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MULTI-DIMENSIONAL BATCH SCHEDULING FRAMEWORK FOR HIGH-END SUPERCOMPUTERS
Title
MULTI-DIMENSIONAL BATCH SCHEDULING FRAMEWORK FOR HIGH-END SUPERCOMPUTERS
Creator
Zhou, Zhou
Date
2016, 2016-05
Description
In the field of high performance computing (HPC), batch scheduling plays a critical role. They determine when and how to process the various...
Show moreIn the field of high performance computing (HPC), batch scheduling plays a critical role. They determine when and how to process the various jobs waiting for service. Conventional batch schedulers allocate user jobs solely based on their CPU footprints. However, for a given user job, it requires many different resources during its execution, such as power, network, I/O bandwidth, etc. Today’s job schedulers rarely take into account these resource requirements which sometimes turn out to be the Achilles’ heel of system-wide performance. In this research, we propose a multi-dimensional batch scheduling framework for high-end supercomputers. Our research aims to treat these common but often ignored resources (e.g., power, network, bandwidth) as schedulable resource and further transform each scheduling into a multi-objective optimization process. Our main contributions consist of a set of scheduling models and policies, aiming at addressing the issues in batch scheduling for large-scale production supercomputers. We evaluate our design by means of trace-based simulations using real workload and performance traces from production systems. Experimental results show our methods can effectively improve batch scheduling regarding user satisfaction, system performance and operating cost.
Ph.D. in Computer Science, May 2016
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MODELS AND SIMULATIONS OF SPROUTING ANGIOGENESIS
Title
MODELS AND SIMULATIONS OF SPROUTING ANGIOGENESIS
Creator
Langman, Catherine
Date
2016, 2016-05
Description
All living mammalian cells need to consume oxygen and nutrients for cellular processes and need a way to remove waste from those cellular...
Show moreAll living mammalian cells need to consume oxygen and nutrients for cellular processes and need a way to remove waste from those cellular processes. Capillary networks provide places for such exchanges to occur. The process of creating new capillaries from existing blood vessels is called angiogenesis. Understanding angiogenesis is critical to the advancement of knowledge in the life sciences, as well as in medical applications where blood vessels play an important role. Angiogenesis is a complex process composed of many subprocesses which are not yet fully understood and take place over varying temporal and spatial scales. Mathematically modeling and simulating angiogenesis, and evaluating the capillary networks that result from angiogenesis, can help further understanding of angiogenesis and improve therapeutic treatments. This thesis examines mathematical models and simulations of sprouting angiogenesis and proposes two generic models of sprouting angiogenesis based on descriptions found in educational and scientific literature. Future research opportunities for scientific study and educational study using these models as a starting place are discussed.
M.S. in Applied Mathematics, May 2016
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GUARANTEED ADAPTIVE MONTE CARLO METHODS FOR ESTIMATING MEANS OF RANDOM VARIABLES
Title
GUARANTEED ADAPTIVE MONTE CARLO METHODS FOR ESTIMATING MEANS OF RANDOM VARIABLES
Creator
Jiang, Lan
Date
2016, 2016-05
Description
Monte Carlo is a versatile computational method that may be used to approximate the means, μ, of random variables, Y , whose distributions are...
Show moreMonte Carlo is a versatile computational method that may be used to approximate the means, μ, of random variables, Y , whose distributions are not known explicitly. This thesis investigates how to reliably construct fixed width confidence intervals for μ with some prescribed absolute error tolerance, "a, relative error tolerance, "r or some generalized error criterion. To facilitate this, it is assumed that the kurtosis, , of the random variable, Y , does not exceed a user specified bound max. The key idea is to confidently estimate the variance of Y by applying Cantelli’s Inequality. A Berry-Esseen Inequality makes it possible to determine the sample size required to construct such a confidence interval. When relative error is involved, this requires an iterative process. This idea for computing μ = E(Y ) can be used to develop a numerical integration method by writing the integral as μ = E(f(x)) = RRd f(x)⇢(x)dx, where x is a d dimensional random vector with probability density function ⇢. A similar idea is used to develop an algorithm for computing p = E(Y) where Y is a Bernoulli random variable. All of the algorithms have been implemented in the Guaranteed Automatic Integration Library (GAIL).
