SHF: Small: Algorithms for Dynamic Analysis of Run-Time Bloat

SHF：小：运行时膨胀动态分析算法

• 批准号: 1017204
• 负责人: Atanas Rountev
• 金额: $ 35.65万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017204&HistoricalAwards=false
• 关键词: SHF; Small; Algorithms; Dynamic; Analysis

Many object-oriented programs, and in particular large enterprise applications, suffer from chronic run-time bloat: the excessive memory usage and run-time work that occur as part of seemingly simple computations. Such bloat significantly affects scalability and performance, and presents a serious problem for software used every day by thousands of businesses. Performance tuning may find substantial optimization opportunities, but it is very labor-intensive and requires a great deal of skill.This project develops novel algorithms for run-time analysis of Java programs to identify the symptoms of bloat and to pinpoint their causes. These algorithms can be used in checking tools during software development, and later in tuning tools for performance debugging. A framework for algorithm design and implementation defines a generalized form of a run-time data dependence graph, with abstractions specific to the targeted analysis, and with several dimensions of parameterization. Framework instances are used to create analyses of temporary data structures, analyses of copy chains, data structure cost-benefit analyses, and analyses of inefficiently-used containers. Experimental evaluation investigates the cost/precision trade-offs in the design and implementation of these algorithms, and their effectiveness in helping a programmer to improve performance.The project provides a foundation for systematic exploration of bloat analyses, which will help increase software performance and reduce tuning efforts. The framework and its instances will be made publicly available. These advances could become part of development toolkits, leading to higher performance of enterprise applications. Educational efforts will contribute to the skills of the next generation of developers of enterprise systems.

许多面向对象的程序，尤其是大型企业应用程序都遭受了慢性运行时膨胀：作为看似简单的计算的一部分发生的过度记忆使用和运行时工作。这种膨胀会显着影响可扩展性和性能，并给数千个企业每天使用的软件带来一个严重的问题。性能调整可能会发现大量优化机会，但是它非常密集，需要大量技能。该项目开发了新的算法，用于对Java计划进行运行时间分析，以识别膨胀的症状并确定其原因。这些算法可用于在软件开发过程中检查工具，然后在调整性能调试工具中使用。算法设计和实现的框架定义了运行时数据依赖图的广义形式，其抽象特定于目标分析，并且具有几个参数化的维度。框架实例用于创建临时数据结构的分析，副本链的分析，数据结构成本效益分析以及对低效率容器的分析。实验评估调查了这些算法的设计和实施中的成本/精确权衡，及其在帮助程序员提高性能方面的有效性。该项目为对膨胀分析的系统探索提供了基础，这将有助于提高软件性能并减少调谐工作。框架及其实例将公开可用。这些进步可能成为开发工具包的一部分，从而导致企业应用程序的更高性能。教育工作将有助于企业系统的下一代开发人员的技能。