Towards bespoke optimizations of energy efficiency in HPC environments
Abstract We present bespoke energy efficiency optimizations in high performance computing (HPC) environments using holistic approach to data collection, analysis and proactive management of resources and workloads. Our solution has three major components: (i) platform for collecting, storing and pro...
Main Authors: | , , , , , , , , , |
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Format: | Article |
Language: | English |
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Wiley
2023-12-01
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Series: | Applied AI Letters |
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Online Access: | https://doi.org/10.1002/ail2.87 |
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author | Robert Tracey Vadim Elisseev Michalis Smyrnakis Lan Hoang Mark Fellows Michael Ackers Andrew Laughton Stephen Hill Phillip Folkes John Whittle |
author_facet | Robert Tracey Vadim Elisseev Michalis Smyrnakis Lan Hoang Mark Fellows Michael Ackers Andrew Laughton Stephen Hill Phillip Folkes John Whittle |
author_sort | Robert Tracey |
collection | DOAJ |
description | Abstract We present bespoke energy efficiency optimizations in high performance computing (HPC) environments using holistic approach to data collection, analysis and proactive management of resources and workloads. Our solution has three major components: (i) platform for collecting, storing and processing data from multiple sources across hardware and software stacks, (ii) collections of regression machine learning (ML) algorithms for making workloads classifications and energy usage predictions, (iii) agent‐based decision‐making framework for delivering control decisions to middle‐ware and infrastructure thus supporting real time or near real energy efficiency optimizations. We will provide some concrete examples of using our proposed approach in HPC environment. |
first_indexed | 2024-03-08T17:49:58Z |
format | Article |
id | doaj.art-48e41d27bc884d10b78b5d751a3bba83 |
institution | Directory Open Access Journal |
issn | 2689-5595 |
language | English |
last_indexed | 2024-03-08T17:49:58Z |
publishDate | 2023-12-01 |
publisher | Wiley |
record_format | Article |
series | Applied AI Letters |
spelling | doaj.art-48e41d27bc884d10b78b5d751a3bba832024-01-02T08:29:24ZengWileyApplied AI Letters2689-55952023-12-0144n/an/a10.1002/ail2.87Towards bespoke optimizations of energy efficiency in HPC environmentsRobert Tracey0Vadim Elisseev1Michalis Smyrnakis2Lan Hoang3Mark Fellows4Michael Ackers5Andrew Laughton6Stephen Hill7Phillip Folkes8John Whittle9Daresbury Laboratory IBM Research Warrington UKDaresbury Laboratory IBM Research Warrington UKDaresbury Laboratory, The Hartree Centre STFC Warrington UKDaresbury Laboratory IBM Research Warrington UKDaresbury Laboratory, The Hartree Centre STFC Warrington UKDaresbury Laboratory, The Hartree Centre STFC Warrington UKDaresbury Laboratory, The Hartree Centre STFC Warrington UKDaresbury Laboratory STFC Warrington UKDaresbury Laboratory STFC Warrington UKDaresbury Laboratory STFC Warrington UKAbstract We present bespoke energy efficiency optimizations in high performance computing (HPC) environments using holistic approach to data collection, analysis and proactive management of resources and workloads. Our solution has three major components: (i) platform for collecting, storing and processing data from multiple sources across hardware and software stacks, (ii) collections of regression machine learning (ML) algorithms for making workloads classifications and energy usage predictions, (iii) agent‐based decision‐making framework for delivering control decisions to middle‐ware and infrastructure thus supporting real time or near real energy efficiency optimizations. We will provide some concrete examples of using our proposed approach in HPC environment.https://doi.org/10.1002/ail2.87cloudenergy efficiencyHPCmachine learning |
spellingShingle | Robert Tracey Vadim Elisseev Michalis Smyrnakis Lan Hoang Mark Fellows Michael Ackers Andrew Laughton Stephen Hill Phillip Folkes John Whittle Towards bespoke optimizations of energy efficiency in HPC environments Applied AI Letters cloud energy efficiency HPC machine learning |
title | Towards bespoke optimizations of energy efficiency in HPC environments |
title_full | Towards bespoke optimizations of energy efficiency in HPC environments |
title_fullStr | Towards bespoke optimizations of energy efficiency in HPC environments |
title_full_unstemmed | Towards bespoke optimizations of energy efficiency in HPC environments |
title_short | Towards bespoke optimizations of energy efficiency in HPC environments |
title_sort | towards bespoke optimizations of energy efficiency in hpc environments |
topic | cloud energy efficiency HPC machine learning |
url | https://doi.org/10.1002/ail2.87 |
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