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.

功率，散热和设计复杂性的越来越多的问题导致处理器技术向热爱的多处理器转移。随着这一转变，会员层次结构的共享变得更深，异构和更复杂，引起了缓存的争论，增加了冲突以及协调性。 在不了解这种变化的含义的情况下，当前的多核心系统遭受了相当大的性能，性能隔离和劣等保证。 这些问题的紧迫性随着处理器级并行性的程度迅速增加而增加。在建筑和操作系统领域中，研究依赖于简单的启发式方法来估计CACHE的CACHE要求。不准确和间接费用限制了它们的可扩展性和效率。 这项工作通过构建预测行为模型，构建流程，开发缓存 - 分享意识的程序传输和循环调度，以及将编程系统的程序级别的知识与运行时系统的主动资源管理相结合，从而解决了这些挑战的独特挑战。具体而言，这项工作提出了包括重复使用的签名来表征包容性局部性 - - 在共享scaches，和Inter-thinead Affinity模型上旋转程序的核心行为，以捕获并行线程之间的数据局部性。它解决了衡量标准，预测和剥削包容性地区面临的挑战。该分析为两个编译器和跑步系统的共享调查优化开放。 PI开发了一系列的程序转换，例如线际上的内存重组和缓存共享awareloop调度，以增加线程间的空间区域和省略冲突，争论和错误共享。对于Runtimessystems，这项工作会主动根据预测的包含性范围进行分区或调度的积极缓存管理，从而积极地克服了当前反应性的局限性对可伸缩性，准确性和有效性的限制。