Scalable Performance and Power-Aware Hybrid Compilation System for Multicores

适用于多核的可扩展性能和功耗感知混合编译系统

• 批准号: 0702775
• 负责人: Barbara Chapman
• 金额: --
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-05-15 至 2011-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0702775&HistoricalAwards=false
• 关键词: Scalable; Performance; Power; Aware; Hybrid

A large body of existing, sequential applications must be migrated to multicore platforms with maximum transparency. To achieve this, techniques for automatic parallelization must be pushed to their limits, and current shared memory programming paradigms must be reconsidered with respect to their ease of use and ability to support a wide range of programming needs. The potential for subtle, hard-to-find bugs such as data race conditions needs to be minimized and strategies devised to ensure that remaining errors are caught at run time. In this project, the investigator builds on top of existing compiler technology for automatic parallelization and OpenMP translation in order to facilitate application development for multicore systems. To do so, a novel translation strategy is developed for this hybrid sequential/directive-based programming model that takes characteristics of multicore platforms into account as well as dealing with power conservation constraints. The approach minimizes the likelihood of erroneous code and relies on the compilation system to reduce synchronization, improve the load balance and otherwise adapt the program to the system. The investigator develops strategies to gather both coarse-grain and fine-grain performance information for selected program regions with low overheads. The project also creates techniques to exploit this information dynamically, and to adapt and optimize based on different levels of program representation. The ideas will be implemented and deployed via an infrastructure that combines a robust, open source compiler, its run time system and the corresponding dynamic compiler into a cohesive environment to support the translation and execution of sequential and directive-based programs.

大量现有的顺序应用程序必须以最大的透明度迁移到多核平台。为了实现这一点，自动并行化技术必须推到他们的极限，和当前的共享内存编程范式必须重新考虑其易用性和能力，以支持广泛的编程需求。需要最大限度地减少细微的、难以发现的错误（如数据竞争条件）的可能性，并设计策略以确保在运行时捕获剩余的错误。在这个项目中，研究人员建立在现有的编译器技术之上，用于自动并行化和OpenMP翻译，以促进多核系统的应用程序开发。要做到这一点，一种新的翻译策略开发的混合顺序/基于指令的编程模型，考虑到多核平台的特点，以及处理与节能约束。该方法最小化了错误代码的可能性，并依赖于编译系统来减少同步，改善负载平衡，以及以其他方式使程序适应系统。调查员制定策略，收集粗粒度和细粒度的性能信息，为选定的程序区域低开销。该项目还创建了动态利用这些信息的技术，并根据不同的程序表示级别进行调整和优化。这些想法将通过一个基础设施来实现和部署，该基础设施将一个强大的开源编译器，其运行时系统和相应的动态编译器结合到一个有凝聚力的环境中，以支持顺序和基于指令的程序的翻译和执行。