests men and women may approach WTLM with different behaviors. Additionally, QCA findings suggest weight change in the first 3 months of WTLM may determine success at 12-months. WTLM treatments should provide more intensive support during the transition period from WL to WTLM. Future research in predictors of WTLM, particularly within the context of sex, is essential. / Master of Science

Diabetes mellitus affects 7.8% of the American population. National health statistic data and other research shows that racial/ethnic disparities exist in terms of prevalence and treatment outcomes. The present study investigated the role of patient health beliefs (i.e., locus of control, self-efficacy) and the doctor-patient relationship (e.g., satisfaction and collaboration with health care provider), as relative predictors of diabetic control (i.e., HbA1c levels) and overall satisfaction with diabetes care, in older adult participants with diabetes. Demographic, psychosocial, and diabetes-related data from the Health and Retirement Study (HRS) 2003 Diabetes Study were analyzed to compare treatment outcomes among non-Hispanic White, non-Hispanic Black, and Hispanic individuals with various types of diabetes. Non-Hispanic White individuals exhibited better diabetic control than their minority counterparts (F(2, 592) = 7.60, p < .001); however, no significant group differences were noted in terms of psychosocial factors. Diabetic control was best predicted by time since diagnosis (&#946; = -.21, p < .001), satisfaction with diabetes self-care (&#946; = .19, p < .001) and age (&#946; = .12, p < .01). In addition, satisfaction with provider care was best predicted by perceived collaboration with provider (&#946; = .44, p < .001), satisfaction with diabetes self-care (&#946; = .22, p < .001) and diabetes self-efficacy (&#946; = .08, p < .05). Recommendations for future research were discussed.

Yes / This study aimed to determine the predictors of Knowledge, Attitude and Practice (KAP) towards Family Planning (FP) among pregnant Fijian women. A cross-sectional study was conducted over two months in 2019 with adult pregnant women attending the Antenatal Clinic (ANC) at Ba Mission Hospital (BMH), Fiji. Data was collected using a self-administrated questionnaire. Statistical analysis included correlation tests and regression analysis in determining predictors of KAP. 240 pregnant women participated in this study with a mean age of 26.02 (± SD = 4.13). The results showed a moderate level of knowledge (mean 14.95, SD ± 3.15), positive attitude (mean 20.56, SD ± 5.68), and good practice (mean 4.97, SD ± 1.73). Linear regression identified that women with more than seven children had a knowledge score of 3.65, lower than null parity (t value = -2.577, p = 0.011). Women aged 20 to 24 had a 6.47 lower attitude score than women aged 18 to 19 (t value = -2.142, p = 0.033). Women in defacto relationships had a 2.12 lower attitude score compared to the married category (t value = -2.128, p = 0.034). Fijian women of Indian descent had a 1.98 lower attitude score than the I Taukei women (t value = -2.639, p = 0.009). Women aged 30-34 had 2.41 lower practice scores than those aged 18-19 (t value = -2.462, p = 0.015). This study found a medium knowledge of FP among pregnant women. These findings support a recommendation for further research to implement effective strategies.

The research regarding the effect of policy on teacher absenteeism is scarce and research examining teacher absenteeism from a state perspective is very limited. This mixed methods study analyzed selected school variables for public schools and districts in Virginia contained in the 2011-2012 and 2013-2014 Civil Rights Data Collection (CRDC) and in the National Center for Educational Statistics. In addition, a content analysis was performed on leave policies for all 132 school districts in Virginia yielding policy variables for the study. The purpose of the study was to determine the relationship of school and policy characteristics to teacher absences. The analysis for this study involved computing descriptive statistics, correlating continuous variables, and running multiple regressions for each dataset (school and district for each year) to determine the predictors of the dependent variable, chronically absent teachers, defined as the percentage of teachers absent for more than 10 days. Although the school models were significant, neither was a particularly strong predictor of chronically absent teachers, only accounting for 15.2 percent variation (2011-2012 model with R2=.152) and 9.6 percent variation (2013-2014 model with R2=.096) that is predicted by the independent variables. Nevertheless, there were independent policy and school variables that were significant predictors in both school years. The most prominent variables included: total leave, personal leave maximums, income protection provisions (sick leave banks, short-term disability), free and reduced lunch population percentage of a school, pupil/teacher ratio of the school, and the grade level of the school (elementary, middle, and high). / Ed. D. / Teacher absences have an educational impact on student achievement. Research specific to teacher absences is very limited. Using data for public school districts in Virginia, this study aimed to determine the relationship of school and policy characteristics to teacher absences. The study found that increasing the amount of total leave (sick and personal) granted annually and increasing the number of personal leave days a teacher is allowed to take annually, is likely to lead to higher number of teachers absent more than 10 days annually. The study also found schools with higher free and reduced lunch student population and higher pupil/teacher ratios are more likely to have a higher number of teachers absent more than 10 days. Finally the study found that there is a relationship of the grade level of the schools with middle schools having a higher percentage of teachers absent more than 10 days.

