CcNav: Understanding Compiler Optimizations in Binary Code

Program developers spend significant time on optimizing and tuning programs. During this iterative process, they apply optimizations, analyze the resulting code, and modify the compilation until they are satisfied. Understanding what the compiler did with the code is crucial to this process but is very time-consuming and labor-intensive. Users need to navigate through thousands of lines of binary code and correlate it to source code concepts to understand the results of the compilation and to identify optimizations. We present a design study in collaboration with program developers and performance analysts. Our collaborators work with various artifacts related to the program such as binary code, source code, control flow graphs, and call graphs. Through interviews, feedback, and pair-analytics sessions, we analyzed their tasks and workflow. Based on this task analysis and through a human-centric design process, we designed a visual analytics system Compilation Navigator (CcNav) to aid exploration of the effects of compiler optimizations on the program. CcNav provides a streamlined workflow and a unified context that integrates disparate artifacts. CcNav supports consistent interactions across all the artifacts making it easy to correlate binary code with source code concepts. CcNav enables users to navigate and filter large binary code to identify and summarize optimizations such as inlining, vectorization, loop unrolling, and code hoisting. We evaluate CcNav through guided sessions and semi-structured interviews. We reflect on our design process, particularly the immersive elements, and on the transferability of design studies through our experience with a previous design study on program analysis.Comment: IEEE VIS VAST 202

Performance Optimization Strategies for Transactional Memory Applications

This thesis presents tools for Transactional Memory (TM) applications that cover multiple TM systems (Software, Hardware, and hybrid TM) and use information of all different layers of the TM software stack. Therefore, this thesis addresses a number of challenges to extract static information, information about the run time behavior, and expert-level knowledge to develop these new methods and strategies for the optimization of TM applications

Doctor of Philosophy

dissertationMessage passing (MP) has gained a widespread adoption over the years, so much so, that even heterogeneous embedded multicore systems are running programs that are developed using message passing libraries. Such a phenomenon is a shift in computing practices, since, traditionally MP programs have been developed specifically for high performance computing. With growing importance and the complexity of MP programs in today's times, it becomes absolutely imperative to have formal tools and sound methodologies that can help reason about the correctness of the program. It has been demonstrated by many researchers in the area of concurrent program verification that a suitable strategy to verify programs which rely heavily on nondeterminism, is dynamic verification. Dynamic verification integrates the best features of testing and model checking. In the area of MP program verification, however, there have been only a handful of dynamic verifiers. These dynamic verifiers, despite their strengths, suffer from the explosion in execution scenarios. All existing dynamic verifiers, to our knowledge, exhaustively explore the nondeterministic choices in an MP program. It is apparent that an MP program with many nondeterministic constructs will quickly inundate such tools. This dissertation focuses on the problem of containing the exponential space of execution scenarios (or interleavings) while providing a soundness and completeness guarantee over safety properties of MP programs (specifically deadlocks). We present a predictive verification methodology and an associated framework, called MAAPED(Messaging Application Analysis with Predictive Error Discovery), that operates in polynomial time over MP programs to detect deadlocks among other safety property violations. In brief, we collect a single execution trace of an MP program and without re-running other execution schedules, reliably construct the artifacts necessary to predict any mishappening in an unexplored execution schedule with the aforementioned formal guarantee. The main contributions of the thesis are the following: The Functionally Irrelevant Barrier Algorithm to increase program productivity and ease in verification complexity. A sound pragmatic strategy to reduce the interleaving space of existing dynamic verifiers which is complete only for a certain class of MPI programs. A generalized matches-before ordering for MP programs. A predictive polynomial time verification framework as an alternate solution in the dynamic MP verification landscape. A soundness and completeness proof for the predictive framework's deadlock detection strategy for many formally characterized classes of MP programs. In the process of developing solutions that are mentioned above, we also collected important experiences relating to the development of dynamic verification schedulers. We present those experiences as a minor contribution of this thesis

Breaking Computational Barriers to Perform Time Series Pattern Mining at Scale and at the Edge

