Thesis (Ph. D.)--University of Wisconsin--Madison, 1954. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 76-82).

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Call number: LD2668 .T4 1960 Y39

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Typescript (photocopy). / Digitized by Kansas Correctional Industries

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Thesis (Ph. D.)--University of Wisconsin--Madison, 1985. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographies.

This paper focuses on developing and evaluating a bloat-free 3D game engine, prioritizingmemory efficiency, loading times, and overall performance. Optimizing softwareperformance becomes imperative with the ever-increasing complexity of software and thediminishing returns of Moore’s law. The prevalent use of design patterns, libraries, andlanguage features often prioritizes abstraction and scalability over performance, leadingto bloated software with memory inefficiencies and longer loading times.The presented research addresses this challenge by exploring the feasibility and potentialbenefits of designing a game engine with a bloat-free approach. The study formulatesproblem statements regarding memory utilization, CPU usage, time to export a game, andengine launch time, setting the foundation for developing the game engine, CogWheel.The evaluation of CogWheel involves comparative testing against industry-standardengines like Unity and Unreal Engine with the problem statements as a foundation.Results indicate promising outcomes for CogWheel, showing notable improvements intime-to-export, CPU usage, and memory utilization, especially in simpler projects.However, the thesis shows that the differences become more nuanced in more complexscenes, highlighting areas for further development and testing.

Large flexible software systems tend to incur “bloat”, here defined as the runtime overhead induced by the accumulation of excess functionality and objects. Removing bloat is hard as these overheads are a side-effect of the same trends that have fuelled software growth. Even defining and measuring bloat is non-trivial, as software doesn’t come with built-in labels that indicate which portions of computation are necessary for a given application and which lead to bloat. Much progress has been made in novel analysis tools that aid (human experts in) the process of finding bloat by highlighting signs of excessive activity and data flow. However, there has been very little research focus on understanding the connection between sources of bloat and its system level implications. In particular, excess resource usage due to bloat could be a significant source of power-performance inefficiencies, but the relation between bloat and energy efficient design remains unexplored. In order to systematically devise effective mechanisms for reaping power-performance benefits through bloat mitigation, we require a deeper insight into exactly when excess features can originate bloat, when the resource overheads of bloat are most pronounced and when bloat matters for power performance. This dissertation explores the problem of software bloat and its energy efficiency implications from multiple perspectives to develop a better understanding of these connections. First, we establish the need for a whole systems perspective in assessing potential energy efficiency benefits of bloat reduction, based on a systematic empirical and analytical study that highlights a curious interplay between bloat, energy proportionality and system bottlenecks. Second, we present a novel static analysis algorithm to perform an automated code transformation for object reuse that mitigates bloat involving the generation of excess temporary objects within loops. Third, we introduce the idea of concern augmented program analysis (CAPA), to identify sources of bloat due to excess features; the technique uses externally supplied information about program concerns and their properties as an abstraction of underlying intent. Fourth, as an early diagnostic aid, we use a statistical topic model to automatically discover latent concerns from source code statements and then correlate these latent concerns with resource usage properties that vary at statement granularity. The statistical model has a built-in sensitivity to the context of individual statements so that it can discover even diffused concerns without any apriori concern information. Together, our findings show that presence of excess features, in itself, may not lead to (runtime) bloat, unless these features have some structural interaction with essential features. Further, the overheads due to such structural interactions, in turn, may not cause substantial bloat in the resource consumption of a long running (server) application unless incurred repeatedly during program execution. Finally, even such bloated resource usage has a pronounced impact on power-performance only if it affects a system bottleneck or a hardware resource that has a high degree of energy proportionality and consumes a high fraction of power compared to the other system resources. We conclude that energy wastage due to bloat need not be an inevitable consequence of over-provisioning flexibility. Instead, the extent to which excess features result in runtime bloat and poor power-performance is determined by certain characteristics of the program structure and of the underlying hardware system --these represent potential control points that could be exercised to develop principled design approaches for mitigating bloat without sacrificing flexibility or productivity.

Call number: LD2668 .T4 1961 H39