In mobile computing systems, predicting the next location of a mobile wireless user has gained interest over the past decade. Location prediction may have a wide-range of application areas such as network load balancing, advertising and web page prefetching. In the literature, there exist many location predictors which are divided into two main classes: domain-independent and domain-specific. Song et al. compare the prediction accuracy of the domain-independent predictors from four major families, namely, Markov-based, compression-based, PPM and SPM predictors on Dartmouth&#039 / s campus-wide Wi-Fi mobility data. As a result, the low-order Markov predictors are found as the best predictor. In another work, Bayir et al. propose a domain-specific location predictor (LPMP) as the application of a framework used for discovering mobile cell phone user profiles. In this thesis, we evaluate LPMP and the best Markov predictor with Dartmouth&#039 / s campus-wide Wi-Fi mobility data in terms of accuracy. We also propose a simple method which improves the accuracy of LPMP slightly in the location prediction part of LPMP. Our results show that the accuracy of the best Markov predictor is better than that of LPMP in total. However, interestingly, LPMP yields more accurate results than the best Markov predictor does for the users with the low prediction accuracy.

O consumo de energia se torna cada vez mais importante para a arquitetura de processadores, onde o número de cores dentro de um mesmo chip está aumentando mas o total de energia disponível se mantém no mesmo nível ou até mesmo se reduz. Assim, técnicas para economizar energia, tais como opções de escala de frequência e desligamento automático de subsistemas, estão sendo usadas para manter a troca entre energia e desempenho. Para se obter alto desempenho, os atuais Chip Multiprocessors (CMPs) integram grandes memórias cache a fim de reduzir a latência média para acesso a memória principal, através da alocação do conjunto de dados da aplicação dentro do chip. Essas memórias cache tem sido projetadas tradicionalmente para explorar a localidade temporal usando políticas de substituição inteligentes e localidade espacial buscando todos os dados da linha da cache após uma falta de dados. Entretanto, estudos recentes mostraram que o número de sub-blocos dentro da linha da memória cache, que são realmente usados, costuma ser baixo, sendo que, os sub-blocos que são usados recebem poucos acessos antes de se tornarem mortos (isto é, nunca mais são acessados). Além disso, muitas da linhas da memória cache permanecem ligadas por longos períodos de tempo, mesmo que os dados não sejam usados novamente ou são inválidos. Para linhas de cache modificadas, a memória cache aguarda até que a linha seja expulsa para que esta seja gravada (write-back) de volta no próximo nível de memória. Essas escritas competem com as requisições de leitura (demanda do processador e prébusca da cache), aumentando a pressão no controlador de memória. Por essas razões, a eficiência energética e o desempenho das memórias cache não são ideais. Essa tese propõe a aplicação de preditores de uso de linhas da cache para aumentar a eficiência energética das memórias cache. São propostos os mecanismos Dead Sub-Block Predictor (DSBP) e Dead Line and Early Write-Back Predictor (DEWP) para permitir economia de energia sem que haja degradação do desempenho. DSBP é usado para prever quais sub-blocos da linha da cache serão usados e quantas vezes eles serão acessados de forma a trazer para a cache apenas os sub-blocos úteis e desliga-los após eles serem acessados pelo número de vezes previsto. DEWP prevê linhas de cache mortas assim que elas recebem o último acesso, desligando essas linhas. As linhas sujas são escalonadas para sofrerem write-back após a última operação de escrita, aumentando o potencial de salvar energia, reduzindo também a pressão no controlador de memória. Ambos os mecanismos propostos também reduzem a poluição nas memórias cache, dando prioridade para a expulsão de linhas mortas, melhorando as atuais políticas de substituição. Embora cada mecanismo apresentado seja capaz de funcionar separadamente dentro do sistema, ambos os mecanismos podem também ser misturados em uma mesma hierarquia de cache. Essa implementação mista é interessante pois a granularidade de sub-bloco é preferível para níveis de cache próximos do processador, onde as linhas de memória cache são expulsas rapidamente, enquanto o último nível de cache tende a usar toda a linha antes da sua expulsão. Com o intuito de avaliar os mecanismos propostos, é apresentado o Simulator of Non- Uniform