Ph.D. in Applied Mathematics, May 2016
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Quantification of Vascular Permeability in the Retina Using Fluorescein Videoangiography Data as a Biomarker for Early Diabetic Retinopathy
Title
Quantification of Vascular Permeability in the Retina Using Fluorescein Videoangiography Data as a Biomarker for Early Diabetic Retinopathy
Creator
Kayaalp Nalbant, Elif
Date
2023
Description
Diabetic retinopathy, which is the most common reason for blindness in the working-age population, affects over one-third of those who have...
Show moreDiabetic retinopathy, which is the most common reason for blindness in the working-age population, affects over one-third of those who have had diabetes for over ten years. High blood sugar level (hyperglycemia) in the blood damages blood vessels and tight junction at the blood-retinal barrier (BRB). Chronic inflammation leads to changes in vascular health, and over time blood vessels tend to get damaged and exhibit higher “leakage” or permeability. In the late stage of DR, hemorrhages can occur, leading to irreversible damage of neuronal tissue in the retina and vision loss. In the clinic, there are some biomarkers and imaging modalities used to diagnose DR based on some of the more severe products of DR (e.g., hemorrhage), but there is no non-invasive, highly sensitive method to detect diabetic retinopathy before clinical signs occur, when mitigating therapies could be more effective. In this thesis, indicator dilution theory was explored to modeling the temporal dynamics of fluorescein in the retina after intravenous injection, with an aim to quantitatively map subtle changes in retinal blood flow and vascular permeability that could preempt subsequent irreversible damage. Specifically, a simplified version of indicator dilution theory—namely the “adiabatic approximation in tissue homogeneity” (AATH) model—was used to estimate physiological parameters such as the blood flow (F) and the extraction fraction (E: a parameter coupled with vascular permeability) from retinal fluorescein videoangiography data. The AATH fitting protocol was optimized through simulations using a more complex model (the AATH-vascular heterogeneity model, AATH-VH). It was determined that a two-step least square fitting method was more sensitive than a single-step least square fitting of AATH to simulated data to evaluate vascular permeability in early diabetic retinopathy. The optimized data analysis protocol was then evaluated in an initial clinical study comparing healthy control subjects to those with moderate non-proliferative DR. Volumetric blood flow and retinal vascular permeability maps were compared between patient groups with clear increases in extraction fraction observed in the mild NPDR patients compared to control. These promising early data have been the foundation to an ongoing 5 year study tracking 100 Diabetic patients with no DR so see if early changes in vascular permeability can predict which patients are more likely to progress to DR.
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Understanding and Combating Filter Bubbles in News Recommender Systems
Title
Understanding and Combating Filter Bubbles in News Recommender Systems
Creator
Liu, Ping
Date
2022
Description
Algorithmic personalization of news and social media content aims to improve user experience. However, there is evidence that this filtering...
Show moreAlgorithmic personalization of news and social media content aims to improve user experience. However, there is evidence that this filtering can have the unintended side effect of creating homogeneous ``filter bubbles'' in which users are over-exposed to ideas that conform with their pre-existing perceptions and beliefs. In this thesis, I investigate this phenomenon in political news recommendation algorithms, which have important implications for civil discourse.I first collect and curate a collection of over 900K news articles from over 40 sources. The dataset was annotated in the topic and partisan leaning dimensions by conducting an initial pilot study and later via Amazon Mturk. This dataset is studied and used consistently throughout this thesis. In the first part of the thesis, I conduct simulation studies to investigate how different algorithmic strategies affect filter bubble formation. Drawing on Pew studies of political typologies, we identify heterogeneous effects based on the user's pre-existing preferences. For example, I find that i) users with more extreme preferences are shown less diverse content but have higher click-through rates than users with less extreme preferences, ii) content-based and collaborative-filtering recommenders result in markedly different filter bubbles, and iii) when users have divergent views on different topics, recommenders tend to have a homogenization effect.Secondly, I conduct a content analysis of the news to understand language usage among and across various topics and political stances. I examine words and phrases used by the liberal media and by the conservative media on each topic. I first study what differentiates the liberal media from the conservative media on each topic. I then study common phrases that are used by the liberals and the conservatives on different topics. For example, I examine which phrases are shared by the liberal articles on guns and conservative articles on abortion. Finally, I compare and visualize these words using different clustering algorithms and supervised classification methods.In the last chapter, I conduct an extensive user study to find possible solutions to combat the filter bubbles in the political news recommender systems. I designed a self-contained website that enables a content-based news recommender system and indexed 40,000 U.S.~political articles. I recruited over 800 U.S.~participants from Amazon Mechanical Turk (approved by IRB). The qualified participants are split into control and treatment groups. The users in the treatment group are provided transparency and interaction mechanisms, which grant them more control over the recommendations. Our results show that providing interaction and transparency a) increases click-through rates, b) has the potential to reduce the filter bubbles, and c) raises more awareness about filter bubbles.
