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Full-Text Articles in Physical Sciences and Mathematics
Data-Driven Reflectance Estimation Under Natural Lighting, Victoria Cooper
Data-Driven Reflectance Estimation Under Natural Lighting, Victoria Cooper
Dissertations, Theses, and Masters Projects
Bidirectional Reflectance Distribution Functions, (BRDFs), describe how light is reflected off of a material. BRDFs are captured so that the materials can be re-lit under new while maintaining accuracy. BRDF models can approximate the reflectance of a material, but are unable to accurately represent the full BRDF of the material. Acquisition setups for BRDFs trade accuracy for speed with the most accurate methods, gonioreflectometers, being the slowest. Image-based BRDF acquisition approaches range from using complicated controlled lighting setups to uncontrolled known lighting to assuming the lighting is unknown. We propose a data-driven method for recovering BRDFs under known, but uncontrolled …
Performance Optimization With An Integrated View Of Compiler And Application Knowledge, Ruiqin Tian
Performance Optimization With An Integrated View Of Compiler And Application Knowledge, Ruiqin Tian
Dissertations, Theses, and Masters Projects
Compiler optimization is a long-standing research field that enhances program performance with a set of rigorous code analyses and transformations. Traditional compiler optimization focuses on general programs or program structures without considering too much high-level application operations or data structure knowledge. In this thesis, we claim that an integrated view of the application and compiler is helpful to further improve program performance. Particularly, we study integrated optimization opportunities for three kinds of applications: irregular tree-based query processing systems such as B+ tree, security enhancement such as buffer overflow protection, and tensor/matrix-based linear algebra computation. The performance of B+ tree query …
Low-Overhead Techniques For Secure And Reliable Gpu Computing, Gurunath Kadam
Low-Overhead Techniques For Secure And Reliable Gpu Computing, Gurunath Kadam
Dissertations, Theses, and Masters Projects
In recent years, Graphics Processing Units (GPUs) have become a de facto choice to accelerate the computations in various domains such as machine learning, security, financial and scientific computing. GPUs leverage the inherent data parallelism in the target applications to provide high throughput at superior energy efficiency. Due to the rising usage of GPUs for a large number of applications, they are facing new challenges, especially in the security and reliability domains. From the security side, recently several microarchitectural attacks targeting GPUs have been demonstrated. These attacks leak the secret information stored on GPUs, for example, the parameters of a …
Epidemic Spread Modeling For Covid-19 Using Hard Data, Anna Schmedding
Epidemic Spread Modeling For Covid-19 Using Hard Data, Anna Schmedding
Dissertations, Theses, and Masters Projects
We present an individual-centric model for COVID-19 spread in an urban setting. We first analyze patient and route data of infected patients from January 20, 2020 ,to May 31, 2020, collected by the Korean Center for Disease Control & Prevention (KCDC) and illustrate how infection clusters develop as a function of time. This analysis offers a statistical characterization of mobility habits and patterns of individuals. We use this characterization to parameterize agent-based simulations that capture the spread of the disease, we evaluate simulation predictions with ground truth, and we evaluate different what-if counter-measure scenarios. Although the presented agent-based model is …
Combining Performance Profiling And Modeling For Accuracy And Efficiency, Hao Xu
Combining Performance Profiling And Modeling For Accuracy And Efficiency, Hao Xu
Dissertations, Theses, and Masters Projects
Modern computer systems have evolved to employ powerful parallel architectures, including multi-core processors, multi-socket chips, large memory subsystems, and fast network communication. Given such powerful hardware, developers rely on performance profiling and modeling to guide their performance optimization. However, performance optimization is facing new challenges on efficiency and accuracy with emerging computer systems. In this dissertation, we propose approaches to address these challenges. We first study memory contention in Non-Uniform Memory Access (NUMA) architectures. We present DR-BW, a new tool based on machine learning to identify bandwidth contention in NUMA architectures and provide optimization guidance. DR-BW collects performance data with …
On Supporting Android Software Developers And Testers, Carlos Eduardo Bernal Cardenas
On Supporting Android Software Developers And Testers, Carlos Eduardo Bernal Cardenas
Dissertations, Theses, and Masters Projects
Users entrust mobile applications (apps) to help them with different tasks in their daily lives. However, for each app that helps to finish a given task, there are a plethora of other apps in popular marketplaces that offer similar or nearly identical functionality. This makes for a competitive market where users will tend to favor the highest quality apps in most cases. Given that users can easily get frustrated by apps which repeatedly exhibit bugs, failures, and crashes, it is imperative that developers promptly fix problems both before and after the release. However, implementing and maintaining high quality apps is …
Rethinking Cache Hierarchy And Interconnect Design For Next-Generation Gpus, Mohamed Assem Abd Elmohsen Ibrahim
Rethinking Cache Hierarchy And Interconnect Design For Next-Generation Gpus, Mohamed Assem Abd Elmohsen Ibrahim
Dissertations, Theses, and Masters Projects
To match the increasing computational demands of GPGPU applications and to improve peak compute throughput, the core counts in GPUs have been increasing with every generation. However, the famous memory wall is a major performance determinant in GPUs. In other words, in most cases, peak throughput in GPUs is ultimately dictated by memory bandwidth. Therefore, to serve the memory demands of thousands of concurrently executing threads, GPUs are equipped with several sources of bandwidth such as on-chip private/shared caching resources and off-chip high bandwidth memories. However, the existing sources of bandwidth are often not sufficient for achieving optimal GPU performance. …
Distributed Byzantine Tolerant Machine Learning, Qi Xia
Distributed Byzantine Tolerant Machine Learning, Qi Xia
Dissertations, Theses, and Masters Projects
Oftentimes, training a large-scale deep learning neural network on a single machine becomes more difficult in a complex network model. Distributed training provides an efficient solution, but opens up participating workers to Byzantine attacks. This problem emerges when some workers cheat during uploading gradients or weights to the central server, e.g., the information received by the server is not always the true result computed by workers. In order to address this problem, we investigate Byzantine problems in distributed machine learning and respectively defend against these kinds of attacks in three scenarios: i) classic distributed machine learning; ii) federated learning; and …
Revisiting Isolation For System Security And Efficiency In The Era Of Internet Of Things, Lele Ma
Revisiting Isolation For System Security And Efficiency In The Era Of Internet Of Things, Lele Ma
Dissertations, Theses, and Masters Projects
Isolation is a fundamental paradigm for secure and efficient resource sharing on a computer system. However, isolation mechanisms in traditional cloud computing platforms are heavy-weight or just not feasible to be applied onto the computing environment for Internet of Things(IoT). Most IoT devices have limited resources and their servers are less powerful than cloud servers but are widely distributed over the edge of the Internet. Revisions to the traditional isolation mechanisms are needed in order to improve the system security and efficiency in these computing environments. The first project explores container-based isolation for the emerging edge computing platforms. We show …
High-Dimensional Machine Learning Models In Fintech, Qiong Wu
High-Dimensional Machine Learning Models In Fintech, Qiong Wu
Dissertations, Theses, and Masters Projects
This thesis develops several forecasting models for simultaneously predicting the prices of d assets traded in financial markets, a most fundamental problem in the emerging area of ``FinTech''. The models are optimized to address three critical challenges, C1. High-dimensional interactions between assets. Assets could interact (e.g., Amazon's disclosure of its revenue change in cloud services could indicate that revenues also could change in other cloud providers). The number of possible interactions is quadratic in d, and is often much larger than the number of observations. C2. Non-linearity of the hypothesis class. Linear models are usually insufficient to characterize the relationship …