Toward an End-to-End Auto-tuning Framework in HPC PowerStack

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 differen...

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Bibliographic Details
Published in:arXiv.org 2020-08
Main Authors: Wu, Xingfu, Marathe, Aniruddha, Jana, Siddhartha, Vysocky, Ondrej, Jophin, John, Bartolini, Andrea, Riha, Lubomir, Gerndt, Michael, Taylor, Valerie, Sridutt Bhalachandra
Format: Article
Language:English
Subjects:
Optimization
Software
Streamlining
Telemetry
Tuning
Online Access:Get full text
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Summary: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.
ISSN:2331-8422