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首页> 外文会议>2019 Spring Simulation Conference >ML Guided Energy-Performance Trade-Off Estimation For Uncore Frequency Scaling
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ML Guided Energy-Performance Trade-Off Estimation For Uncore Frequency Scaling

机译：ML指导的非核心频率缩放的能量-性能折衷估计

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摘要

Chip multiprocessors (CMPs) - also called multicores - have become the main architectural component for computing systems ranging from high-end servers to hand-held devices. CMPs enhance performance through parallelism by permitting multi-programmed/threaded workloads to run concurrently on the available computing cores. However, the power-performance trade-off due to frequency scaling of these cores cannot be determined independently as they share critical resources like L2/L3 cache as well as memory during execution. Thus, unlike the uni-processor environment, the energy consumption of an application running on a CMP depends not only on its characteristics but also its co-runners (applications running on other cores). In this work, we investigate an application's performance response to core and uncore frequency scaling and propose a learning-based model for determining a suitable uncore frequency. The model takes selected individual characteristics of the applications as input and suggests an optimal uncore frequency that would satisfy the overall QoS requirements.
机译：芯片多处理器（CMP）（也称为多核）已成为从高端服务器到手持设备的计算系统的主要架构组件。 CMP通过允许多程序/线程工作负载在可用计算核心上并行运行，通过并行性提高了性能。但是，由于这些内核的频率缩放而导致的功率性能折衷无法独立确定，因为它们在执行期间会共享关键资源，如L2 / L3高速缓存以及内存。因此，与单处理器环境不同，在CMP上运行的应用程序的能耗不仅取决于其特性，还取决于其共同运行者（在其他内核上运行的应用程序）。在这项工作中，我们调查了应用程序对核心和非核心频率缩放的性能响应，并提出了一种基于学习的模型来确定合适的非核心频率。该模型将应用程序的选定个别特征作为输入，并提出可满足整体QoS要求的最佳非核心频率。

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