Runtime-Guided Cache Coherence Optimizations in Multi-core Architectures

M. Manivannan, P. Stenström
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引用次数: 21

Abstract

Emerging task-based parallel programming models shield programmers from the daunting task of parallelism management by delegating the responsibility of mapping and scheduling of individual tasks to the runtime system. The runtime system can use semantic information about task dependencies supplied by the programmer and the mapping information of tasks to enable optimizations like data-flow based execution and locality-aware scheduling of tasks. However, should the cache coherence substrate have access to this information from the runtime system, it would enable aggressive optimizations of prevailing access patterns such as one-to-many producer-consumer sharing and migratory sharing. Such linkage has however not been studied before. We present a family of runtime guided cache coherence optimizations enabled by linking dependency and mapping information from the runtime system to the cache coherence substrate. By making this information available to the cache coherence substrate, we show that optimizations, such as downgrading and self-invalidation, that help reducing overheads associated with producer-consumer and migratory sharing can be supported with reasonable extensions to the baseline cache coherence protocol. Our experimental results establish that each optimization provides significant performance gain in isolation and can provide additional gains when combined. Finally, we evaluate these optimizations in the context of earlier proposed runtime-guided prefetching schemes and show that they can have synergistic effects.
运行时导向的多核架构缓存一致性优化
新兴的基于任务的并行编程模型通过将单个任务的映射和调度的责任委托给运行时系统,使程序员免受并行性管理的艰巨任务。运行时系统可以使用程序员提供的关于任务依赖关系的语义信息和任务的映射信息来实现诸如基于数据流的执行和任务的位置感知调度之类的优化。然而,如果缓存一致性底层可以从运行时系统访问这些信息,那么它将支持对主流访问模式的积极优化,例如一对多的生产者-消费者共享和迁移共享。然而,这种联系以前没有被研究过。我们提出了一系列运行时引导的缓存一致性优化,通过将依赖关系和映射信息从运行时系统链接到缓存一致性基板来实现。通过将这些信息提供给缓存一致性底层,我们表明,可以通过对基线缓存一致性协议的合理扩展来支持优化,例如降级和自我失效,这些优化有助于减少与生产者-消费者和迁移共享相关的开销。我们的实验结果表明,每个优化单独提供了显著的性能增益,并且在组合时可以提供额外的增益。最后,我们在先前提出的运行时导向预取方案的背景下评估了这些优化,并表明它们可以具有协同效应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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