论文标题
朝HPC Powerstack中的端到端自动调整框架
Toward an End-to-End Auto-tuning Framework in HPC PowerStack
论文作者
论文摘要
有效利用采购的功率并优化在权力和能量限制下的科学应用的性能是具有挑战性的。 HPC Powerstack定义了一个软件堆栈,以管理高性能计算系统的功率和能量,并标准化堆栈不同组件之间的接口。该调查文件介绍了一个工作组的发现,该工作组侧重于Powerstack的端到端调整。首先,我们提供有关Powerstack层特定的调整工作的背景,以其高级目标,约束和优化目标,特定于层的遥测和控制参数,并列出了解决这些挑战的现有软件解决方案。其次,我们提出了Powerstack端到端自动调整框架,确定在Powerstack中共同调整不同层的机会,并提供特定的用例和解决方案。第三,我们讨论了Powerstack中两个或多个管理层(或域)的集体自动调整的研究机会和挑战。本文在识别和汇总了简化Powerstack层次的优化工作时采取的第一步。
Efficiently utilizing procured power and optimizing performance of scientific applications under power and energy constraints are challenging. The HPC PowerStack defines a software stack to manage power and energy of high-performance computing systems and standardizes the interfaces between different components of the stack. This survey paper presents the findings of a working group focused on the end-to-end tuning of the PowerStack. First, we provide a background on the PowerStack layer-specific tuning efforts in terms of their high-level objectives, the constraints and optimization goals, layer-specific telemetry, and control parameters, and we list the existing software solutions that address those challenges. Second, we propose the PowerStack end-to-end auto-tuning framework, identify the opportunities in co-tuning different layers in the PowerStack, and present specific use cases and solutions. Third, we discuss the research opportunities and challenges for collective auto-tuning of two or more management layers (or domains) in the PowerStack. This paper takes the first steps in identifying and aggregating the important R&D challenges in streamlining the optimization efforts across the layers of the PowerStack.