在降低能耗的同时保持性能：一种控制理论方法
Sustaining Performance While Reducing Energy Consumption: A Control Theory Approach
摘要：
生产高性能计算系统在复杂性和大小上持续增长。随着应用程序努力利用日益异构的计算节点，保持整个平台的高效率（性能功耗比）成为一项挑战。随着科学工作负载的日益复杂，这种极端的异构性也是一个机会：由于设备之间的阶段或数据/计算移动，应用程序动态地经历工作负载的变化，人们可以动态地调整计算元素的功率，以节省能源而不影响性能。为了给当前和未来的高性能计算架构提供一种自主的动态电源管理策略，本文探讨了利用控制理论设计一种动态电源调节方法。结构作为一个反馈回路，我们的方法，这是新颖的计算资源管理，包括定期监测应用程序的进展，并在运行时选择一个合适的功率上限的处理器。由于初步的离线识别过程中，我们推导出一个模型的动态系统和比例积分（PI）控制器。我们评估我们的方法上现有的资源管理框架，Argo节点资源管理器，部署在几个集群的网格'5000，使用标准的内存绑定HPC基准。
英文摘要：
Production high-performance computing systems continue to grow in complexity and size. As applications struggle to make use of increasingly heterogeneous compute nodes, maintaining high efficiency (performance per watt) for the whole platform becomes a challenge. Alongside the growing complexity of scientific workloads, this extreme heterogeneity is also an opportunity: as applications dynamically undergo variations in workload, due to phases or data/compute movement between devices, one can dynamically adjust power across compute elements to save energy without impacting performance. With an aim toward an autonomous and dynamic power management strategy for current and future HPC architectures, this paper explores the use of control theory for the design of a dynamic power regulation method. Structured as a feedback loop, our approach-which is novel in computing resource management-consists of periodically monitoring application progress and choosing at runtime a suitable power cap for processors. Thanks to a preliminary offline identification process, we derive a model of the dynamics of the system and a proportional-integral (PI) controller. We evaluate our approach on top of an existing resource management framework, the Argo Node Resource Manager, deployed on several clusters of Grid'5000, using a standard memory-bound HPC benchmark.
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