LFOC：一种面向商用多核的轻量级公平性导向缓存聚类策略
LFOC: A Lightweight Fairness-Oriented Cache Clustering Policy for Commodity Multicores
AI总结：
提出LFOC，一种基于聚类的轻量级缓存分区方案，利用Intel CAT技术识别流式攻击程序和缓存敏感型应用并分配独立分区，在Linux内核和Intel Skylake上实现并评估，有效降低不公平性同时保持可接受的吞吐量。
摘要：
多核处理器是现代计算系统在不同市场领域的主要架构选择。尽管具有诸多优势，但当多个应用程序竞争使用核心间的共享资源（如末级缓存（LLC））时，自然出现的争用可能导致严重的性能下降。这可能对吞吐量和公平性等关键系统方面产生负面影响。将工作负载中的各种应用程序分配到大小可能不同的独立LLC分区中，已被证明能有效缓解共享资源争用的影响。在本文中，我们提出了LFOC，一种基于聚类的缓存分区方案，旨在提供公平性的同时提供可接受的系统吞吐量。LFOC利用Intel Cache Allocation Technology (CAT)，该技术使系统软件能够将LLC划分为不同分区。为实现其目标，LFOC尝试模拟最优缓存聚类解决方案的行为，我们通过模拟器在不同场景下近似了该方案。为此，LFOC有效识别流式攻击程序和缓存敏感型应用程序，随后将它们分配到独立的缓存分区。我们在Linux内核中实现了LFOC，并在配备Intel Skylake处理器的真实系统上进行了评估，将其有效性与两种分别优化公平性和吞吐量的最先进策略进行了比较。我们的实验分析表明，LFOC能够通过利用适合在真实OS中采用的轻量级算法，带来更高的不公平性降低。
英文摘要：
Multicore processors constitute the main architecture choice for modern computing systems in different market segments. Despite their benefits, the contention that naturally appears when multiple applications compete for the use of shared resources among cores, such as the last-level cache (LLC), may lead to substantial performance degradation. This may have a negative impact on key system aspects such as throughput and fairness. Assigning the various applications in the workload to separate LLC partitions with possibly different sizes, has been proven effective to mitigate shared-resource contention effects. In this article we propose LFOC, a clustering-based cache partitioning scheme that strives to deliver fairness while providing acceptable system throughput. LFOC leverages the Intel Cache Allocation Technology (CAT), which enables the system software to divide the LLC into different partitions. To accomplish its goals, LFOC tries to mimic the behavior of the optimal cache-clustering solution, which we could approximate by means of a simulator in different scenarios. To this end, LFOC effectively identifies streaming aggressor programs and cache sensitive applications, which are then assigned to separate cache partitions. We implemented LFOC in the Linux kernel and evaluated it on a real system featuring an Intel Skylake processor, where we compare its effectiveness to that of two state-of-the-art policies that optimize fairness and throughput, respectively. Our experimental analysis reveals that LFOC is able to bring a higher reduction in unfairness by leveraging a lightweight algorithm suitable for adoption in a real OS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