Cache Architectures (SiNUCA). Esse simulador de microarquitetura com precisão de ciclos é validado em termos de desempenho e consumo de energia através da comparação com um processador real. Os resultados de desempenho foram obtidos executando aplicações das cargas de trabalho single-threaded do conjunto SPEC-CPU2006 e aplicações multi-threaded dos conjuntos SPEC-OMP2001 e NAS-NPB. Os resultados relativos a energia foram obtidos integrando o SiNUCA com as ferramentas de modelagem Multi-core Power, Area, and Timing (McPAT) e CACTI. Quando aplicados os mecanismos em todos os níveis de memória cache, observou-se em média uma redução de 36% no consumo de energia usando o DSBP, 25% usando o DEWP e 37% quando usou-se o DSBP nos níveis L1 e L2 e o DEWP no último nível. Todas essas reduções causaram uma perda desprezível de desempenho de menos de 4% em média. / Energy consumption is becoming more important for processor architectures, where the number of cores inside the chip is increasing and the total power budget is kept at the same level or even reduced. Thus, energy saving techniques such as frequency scaling options and automatic shutdown of sub-systems are being used to maintain the trade-off between power and performance. To deliver high performance, current Chip Multiprocessors (CMPs) integrate large caches in order to reduce the average memory access latency by allocating the applications’ working set on-chip. These cache memories have traditionally been designed to exploit temporal locality by using smart replacement policies, and spatial locality by fetching entire cache lines from memory on a cache miss. However, recent studies have shown that the number of sub-blocks within a line that are actually used is often low, and those sub-blocks that are used are accessed only a few times before becoming dead (that is, never accessed again). Additionally, many of the cache lines remain powered for a long period of time even if the data is not used again, or is invalid. For modified cache lines, the cache memory waits until the line is evicted to perform the write-back to next memory level. These write-backs compete with read requests (processor demand and cache prefetch), increasing the pressure on the memory controller. For these reasons, the energy efficiency and performance of cache memories are not ideal. This thesis introduces cache line usage predictors to increase the energy efficiency of cache memories. We propose the Dead Sub-Block Predictor (DSBP) and Dead Line and Early Write-Back Predictor (DEWP) mechanisms to enable energy savings without performance degradation. DSBP is used to predict which sub-blocks of a cache line will be actually accessed and how many times they will be used in order to bring into the cache only those sub-blocks that are necessary, and power them off after they are accessed the predicted number of times. DEWP predicts dead lines as soon as they receive the last access, and turns off these lines. Dirty lines are scheduled for write-back after the last write operation occurs, increasing the energy savings potential and also reducing the pressure on the memory controller. Both proposed mechanisms also reduce pollution in cache memories by prioritizing dead lines for eviction in the existing replacement policy. Although each introduced mechanism is capable of performing separately inside a system, both mechanisms can also be mixed in the same cache hierarchy. This mixed implementation is interesting because the sub-block granularity is more suitable for cache levels closer to the processor, where the cache lines are quickly evicted, while the Last- Level Cache (LLC) tends to use the whole cache line before its eviction. In order to evaluate our proposed mechanisms, we introduce the Simulator of Non- Uniform Cache Architectures (SiNUCA). This cycle-accurate microarchitecture simulator is validated in terms of performance and energy consumption by comparing it to a real processor. Our performance results were obtained executing single-threaded applications from SPEC-CPU2006 and multi-threaded applications from SPEC-OMP2001 and NASNPB benchmark suites. The energy related results were obtained by integrating SiNUCA with the Multi-core Power, Area, and Timing (McPAT) framework and the CACTI power modeling tool. When applying our mechanisms on all the cache levels, we observe on average a 36% energy reduction for DSBP, 25% energy reduction using DEWP and an average reduction of 37% in the energy consumption applying DSBP on L1 and L2 and DEWP on the LLC. All these reductions caused a negligible performance loss of less than 4% on average.

