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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
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
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