回顾:区域感知缓存分区

Karthik T. Sundararajan, Timothy M. Jones, N. Topham
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引用次数: 15

摘要

近年来,高性能计算系统获得了更多的处理内核,并共享最后一级缓存(LLC)。然而,随着它们数量的增长,LLC中的核路比也会增加,这给现有的缓存分区技术带来了问题,因为它们需要的路比核多。此外,由于有限责任公司的规模,有效的能源管理变得越来越重要。本文提出了一种区域感知缓存分区(RECAP),这是一种适用于高性能多核处理器的有限责任节能方案。RECAP将缓存中的数据划分为共享区域和私有区域。应用程序只访问包含其所需数据的路径,从而实现动态节能。任何不在共享或私有区域内的方式都可以关闭以节省静态能源。我们使用运行多程序工作负载的8核CMP来评估我们的方案,并表明它在共享LLC中实现了17%的动态节能和13%的静态节能,并获得了15%的性能提升。在我们的多线程应用程序中,我们实现了17%的动态节能和41%的静态节能,而对性能没有影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RECAP: Region-Aware Cache Partitioning
In recent years, high performance computing systems have obtained more processing cores and share a last level cache (LLC). However, as their number grows, the core-to-way ratio in the LLC increases, presenting problems to existing cache partitioning techniques which require more ways than cores. Furthermore, effective energy management of the LLC becomes increasingly important due to its size. This paper proposes a Region Aware Cache Partitioning (RECAP), an LLC energy-saving scheme for high-performance, many-core processors. RECAP partitions the data within the cache into shared and private regions. Applications only access the ways containing the data that they require, realising dynamic energy savings. Any ways that are not within the shared or private regions can be turned off to save static energy. We evaluate our scheme using an 8-core CMP running multi-programmed workloads and show that it achieves 17% dynamic and 13% static energy savings in the shared LLC with a 15% performance gain. Across our multi-threaded applications, we achieve 17% dynamic and 41% static energy savings with no impact on performance.
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