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PREDICTING AND SIMULATING OUTDOOR THERMAL COMFORT-BASED HUMAN BEHAVIOR IN URBAN ENVIRONMENTS
Title
PREDICTING AND SIMULATING OUTDOOR THERMAL COMFORT-BASED HUMAN BEHAVIOR IN URBAN ENVIRONMENTS
Creator
Khan, Zahida Marzaban
Date
2022
Description
Rapid urban growth due to a constant rise in world population has amplified the need for sustainable design development of cities. Human...
Show moreRapid urban growth due to a constant rise in world population has amplified the need for sustainable design development of cities. Human behavior, a key performance metric of sustainable design, can be rewarding for urban policies and city planning. Due to its complex nature, human behavior prediction and simulation are increasingly challenging. Complexity is associated with multiple factors, among which social and environmental factors are critical, especially in urban conditions with tall buildings that create unique microclimates. Human behavior in this study referred to human spatial behavior. This research hypothesized that the microclimatic variations at seasonal and diurnal levels affect people’s behavior in outdoor urban environments. Additionally, interdisciplinary crossover studies on novel methodologies to predict human behavior is becoming popular. Moreover, architects and urban designers are interested in human behavior simulation tools that can help them make informed design decisions through ‘what-if’ scenarios and assess their designs before execution. This doctoral research investigated the inter-relationship between Outdoor Thermal Comfort (OTC), human behavior, and urban morphology for Plazas in urban conditions with tall buildings and within a specific climate zone. The study focused on two overarching objectives: (1) to present a novel research methodology to investigate and predict OTC-based human behavior in urban conditions; and (2) to develop HuBeSIM - a human behavior simulation framework using an agent-based model (ABM) to simulate OTC integrated human behavior in outdoor spaces. Daley Plaza – an urban public space built-in 1965 in downtown Chicago — was used as (1) a case study to test the feasibility of this research methodology and (2) a pilot study to demonstrate the HuBeSIM framework. The outcome of this study shows a significant impact in the outdoor urban environments with design goals that use human behavior as a key performance indicator. The research contributes to the modeling and simulation of OTC-based human behavior in urban environments to nurture livable communities and sustainable cities. The first part of the dissertation presented a novel research methodology involving data collection through an on-site observational study for behavioral mapping, and microclimatic CFD simulations for OTC index - Physiological Equivalent Temperature (PET). The sample data consisted of two seasons, namely summer and fall, with more than 600 observations collected during the three-hour lunchtime period. The second part of the dissertation involved developing a Human Behavior SIMulation (HuBeSIM) framework in the popular computer aided design platform Rhino® and Grasshopper® (GH). This part integrated OTC using physics-based modeling and human behavior using mathematical agent-based modeling to develop a simulation framework for outdoor urban space design. The findings from the observational study revealed a moderate relationship between microclimate and human behavior in the fall, and a weak correlation in summer. The results showed that people’s behavior is not affected by PET values above 35°C. The proposed Human Behavior SIMulation framework has a high potential to develop into a comprehensive model by incorporating other behavioral factors. This study contributes to the sustainable built environment design that leads to the environmental, social, and economic upliftment of a city.
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