O consumo de energia se torna cada vez mais importante para a arquitetura de processadores, onde o número de cores dentro de um mesmo chip está aumentando mas o total de energia disponível se mantém no mesmo nível ou até mesmo se reduz. Assim, técnicas para economizar energia, tais como opções de escala de frequência e desligamento automático de subsistemas, estão sendo usadas para manter a troca entre energia e desempenho. Para se obter alto desempenho, os atuais Chip Multiprocessors (CMPs) integram grandes memórias cache a fim de reduzir a latência média para acesso a memória principal, através da alocação do conjunto de dados da aplicação dentro do chip. Essas memórias cache tem sido projetadas tradicionalmente para explorar a localidade temporal usando políticas de substituição inteligentes e localidade espacial buscando todos os dados da linha da cache após uma falta de dados. Entretanto, estudos recentes mostraram que o número de sub-blocos dentro da linha da memória cache, que são realmente usados, costuma ser baixo, sendo que, os sub-blocos que são usados recebem poucos acessos antes de se tornarem mortos (isto é, nunca mais são acessados). Além disso, muitas da linhas da memória cache permanecem ligadas por longos períodos de tempo, mesmo que os dados não sejam usados novamente ou são inválidos. Para linhas de cache modificadas, a memória cache aguarda até que a linha seja expulsa para que esta seja gravada (write-back) de volta no próximo nível de memória. Essas escritas competem com as requisições de leitura (demanda do processador e prébusca da cache), aumentando a pressão no controlador de memória. Por essas razões, a eficiência energética e o desempenho das memórias cache não são ideais. Essa tese propõe a aplicação de preditores de uso de linhas da cache para aumentar a eficiência energética das memórias cache. São propostos os mecanismos Dead Sub-Block Predictor (DSBP) e Dead Line and Early Write-Back Predictor (DEWP) para permitir economia de energia sem que haja degradação do desempenho. DSBP é usado para prever quais sub-blocos da linha da cache serão usados e quantas vezes eles serão acessados de forma a trazer para a cache apenas os sub-blocos úteis e desliga-los após eles serem acessados pelo número de vezes previsto. DEWP prevê linhas de cache mortas assim que elas recebem o último acesso, desligando essas linhas. As linhas sujas são escalonadas para sofrerem write-back após a última operação de escrita, aumentando o potencial de salvar energia, reduzindo também a pressão no controlador de memória. Ambos os mecanismos propostos também reduzem a poluição nas memórias cache, dando prioridade para a expulsão de linhas mortas, melhorando as atuais políticas de substituição. Embora cada mecanismo apresentado seja capaz de funcionar separadamente dentro do sistema, ambos os mecanismos podem também ser misturados em uma mesma hierarquia de cache. Essa implementação mista é interessante pois a granularidade de sub-bloco é preferível para níveis de cache próximos do processador, onde as linhas de memória cache são expulsas rapidamente, enquanto o último nível de cache tende a usar toda a linha antes da sua expulsão. Com o intuito de avaliar os mecanismos propostos, é apresentado o Simulator of Non- Uniform Cache Architectures (SiNUCA). Esse simulador de microarquitetura com precisão de ciclos é validado em termos de desempenho e consumo de energia através da comparação com um processador real. Os resultados de desempenho foram obtidos executando aplicações das cargas de trabalho single-threaded do conjunto SPEC-CPU2006 e aplicações multi-threaded dos conjuntos SPEC-OMP2001 e NAS-NPB. Os resultados relativos a energia foram obtidos integrando o SiNUCA com as ferramentas de modelagem Multi-core Power, Area, and Timing (McPAT) e CACTI. Quando aplicados os mecanismos em todos os níveis de memória cache, observou-se em média uma redução de 36% no consumo de energia usando o DSBP, 25% usando o DEWP e 37% quando usou-se o DSBP nos níveis L1 e L2 e o DEWP no último nível. Todas essas reduções causaram uma perda desprezível de desempenho de menos de 4% em média. / Energy consumption is becoming more important for processor architectures, where the number of cores inside the chip is increasing and the total power budget is kept at the same level or even reduced. Thus, energy saving techniques such as frequency scaling options and automatic shutdown of sub-systems are being used to maintain the trade-off between power and performance. To deliver high performance, current Chip Multiprocessors (CMPs) integrate large caches in order to reduce the average memory access latency by allocating the applications’ working set on-chip. These cache memories have traditionally been designed to exploit temporal locality by using smart replacement policies, and spatial locality by fetching entire cache lines from memory on a cache miss. However, recent studies have shown that the number of sub-blocks within a line that are actually used is often low, and those sub-blocks that are used are accessed only a few times before becoming dead (that is, never accessed again). Additionally, many of the cache lines remain powered for a long period of time even if the data is not used again, or is invalid. For