CSR: Small: A Practical Data Dependence Profiler for Program Characterization and Optimization

CSR：Small：用于程序表征和优化的实用数据依赖性分析器

• 批准号: 1320356
• 负责人: James Tuck
• 金额: $ 38.41万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320356&HistoricalAwards=false
• 关键词: CSR; Small; Practical; Data; Dependence

Society relies on an ever broadening array of computer systems for productivity, communication, entertainment, safety, and health. Systems capable of processing at faster rates and with greater efficiency are necessary to sustain the pace of innovation. A key part of achieving this goal is the development of tools and techniques that make it easier to build sophisticated software with a desired set of requirements. One aspect of these tools, the focus of this work, is data dependence profiling. A data dependence profiler (DDP) conveys to a programmer, compiler, or other program analysis tool the likelihood of a data dependence between two arbitrary memory operations while the program is running. DDPs are critical since compilers and programmers often do not know or cannot determine all such relationships simply by analyzing the source code; hence, DDPs provide important information for further optimization and tuning.This project focuses on the design of a fast practical DDP that works effectively for a wide range of applications and for a wide range of program analysis needs. The first goal is speed: that the DDP impose only a small slowdown, the target being a factor of two. A second goal is to maintain accuracy: bounding the uncertainty and imprecision inherent in profiling, and providing information about the accuracy with the profiler feedback. The third goal is integration of the DDP into a feedback-directed optimization framework, to explore and understand its capabilities.If the goals of the project are met, DDPs are expected to become more widely integrated into program development tools in support of existing technology and enabling new technologies that ultimately will benefit society. Open source distribution of the tools developed by the project strengthens and extends the available open-source software infrastructure relied upon by both academia and industry. The project integrates education with research through involvement of graduate, undergraduate, and high school students.

社会依赖于越来越多的计算机系统来实现生产力、通信、娱乐、安全和健康。能够以更快的速度和更高的效率进行处理的系统是保持创新步伐所必需的。实现这一目标的一个关键部分是开发工具和技术，使其更容易构建具有所需需求集的复杂软件。 这些工具的一个方面，这项工作的重点，是数据依赖性分析。数据依赖分析器（DDP）向程序员、编译器或其他程序分析工具传达在程序运行时两个任意存储器操作之间的数据依赖的可能性。DDP是至关重要的，因为编译器和程序员往往不知道或不能确定所有这些关系，简单地通过分析源代码，因此，DDP提供了重要的信息，进一步优化和tuning.This项目的重点是设计一个快速实用的DDP，有效地适用于广泛的应用程序和广泛的程序分析需求。 第一个目标是速度：DDP只实施小幅放缓，目标是两倍。 第二个目标是保持准确性：限制剖析中固有的不确定性和不精确性，并通过剖析器反馈提供有关准确性的信息。第三个目标是将DDP集成到反馈导向的优化框架中，以探索和理解其功能。如果该项目的目标得以实现，DDP有望更广泛地集成到程序开发工具中，以支持现有技术并实现最终造福社会的新技术。 该项目开发的工具的开放源码分发加强和扩大了学术界和工业界所依赖的现有开放源码软件基础设施。 该项目通过研究生、本科生和高中生的参与，将教育与研究结合起来。