当前位置: X-MOL 学术arXiv.cs.PF › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Toward an End-to-End Auto-tuning Framework in HPC PowerStack
arXiv - CS - Performance Pub Date : 2020-08-14 , DOI: arxiv-2008.06571
Xingfu Wu and Aniruddha Marathe and Siddhartha Jana and Ondrej Vysocky and Jophin John and Andrea Bartolini and Lubomir Riha and Michael Gerndt and Valerie Taylor and Sridutt Bhalachandra

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.
更新日期:2020-08-18
down
wechat
bug