CAREER: Compiler-Assisted Data Adaptation

职业：编译器辅助数据适应

• 批准号: 0238176
• 负责人: Chen Ding
• 金额: $ 40万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0238176&HistoricalAwards=false
• 关键词: CAREER; Compiler; Assisted; Data; Adaptation

Data management has become a core function of computing, stronglyinfluencing not only performance but also energy consumption, programsecurity, fault tolerance, and other program properties. While manyprograms use a large amount of data, they do not access all data atall times. Hence, data adaptation can improve cache efficiency byidentifying and compacting precisely those data required at a givenpoint of computation. Such fine-grained and dynamic data managementmay significantly alleviate the bottleneck on communication andstrengthen the control of data.This research studies fine-grained data adaptation through acompiler-based approach. It develops three compiler-assistedtechniques. First, program monitoring analyzes selected data; then,locality analysis identifies their group locality; and finally, datatransformation changes their layout while maintaining programcorrectness using run-time data maps. The new system improves cacheoptimization for data-intensive applications on conventional machinesand memory management for general-purpose programs on embeddeddevices. In parallel to research, the investigator developsdata-analysis tools to support teaching of memory issues in computerorganization, compiler, and programming courses. The investigatoralso develops an advanced course that focuses on memory optimizationtechniques at all system levels.

数据管理已成为计算，不仅强大的绩效，而且能源消耗，程序安全，容错性和其他程序属性的核心功能。 尽管许多程序都使用大量数据，但它们无法访问所有自动化时间的数据。 因此，数据适应可以通过识别和压实计算点所需的数据来提高缓存效率。 这种细粒度和动态数据管理可以显着减轻有关通信的瓶颈，并强度控制数据的控制。这项研究通过基于分解的方法进行了细粒度的数据适应。 它开发了三个编译器辅助技术。 首先，程序监视分析选定的数据；然后，局部分析确定了他们的群体位置。最后，DataTransformation改变了其布局，同时使用运行时数据映射维护程序校正。 新系统改善了用于嵌入式eves上通用程序的常规机器和内存管理数据密集型应用程序的可缓存。 与研究并行，研究人员开发了SDATA分析工具，以支持计算机组织，编译器和编程课程中记忆问题的教学。 研究人员开发了一个高级课程，该课程侧重于所有系统级别的内存优化技术。