Improving heterogeneous system efficiency : architecture, scheduling, and machine learning

Computer architects are beginning to embrace heterogeneous systems as an effective method to utilize increases in transistor densities for executing a diverse range of workloads under varying performance and energy constraints. As heterogeneous systems become more ubiquitous, architects will need to develop novel CPU scheduling techniques capable of exploiting the diversity of computational resources. In recognizing hardware diversity, state-of-the-art heterogeneous schedulers are able to produce significant performance improvements over their predecessors and enable more flexible system designs. Nearly all of these, however, are unable to efficiently identify the mapping schemes which will result in the highest system performance. Accurately estimating the performance of applications on different heterogeneous resources can provide a significant advantage to heterogeneous schedulers for identifying a performance maximizing mapping scheme to improve system performance. Recent advances in machine learning techniques including artificial neural networks have led to the development of powerful and practical prediction models for a variety of fields. As of yet, however, no significant leaps have been taken towards employing machine learning for heterogeneous scheduling in order to maximize system throughput. The core issue we approach is how to understand and utilize the rise of heterogeneous architectures, benefits of heterogeneous scheduling, and the promise of machine learning techniques with respect to maximizing system performance. We present studies that promote a future computing model capable of supporting massive hardware diversity, discuss the constraints faced by heterogeneous designers, explore the advantages and shortcomings of conventional heterogeneous schedulers, and pioneer applying machine learning to optimize mapping and system throughput. The goal of this thesis is to highlight the importance of efficiently exploiting heterogeneity and to validate the opportunities that machine learning can offer for various areas in computer architecture.Arquitectos de computadores estan empesando a diseñar systemas heterogeneos como una manera efficiente de usar los incrementos en densidades de transistors para ejecutar una gran diversidad de programas corriendo debajo de differentes condiciones y requisitos de energia y rendimiento (performance). En cuanto los sistemas heterogeneos van ganando popularidad de uso, arquitectos van a necesitar a diseñar nuevas formas de hacer el scheduling de las applicaciones en los cores distintos de los CPUs. Schedulers nuevos que tienen en cuenta la heterogeniedad de los recursos en el hardware logran importantes beneficios en terminos de rendimiento en comparacion con schedulers hecho para sistemas homogenios. Pero, casi todos de estos schedulers heterogeneos no son capaz de poder identificar la esquema de mapping que produce el rendimiento maximo dado el estado de los cores y las applicaciones. Estimando con precision el rendimiento de los programas ejecutando sobre diferentes cores de un CPU es un a gran ventaja para poder identificar el mapping para lograr el mejor rendimiento posible para el proximo scheduling quantum. Desarollos nuevos en la area de machine learning, como redes neurales, han producido predictores muy potentes y con gran precision in disciplinas numerosas. Pero en estos momentos, la aplicacion de metodos de machine learning no se han casi explorados para poder mejorar la eficiencia de los CPUs y menos para mejorar los schedulers para sistemas heterogeneos. El tema de enfoque en esta tesis es como poder entender y utilizar los sistemas heterogeneos, los beneficios de scheduling para estos sistemas, y como aprovechar las promesas de los metodos de machine learning con respeto a maximizer el redimiento de el Sistema. Presentamos estudios que dan una esquema para un modelo de computacion para el futuro capaz de dar suporte a recursos heterogeneos en gran escala, discutimos las restricciones enfrentados por diseñadores de sistemas heterogeneos, exploramos las ventajas y desventajas de las ultimas schedulers heterogeneos, y abrimos el camino de usar metodos de machine learning para optimizer el mapping y rendimiento de un sistema heterogeneo. El objetivo de esta tesis es destacar la imporancia de explotando eficientemente la heterogenidad de los recursos y tambien validar las oportunidades para mejorar la eficiencia en diferente areas de arquitectura de computadoras que pueden ser realizadas gracias a machine learning.Postprint (published version

