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Runtime-Assisted Cache Coherence Deactivation in Task Parallel Programs

机译:任务并行程序中的运行时辅助高速缓存一致性停用

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With increasing core counts, the scalability of directory-based cache coherence has become a challenging problem. To reduce the area and power needs of the directory, recent proposals reduce its size by classifying data as private or shared, and disable coherence for private data. However, existing classification methods suffer from inaccuracies and require complex hardware support with limited scalability. This paper proposes a hardware/software co-designed approach: the runtime system identifies data that is guaranteed by the programming model semantics to not require coherence and notifies the microarchitecture. The microarchitecture deactivates coherence for this private data and powers off unused directory capacity. Our proposal reduces directory accesses to just 26% of the baseline system, and supports a 64× smaller directory with only 2.8% performance degradation. By dynamically calibrating the directory size our proposal saves 86% of dynamic energy consumption in the directory without harming performance.
机译:随着核心数量的增加,基于目录的缓存一致性的可伸缩性已成为一个具有挑战性的问题。为了减少目录的面积和电源需求,最近的提案通过将数据分类为私有或共享来减小其大小,并禁用私有数据的一致性。但是,现有的分类方法存在不准确性,并且需要复杂的硬件支持和有限的可伸缩性。本文提出了一种硬件/软件协同设计的方法:运行时系统识别编程模型语义所保证的不需要一致性的数据,并通知微体系结构。微体系结构会取消此私有数据的一致性,并关闭未使用的目录容量。我们的建议将目录访问量减少到基准系统的26%,并支持将目录缩小64倍,而性能仅降低2.8%。通过动态校准目录大小,我们的建议可在不影响性能的情况下,在目录中节省86%的动态能耗。

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