modified cache lines, the cache memory waits until the line is evicted to perform the write-back to next memory level. These write-backs compete with read requests (processor demand and cache prefetch), increasing the pressure on the memory controller. For these reasons, the energy efficiency and performance of cache memories are not ideal. This thesis introduces cache line usage predictors to increase the energy efficiency of cache memories. We propose the Dead Sub-Block Predictor (DSBP) and Dead Line and Early Write-Back Predictor (DEWP) mechanisms to enable energy savings without performance degradation. DSBP is used to predict which sub-blocks of a cache line will be actually accessed and how many times they will be used in order to bring into the cache only those sub-blocks that are necessary, and power them off after they are accessed the predicted number of times. DEWP predicts dead lines as soon as they receive the last access, and turns off these lines. Dirty lines are scheduled for write-back after the last write operation occurs, increasing the energy savings potential and also reducing the pressure on the memory controller. Both proposed mechanisms also reduce pollution in cache memories by prioritizing dead lines for eviction in the existing replacement policy. Although each introduced mechanism is capable of performing separately inside a system, both mechanisms can also be mixed in the same cache hierarchy. This mixed implementation is interesting because the sub-block granularity is more suitable for cache levels closer to the processor, where the cache lines are quickly evicted, while the Last- Level Cache (LLC) tends to use the whole cache line before its eviction. In order to evaluate our proposed mechanisms, we introduce the Simulator of Non- Uniform Cache Architectures (SiNUCA). This cycle-accurate microarchitecture simulator is validated in terms of performance and energy consumption by comparing it to a real processor. Our performance results were obtained executing single-threaded applications from SPEC-CPU2006 and multi-threaded applications from SPEC-OMP2001 and NASNPB benchmark suites. The energy related results were obtained by integrating SiNUCA with the Multi-core Power, Area, and Timing (McPAT) framework and the CACTI power modeling tool. When applying our mechanisms on all the cache levels, we observe on average a 36% energy reduction for DSBP, 25% energy reduction using DEWP and an average reduction of 37% in the energy consumption applying DSBP on L1 and L2 and DEWP on the LLC. All these reductions caused a negligible performance loss of less than 4% on average.

O consumo de energia se torna cada vez mais importante para a arquitetura de processadores, onde o número de cores dentro de um mesmo chip está aumentando mas o total de energia disponível se mantém no mesmo nível ou até mesmo se reduz. Assim, técnicas para economizar energia, tais como opções de escala de frequência e desligamento automático de subsistemas, estão sendo usadas para manter a troca entre energia e desempenho. Para se obter alto desempenho, os atuais Chip Multiprocessors (CMPs) integram grandes memórias cache a fim de reduzir a latência média para acesso a memória principal, através da alocação do conjunto de dados da aplicação dentro do chip. Essas memórias cache tem sido projetadas tradicionalmente para explorar a localidade temporal usando políticas de substituição inteligentes e localidade espacial buscando todos os dados da linha da cache após uma falta de dados. Entretanto, estudos recentes mostraram que o número de sub-blocos dentro da linha da memória cache, que são realmente usados, costuma ser baixo, sendo que, os sub-blocos que são usados recebem poucos acessos antes de se tornarem mortos (isto é, nunca mais são acessados). Além disso, muitas da linhas da memória cache permanecem ligadas por longos períodos de tempo, mesmo que os dados não sejam usados novamente ou são inválidos. Para linhas de cache modificadas, a memória cache aguarda até que a linha seja expulsa para que esta seja gravada (write-back) de volta no próximo nível de memória. Essas escritas competem com as requisições de leitura (demanda do processador e prébusca da cache), aumentando a pressão no controlador de memória. Por essas razões, a eficiência energética e o desempenho das memórias cache não são ideais. Essa tese propõe a aplicação de preditores de uso de linhas da cache para aumentar a eficiência energética das memórias cache. São propostos os mecanismos Dead Sub-Block Predictor (DSBP) e Dead Line and Early Write-Back Predictor (DEWP) para permitir economia de energia sem que haja degradação do desempenho. DSBP é usado para prever quais sub-blocos da linha da cache serão usados e quantas vezes eles serão acessados de forma a trazer para a cache apenas os sub-blocos úteis e desliga-los após eles serem acessados pelo número de vezes previsto. DEWP prevê linhas de cache mortas assim que elas recebem o último acesso, desligando essas linhas. As linhas sujas são escalonadas para sofrerem write-back após a última operação de escrita, aumentando o potencial de salvar energia, reduzindo também a pressão no controlador de memória. Ambos os mecanismos propostos também reduzem a poluição nas memórias