Impacto do exercício sobre o metabolismo dos lipídeos e da dislipidemia

The chronic practice of exercise induces a series of cellular and organismal adaptations that modify the way the human body metabolizes all macronutrients, including lipids. Endurance exercise and resistance exercise elicit different responses that result in differential effects on lipid and lipoprotein metabolism. These effects are quantitatively and qualitatively different and mediated by distinct signaling pathways. In this review, we summarize relevant evidence on the impact of exercise on lipid and lipoprotein metabolism, and finalize with some practical recommendations on exercise practice for patients with dyslipidemia in the primary care settingLa práctica crónica del ejercicio induce una serie de adaptaciones celulares y orgánicas que modifican la forma en que el cuerpo humano metaboliza todos los macronutrientes, incluidos los lípidos. El ejercicio de duración y el ejercicio de resistencia provocan diferentes respuestas que resultan en efectos diferenciales sobre el metabolismo de los lípidos y las lipoproteínas. Estos efectos son cuantitativa y cualitativamente diferentes y mediados por distintas vías de  señalización. Esta revisión, resume la evidencia pertinente sobre la repercusión del ejercicio en el metabolismo de los lípidos y las lipoproteínas, y finaliza con algunas recomendaciones sobre la práctica del ejercicio para los pacientes con dislipidemia en el ámbito de la atención primariaA prática crônica de exercício induz uma série de adaptações celulares e orgânicas que modificam a maneira pela qual o corpo humano metaboliza todos os macronutrientes, incluindo os lipídios. O exercício de duração e o exercício de resistência provocam diversas respostas que resultam em efeitos diferenciais no metabolismo de lipídios e lipoproteínas. Estes efeitos são quantitativa e qualitativamente distintos e mediados por diferentes vias de sinalização. Nesta revisão, se resume as evidências relevantes sobre o impacto do exercício no metabolismo de lipídios e das lipoproteínas e conclui, com algumas recomendações sobre a prática de exercícios para pacientes com dislipidemia no campo da atenção primári

Regulation of pH by Carbonic Anhydrase 9 Mediates Survival of Pancreatic Cancer Cells With Activated KRAS in Response to Hypoxia.

Background & Aims Most pancreatic ductal adenocarcinomas (PDACs) express an activated form of KRAS, become hypoxic and dysplastic, and are refractory to chemo and radiation therapies. To survive in the hypoxic environment, PDAC cells upregulate enzymes and transporters involved in pH regulation, including the extracellular facing carbonic anhydrase 9 (CA9). We evaluated the effect of blocking CA9, in combination with administration of gemcitabine, in mouse models of pancreatic cancer. Methods We knocked down expression of KRAS in human (PK-8 and PK-1) PDAC cells with small hairpin RNAs. Human and mouse (KrasG12D/Pdx1-Cre/Tp53/RosaYFP) PDAC cells were incubated with inhibitors of MEK (trametinib) or extracellular signal-regulated kinase (ERK), and some cells were cultured under hypoxic conditions. We measured levels and stability of the hypoxia-inducible factor 1 subunit alpha (HIF1A), endothelial PAS domain 1 protein (EPAS1, also called HIF2A), CA9, solute carrier family 16 member 4 (SLC16A4, also called MCT4), and SLC2A1 (also called GLUT1) by immunoblot analyses. We analyzed intracellular pH (pHi) and extracellular metabolic flux. We knocked down expression of CA9 in PDAC cells, or inhibited CA9 with SLC-0111, incubated them with gemcitabine, and assessed pHi, metabolic flux, and cytotoxicity under normoxic and hypoxic conditions. Cells were also injected into either immune-compromised or immune-competent mice and growth of xenograft tumors was assessed. Tumor fragments derived from patients with PDAC were surgically ligated to the pancreas of mice and the growth of tumors was assessed. We performed tissue microarray analyses of 205 human PDAC samples to measure levels of CA9 and associated expression of genes that regulate hypoxia with outcomes of patients using the Cancer Genome Atlas database. Results Under hypoxic conditions, PDAC cells had increased levels of HIF1A and HIF2A, upregulated expression of CA9, and activated glycolysis. Knockdown of KRAS in PDAC cells, or incubation with trametinib, reduced the posttranscriptional stabilization of HIF1A and HIF2A, upregulation of CA9, pHi, and glycolysis in response to hypoxia. CA9 was expressed by 66% of PDAC samples analyzed; high expression of genes associated with metabolic adaptation to hypoxia, including CA9, correlated with significantly reduced survival times of patients. Knockdown or pharmacologic inhibition of CA9 in PDAC cells significantly reduced pHi in cells under hypoxic conditions, decreased gemcitabine-induced glycolysis, and increased their sensitivity to gemcitabine. PDAC cells with knockdown of CA9 formed smaller xenograft tumors in mice, and injection of gemcitabine inhibited tumor growth and significantly increased survival times of mice. In mice with xenograft tumors grown from human PDAC cells, oral administration of SLC-0111 and injection of gemcitabine increased intratumor acidosis and increased cell death. These tumors, and tumors grown from PDAC patient-derived tumor fragments, grew more slowly than xenograft tumors in mice given control agents, resulting in longer survival times. In KrasG12D/Pdx1-Cre/Tp53/RosaYFP genetically modified mice, oral administration of SLC-0111 and injection of gemcitabine reduced numbers of B cells in tumors. Conclusions In response to hypoxia, PDAC cells that express activated KRAS increase expression of CA9, via stabilization of HIF1A and HIF2A, to regulate pH and glycolysis. Disruption of this pathway slows growth of PDAC xenograft tumors in mice and might be developed for treatment of pancreatic cancer