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首页> 外文期刊>ACM Transactions on Architecture and Code Optimization >Adaptive Timekeeping Replacement: Fine-Grained Capacity Management for Shared CMP Caches
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Adaptive Timekeeping Replacement: Fine-Grained Capacity Management for Shared CMP Caches

机译：自适应计时替换：用于共享CMP缓存的细粒度容量管理

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In chip multiprocessors (CMPs), several high-performance cores typically compete for capacity in a shared last-level cache. This causes degraded and unpredictable memory performance for multiprogrammed and parallel workloads. In response, recent schemes apportion cache bandwidth and capacity in ways that offer better aggregate performance for the workloads. These schemes, however, focus primarily on relatively coarse-grained capacity management without concern for operating system process priority levels. In this work, we explore capacity management approaches that are both temporally and spatially more fine-grained than prior work. We also consider operating system priority levels as part of capacity management. We propose a capacity management mechanism based on timekeeping techniques that track the time interval since the last access to cached data. This Adaptive Timekeeping Replacement (ATR) scheme maintains aggregate cache occupancies that reflect the priority and footprint of each application. The key novelties of our work are (1) ATR offers a complete cache capacity management framework taking into account application priorities and memory characteristics, and (2) ATR's fine-grained cache capacity control is demonstrated to be effective and important in improving the performance of parallel workloads in addition to sequential ones. We evaluate our ideas using a full-system simulator and multiprogrammed workloads of both sequential and parallel applications. This is the first detailed study of shared cache capacity management considering thread behaviors in parallel applications. ATR outperforms an unmanaged system by as much as 1.63X and by an average of 1.19X. ATR's fine-grained temporal control is particularly important for parallel applications, which are expected to be increasingly prevalent in years to come.
机译：在芯片多处理器（CMP）中，几个高性能内核通常会争用共享的最后一级缓存中的容量。这会导致多程序和并行工作负载的内存性能下降且无法预测。作为响应，最近的方案以为工作负载提供更好的聚合性能的方式分配缓存带宽和容量。但是，这些方案主要集中在相对粗粒度的容量管理上，而不关心操作系统进程优先级。在这项工作中，我们探索了比以前的工作在时间和空间上都更精细的容量管理方法。我们还将操作系统优先级视为容量管理的一部分。我们提出一种基于计时技术的容量管理机制，该技术可跟踪自上次访问缓存数据以来的时间间隔。这种自适应计时替换（ATR）方案可维护反映每个应用程序的优先级和占用空间的聚合缓存占用率。我们工作的主要新颖之处在于：（1）ATR提供了一个完整的缓存容量管理框架，其中考虑了应用程序的优先级和内存特性，并且（2）ATR的细粒度缓存容量控制被证明对提高ATR的性能有效且很重要。并行工作负载和顺序工作负载。我们使用完整的系统模拟器以及顺序和并行应用程序的多程序工作负载来评估我们的想法。这是对并行缓存中考虑线程行为的共享缓存容量管理的首次详细研究。 ATR的性能比非托管系统高1.63倍，平均为1.19倍。 ATR的细粒度时间控制对于并行应用尤其重要，并行应用预计在未来几年中将越来越流行。

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