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

功率、散热和设计复杂性的问题日益增加，导致处理器技术向多核多处理器的转变。沿着这种转变，内存层次的共享变得更深、异构和更复杂，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，目前的多核系统遭受相当大的性能下降，性能隔离差和公平性保证较差。 这些问题的紧迫性随着处理器级并行度的迅速增加而增加。以前的研究，主要是在体系结构和操作系统领域，依靠简单的算法来估计corunningprogram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解决了这些挑战，通过构建corunning进程的预测行为模型，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。具体而言，这项工作提出了包容性重用签名来表征包容性局部性-共享缓存上的内存行为，以及线程间的亲和力模型来捕获并行线程之间的数据局部性。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提供了新的机会。 PI提出了一系列的程序转换，如线程间内存重组和缓存共享awareloop调度，以增加线程间的空间局部性，改善冲突、争用和假共享。对于运行时系统，这项工作发明了主动缓存管理partitionscaches或调度进程根据预测的inclusivelocality主动，克服了目前的reactiveschemes的可扩展性，准确性和有效性的限制。

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.