CPA-CPL: Exploring and Exploiting Heterogeneous Cache Sharing in Chip Multiprocessors Systems for Locality Optimization and Proactive Cache Management

CPA-CPL：探索和利用芯片多处理器系统中的异构缓存共享，实现局部优化和主动缓存管理

• 批准号: 0811791
• 负责人: Xipeng Shen
• 金额: $ 29万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811791&HistoricalAwards=false
• 关键词: CPA; CPL; Exploring; Exploiting; Heterogeneous

The increasing problems of power, heat dissipation, and designcomplexity have caused a shift in processor technology to favormulticore multiprocessors. Along with that shift, the sharing ofmemory hierarchy becomes deeper, heterogeneous and more complex,causing cache contention, increased conflicts, and also, synergysharing. Without understanding the implications of this change,current multicore systems suffer from considerable performancedegradation, poor performance isolation and inferior fairnessguarantees. The urgency of these issues increases as the degree ofprocessor-level parallelism increments rapidly.Prior studies, mostly in areas of architecture and operating systems,rely on simple heuristics to estimate cache requirement of corunningprograms; the inaccuracy and overhead limits their scalability andeffectiveness. This work tackles these challenges uniquely from thecompiler aspect by constructing predictive behavior models forcorunning processes, developing cache-sharing-aware programtransformations and loop scheduling, and combining the program-levelknowledge of programming systems with the proactive resourcemanagement by runtime systems. Specifically, this work proposesinclusive reuse signatures to characterize inclusive locality---thememory behavior of corunning programs on shared caches, andinter-thread affinity models to capture data locality among parallelthreads. It tackles the challenges facing the measurement, predictionand exploitation of inclusive locality. The analysis opens newopportunities for shared-cache optimizations by both compilers andruntime systems. The PI develops a series of program transformations,such as inter-thread memory reorganization and cache-sharing awareloop scheduling, to increase inter-thread spacial locality andameliorate conflicts, contention and false sharing. For runtimesystems, this work invents proactive cache management which partitionscaches or schedules processes according to predicted inclusivelocality proactively, overcoming the limitations of current reactiveschemes on scalability, accuracy and effectiveness.

功耗、散热和设计复杂性等问题日益严重，导致处理器技术转向多核多处理器。随着这种转变，内存层次结构的共享变得更深、更异构、更复杂，导致缓存争用、冲突增加以及协同共享。 如果不了解这一变化的影响，当前的多核系统就会遭受相当大的性能下降、性能隔离差和公平性保证较差的问题。 随着处理器级并行度的迅速增加，这些问题的紧迫性也随之增加。先前的研究主要集中在体系结构和操作系统领域，依靠简单的启发式方法来估计同时运行的程序的缓存需求。不准确性和开销限制了它们的可扩展性和有效性。 这项工作从编译器方面独特地解决了这些挑战，通过为运行进程构建预测行为模型，开发缓存共享感知的程序转换和循环调度，并将编程系统的程序级知识与运行时系统的主动资源管理相结合。具体来说，这项工作提出了包容性重用签名来表征包容性局部性——在共享缓存上运行程序的内存行为，以及用于捕获并行线程之间的数据局部性的线程间亲和性模型。它解决了包容性地方的测量、预测和开发所面临的挑战。该分析为编译器和运行时系统的共享缓存优化开辟了新的机会。 PI开发了一系列程序转换，例如线程间内存重组和缓存共享感知循环调度，以增加线程间空间局部性并改善冲突、争用和错误共享。对于运行时系统，这项工作发明了主动缓存管理，它根据预测的包含局部性主动对缓存进行分区或调度进程，克服了当前反应式方案在可扩展性、准确性和有效性方面的局限性。