cache, dando prioridade para a expulsão de linhas mortas, melhorando as atuais políticas de substituição. Embora cada mecanismo apresentado seja capaz de funcionar separadamente dentro do sistema, ambos os mecanismos podem também ser misturados em uma mesma hierarquia de cache. Essa implementação mista é interessante pois a granularidade de sub-bloco é preferível para níveis de cache próximos do processador, onde as linhas de memória cache são expulsas rapidamente, enquanto o último nível de cache tende a usar toda a linha antes da sua expulsão. Com o intuito de avaliar os mecanismos propostos, é apresentado o Simulator of Non- Uniform Cache Architectures (SiNUCA). Esse simulador de microarquitetura com precisão de ciclos é validado em termos de desempenho e consumo de energia através da comparação com um processador real. Os resultados de desempenho foram obtidos executando aplicações das cargas de trabalho single-threaded do conjunto SPEC-CPU2006 e aplicações multi-threaded dos conjuntos SPEC-OMP2001 e NAS-NPB. Os resultados relativos a energia foram obtidos integrando o SiNUCA com as ferramentas de modelagem Multi-core Power, Area, and Timing (McPAT) e CACTI. Quando aplicados os mecanismos em todos os níveis de memória cache, observou-se em média uma redução de 36% no consumo de energia usando o DSBP, 25% usando o DEWP e 37% quando usou-se o DSBP nos níveis L1 e L2 e o DEWP no último nível. Todas essas reduções causaram uma perda desprezível de desempenho de menos de 4% em média. / Energy consumption is becoming more important for processor architectures, where the number of cores inside the chip is increasing and the total power budget is kept at the same level or even reduced. Thus, energy saving techniques such as frequency scaling options and automatic shutdown of sub-systems are being used to maintain the trade-off between power and performance. To deliver high performance, current Chip Multiprocessors (CMPs) integrate large caches in order to reduce the average memory access latency by allocating the applications’ working set on-chip. These cache memories have traditionally been designed to exploit temporal locality by using smart replacement policies, and spatial locality by fetching entire cache lines from memory on a cache miss. However, recent studies have shown that the number of sub-blocks within a line that are actually used is often low, and those sub-blocks that are used are accessed only a few times before becoming dead (that is, never accessed again). Additionally, many of the cache lines remain powered for a long period of time even if the data is not used again, or is invalid. For modified cache lines, the cache memory waits until the line is evicted to perform the write-back to next memory level. These write-backs compete with read requests (processor demand and cache prefetch), increasing the pressure on the memory controller. For these reasons, the energy efficiency and performance of cache memories are not ideal. This thesis introduces cache line usage predictors to increase the energy efficiency of cache memories. We propose the Dead Sub-Block Predictor (DSBP) and Dead Line and Early Write-Back Predictor (DEWP) mechanisms to enable energy savings without performance degradation. DSBP is used to predict which sub-blocks of a cache line will be actually accessed and how many times they will be used in order to bring into the cache only those sub-blocks that are necessary, and power them off after they are accessed the predicted number of times. DEWP predicts dead lines as soon as they receive the last access, and turns off these lines. Dirty lines are scheduled for write-back after the last write operation occurs, increasing the energy savings potential and also reducing the pressure on the memory controller. Both proposed mechanisms also reduce pollution in cache memories by prioritizing dead lines for eviction in the existing replacement policy. Although each introduced mechanism is capable of performing separately inside a system, both mechanisms can also be mixed in the same cache hierarchy. This mixed implementation is interesting because the sub-block granularity is more suitable for cache levels closer to the processor, where the cache lines are quickly evicted, while the Last- Level Cache (LLC) tends to use the whole cache line before its eviction. In order to evaluate our proposed mechanisms, we introduce the Simulator of Non- Uniform Cache Architectures (SiNUCA). This cycle-accurate microarchitecture simulator is validated in terms of performance and energy consumption by comparing it to a real processor. Our performance results were obtained executing single-threaded applications from SPEC-CPU2006 and multi-threaded applications from SPEC-OMP2001 and NASNPB benchmark suites. The energy related results were obtained by integrating SiNUCA with the Multi-core Power, Area, and Timing (McPAT) framework and the CACTI power modeling tool. When applying our mechanisms on all the cache levels, we observe on average a 36% energy reduction for DSBP, 25% energy reduction using DEWP and an average reduction of 37% in the energy consumption applying DSBP on L1 and L2 and DEWP on the LLC. All these reductions caused a negligible performance loss of less than 4% on average.