AI 中文总结
针对HPC应用的计算与数据移动不平衡问题，提出基于细粒度内存行为建模的MEMPOWER框架，可降低6%-42%的EDP且对执行时间影响小。
AI 中文摘要
管理并行应用的能耗和功耗效率是HPC环境与云平台中的重要问题。随着新兴应用不断冲击现代机器的内存墙，计算与数据移动之间日益加剧的不平衡，为智能调整CPU功耗创造了新机遇。遗憾的是，现有的频率与电压缩放技术无法充分捕捉内存访问行为的细粒度变化，导致系统组件无法感知可利用的计算/数据访问不平衡，从而错失潜在的功耗节省空间。本文提出MEMPOWER，一种灵活的基于模型的方法，用于揭示计算/数据移动的不平衡，该方法对并行工作负载的细粒度内存行为进行表征。此表征随后为我们的自动化软件框架提供依据，该框架可使用模型确定的电压/频率转换静态检测应用二进制文件，从而平衡内存访问行为的细粒度变化与硬件转换的成本。与标准操作系统/硬件管理的功耗控制机制相比，使用MEMPOWER在一系列HPC基准测试上实现了6%至42%的EDP降低，且对执行时间的影响极小。
英文摘要
Managing the energy consumption and power efficiency of parallel applications is a significant issue in both HPC environments and in the cloud. As emerging applications continue to push against the memory wall of modern machines, the growing imbalance between compute and data movement creates new opportunities to intelligently tune CPU power consumption. Unfortunately, existing frequency and voltage scaling techniques do not adequately capture fine-grained changes in memory access behavior, rendering the compute/data access imbalance invisible to the components of the system that could capitalize on it, thus leaving potential power savings on the table. In this paper, we propose MEMPOWER, a flexible, model-based approach to exposing compute/data movement imbalance that characterizes the fine-grained memory behavior of parallel workloads. This characterization then informs our automated software framework which can statically instrument the application binary with model-determined voltage/frequency transitions that balance fine-grained changes in memory access behavior with the costs of hardware transitions. Using MEMPOWER, we demonstrate a reduction in EDP of 6% to 42% on a range of HPC benchmarks with minimal impact on execution time when compared to the standard OS/hardware-managed power control mechanism.
