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Green Algorithms: Quantifying the Carbon Footprint of Computation

Climate change is profoundly affecting nearly all aspects of life on earth, including human societies, economies, and health. Various human activities are responsible for significant greenhouse gas (GHG) emissions, including data centers and other sources of large‐scale computation. Although many im...

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Detalles Bibliográficos
Autores principales: Lannelongue, Loïc, Grealey, Jason, Inouye, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224424/
https://www.ncbi.nlm.nih.gov/pubmed/34194954
http://dx.doi.org/10.1002/advs.202100707
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author Lannelongue, Loïc
Grealey, Jason
Inouye, Michael
author_facet Lannelongue, Loïc
Grealey, Jason
Inouye, Michael
author_sort Lannelongue, Loïc
collection PubMed
description Climate change is profoundly affecting nearly all aspects of life on earth, including human societies, economies, and health. Various human activities are responsible for significant greenhouse gas (GHG) emissions, including data centers and other sources of large‐scale computation. Although many important scientific milestones are achieved thanks to the development of high‐performance computing, the resultant environmental impact is underappreciated. In this work, a methodological framework to estimate the carbon footprint of any computational task in a standardized and reliable way is presented and metrics to contextualize GHG emissions are defined. A freely available online tool, Green Algorithms (www.green‐algorithms.org) is developed, which enables a user to estimate and report the carbon footprint of their computation. The tool easily integrates with computational processes as it requires minimal information and does not interfere with existing code, while also accounting for a broad range of hardware configurations. Finally, the GHG emissions of algorithms used for particle physics simulations, weather forecasts, and natural language processing are quantified. Taken together, this study develops a simple generalizable framework and freely available tool to quantify the carbon footprint of nearly any computation. Combined with recommendations to minimize unnecessary CO(2) emissions, the authors hope to raise awareness and facilitate greener computation.
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spelling pubmed-82244242021-06-29 Green Algorithms: Quantifying the Carbon Footprint of Computation Lannelongue, Loïc Grealey, Jason Inouye, Michael Adv Sci (Weinh) Research Articles Climate change is profoundly affecting nearly all aspects of life on earth, including human societies, economies, and health. Various human activities are responsible for significant greenhouse gas (GHG) emissions, including data centers and other sources of large‐scale computation. Although many important scientific milestones are achieved thanks to the development of high‐performance computing, the resultant environmental impact is underappreciated. In this work, a methodological framework to estimate the carbon footprint of any computational task in a standardized and reliable way is presented and metrics to contextualize GHG emissions are defined. A freely available online tool, Green Algorithms (www.green‐algorithms.org) is developed, which enables a user to estimate and report the carbon footprint of their computation. The tool easily integrates with computational processes as it requires minimal information and does not interfere with existing code, while also accounting for a broad range of hardware configurations. Finally, the GHG emissions of algorithms used for particle physics simulations, weather forecasts, and natural language processing are quantified. Taken together, this study develops a simple generalizable framework and freely available tool to quantify the carbon footprint of nearly any computation. Combined with recommendations to minimize unnecessary CO(2) emissions, the authors hope to raise awareness and facilitate greener computation. John Wiley and Sons Inc. 2021-05-02 /pmc/articles/PMC8224424/ /pubmed/34194954 http://dx.doi.org/10.1002/advs.202100707 Text en © 2021 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Lannelongue, Loïc
Grealey, Jason
Inouye, Michael
Green Algorithms: Quantifying the Carbon Footprint of Computation
title Green Algorithms: Quantifying the Carbon Footprint of Computation
title_full Green Algorithms: Quantifying the Carbon Footprint of Computation
title_fullStr Green Algorithms: Quantifying the Carbon Footprint of Computation
title_full_unstemmed Green Algorithms: Quantifying the Carbon Footprint of Computation
title_short Green Algorithms: Quantifying the Carbon Footprint of Computation
title_sort green algorithms: quantifying the carbon footprint of computation
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224424/
https://www.ncbi.nlm.nih.gov/pubmed/34194954
http://dx.doi.org/10.1002/advs.202100707
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