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，特别是大型企业应用程序，都有长期运行时膨胀的问题：在看似简单的计算中，会出现过多的内存使用和运行时工作。这种膨胀严重影响了可伸缩性和性能，并为成千上万的企业每天使用的软件带来了严重的问题。性能调优可能会发现大量的优化机会，但它是非常劳动密集型的，需要大量的技能。这个项目开发了新的算法，用于Java程序的运行时分析，以识别膨胀的症状，并查明其原因。这些算法可以在软件开发过程中用于检查工具，也可以在以后用于性能调试的调优工具。算法设计和实现的框架定义了运行时数据依赖图的一般形式，具有特定于目标分析的抽象，以及参数化的几个维度。框架实例用于创建临时数据结构分析、复制链分析、数据结构成本效益分析以及低效使用容器分析。实验评估研究这些算法的设计和实现中的成本/精度权衡，以及它们在帮助程序员提高性能方面的有效性。该项目为系统探索膨胀分析提供了基础，这将有助于提高软件性能并减少调优工作。该框架及其实例将公开提供。这些进步可以成为开发工具包的一部分，从而提高企业应用程序的性能。教育工作将有助于提高下一代企业系统开发人员的技能。