Uncovering repeated behavior in time series is an important problem in many domains such as medicine, geophysics, meteorology, and many more. With the continuing surge of smart/embedded devices generating time series data, there is an ever growing need to perform analysis on datasets of increasing size. Additionally, there is an increasing need for analysis at low power edge devices due to latency problems inherent to the speed of light and the sheer amount of data being recorded. The matrix profile has proven to be a tool highly suitable for pattern mining in time series; however, a naive approach to computing the matrix profile makes it impossible to use effectively in both the cloud and at the edge. This dissertation shows how, through the use of GPUs and machine learning, the matrix profile is computed more feasibly, both at cloud-scale and at sensor-scale. In addition, it illustrates why both of these types of computation are important and what new insights they can provide to practitioners working with time series data

Septo-Optic Dysplasia: Educational Issues

Ninety-nine articles published in professional journals related to septo-optic dysplasia (SOD) or the education of young blind children were reviewed by a special education teacher and parent of a blind one year old boy diagnosed with SOD. The articles were classified by publication type (e.g., research studies, descriptive articles, guides, position papers, reviews of literature). Fifty-three of the 99 articles were research studies; these 53 research studies were classified by research design (i.e., quantitative, qualitative, or mixed methods); the participants and data sources of each study were identified; and the findings of each study were summarized. All 99 articles were then analyzed using a modified version of the Stevick-Callaizi-Keen method to draw out the essential themes of this body of literature. The 11 themes that emerged from this analysis included: (a) septo-optic dysplasia and optic nerve hypoplasia; (b) parenting and early intervention; (c) cognitive development; (d) language development; (e) orientation and mobility; (f) social behavior; (g) assistive technology; (h) educational placement; (i) emergent literacy; (j) Braille literacy; and (k) assessment. These themes were then considered from the author's roles of parent and teacher

Symptoms, Causes, and Coping Strategies for Performance Anxiety in Singers: A Synthesis of Research

A multitude of studies exists concerning performance anxiety, yet the number specifically focused upon singers is lacking in the literature. This study, intended as a reference guide for singers and singing instructors, discusses the symptoms, possible causes, and currently accepted coping strategies for music performance anxiety (MPA). An analysis of the published scholarship and correspondence from singers and professionals are included, as well as an appendix listing self-help sources and an extensive bibliography for further research. The study serves as an aid to singers and singing instructors seeking concise information on the topic. Specific steps within each coping method are not included, as the effectiveness of these can be unique to each individual. A survey of the most recent research suggests that more studies focused on singers are needed, as the voice is unlike any other instrument due to the fact that it resides within the body, and thus the efficacy of various treatments may be different for singers

Capillarity: A Theory Of Mlearning And Its Application In Emerging Markets

The theory of capillarity is an organic metaphor invoked to explore the role of network communications as a vehicle for education in a healthy society. Capillarity is offered as a theory in two parts: a mechanism for distribution and a method for engagement. Capillarity seeks to build an architecture that reflects radical humanism\u27s emphasis on access and inclusion as a vehicle for classical humanism\u27s emphasis on the individual expansion of potential and consciousness. The technical instrument whereby this program of humanist education may be deployed is mLearnin

Generative AI in the Construction Industry: A State-of-the-art Analysis

The construction industry is a vital sector of the global economy, but it faces many productivity challenges in various processes, such as design, planning, procurement, inspection, and maintenance. Generative artificial intelligence (AI), which can create novel and realistic data or content, such as text, image, video, or code, based on some input or prior knowledge, offers innovative and disruptive solutions to address these challenges. However, there is a gap in the literature on the current state, opportunities, and challenges of generative AI in the construction industry. This study aims to fill this gap by providing a state-of-the-art analysis of generative AI in construction, with three objectives: (1) to review and categorize the existing and emerging generative AI opportunities and challenges in the construction industry; (2) to propose a framework for construction firms to build customized generative AI solutions using their own data, comprising steps such as data collection, dataset curation, training custom large language model (LLM), model evaluation, and deployment; and (3) to demonstrate the framework via a case study of developing a generative model for querying contract documents. The results show that retrieval augmented generation (RAG) improves the baseline LLM by 5.2, 9.4, and 4.8% in terms of quality, relevance, and reproducibility. This study provides academics and construction professionals with a comprehensive analysis and practical framework to guide the adoption of generative AI techniques to enhance productivity, quality, safety, and sustainability across the construction industry.Comment: 74 pages, 11 figures, 20 table