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

Xingfu Wu, Aniruddha Marathe, Siddhartha Jana, Ondrej Vysocky, Jophin John, Andrea Bartolini, Lubomir Riha, Michael Gerndt, Valerie Taylor, Sridutt Bhalachandra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

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 RD challenges in streamlining the optimization efforts across the layers of the PowerStack.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Cluster Computing, CLUSTER 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages473-483
Number of pages11
ISBN (Electronic)9781728166773
DOIs
StatePublished - Sep 2020
Event22nd IEEE International Conference on Cluster Computing, CLUSTER 2020 - Kobe, Japan
Duration: 14 Sep 202017 Sep 2020

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
Volume2020-September
ISSN (Print)1552-5244

Conference

Conference22nd IEEE International Conference on Cluster Computing, CLUSTER 2020
Country/TerritoryJapan
CityKobe
Period14/09/2017/09/20

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