Ranking programming languages by energy efficiency

Detalhes bibliográficos
Autor(a) principal: Couto,M
Data de Publicação: 2021
Outros Autores: João Alexandre Saraiva, Fernandes,JP, Jácome Costa Cunha, Rua,R, Pereira,R, Ribeiro,F
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/12117
http://dx.doi.org/10.1016/j.scico.2021.102609
Resumo: This paper compares a large set of programming languages regarding their efficiency, including from an energetic point-of-view. Indeed, we seek to establish and analyze different rankings for programming languages based on their energy efficiency. The goal of being able to rank programming languages based on their energy efficiency is both recent, and certainly deserves further studies. We have taken rigorous and strict solutions to 10 well defined programming problems, expressed in (up to) 27 programming languages, from the well known Computer Language Benchmark Game repository. This repository aims to compare programming languages based on a strict set of implementation rules and configurations for each benchmarking problem. We have also built a framework to automatically, and systematically, run, measure and compare the energy, time, and memory efficiency of such solutions. Ultimately, it is based on such comparisons that we propose a series of efficiency rankings, based on single and multiple criteria. Our results show interesting findings, such as how slower/faster languages can consume less/more energy, and how memory usage influences energy consumption. We also present a simple way to use our results to provide software engineers and practitioners support in deciding which language to use when energy efficiency is a concern. In addition, we further validate our results and rankings against implementations from a chrestomathy program repository, Rosetta Code., by reproducing our methodology and benchmarking system. This allows us to understand how the results and conclusions from our rigorously and well defined benchmarked programs compare to those based on more representative and real-world implementations. Indeed our results show that the rankings do not change apart from one programming language. © 2021 Elsevier B.V.
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spelling Ranking programming languages by energy efficiencyThis paper compares a large set of programming languages regarding their efficiency, including from an energetic point-of-view. Indeed, we seek to establish and analyze different rankings for programming languages based on their energy efficiency. The goal of being able to rank programming languages based on their energy efficiency is both recent, and certainly deserves further studies. We have taken rigorous and strict solutions to 10 well defined programming problems, expressed in (up to) 27 programming languages, from the well known Computer Language Benchmark Game repository. This repository aims to compare programming languages based on a strict set of implementation rules and configurations for each benchmarking problem. We have also built a framework to automatically, and systematically, run, measure and compare the energy, time, and memory efficiency of such solutions. Ultimately, it is based on such comparisons that we propose a series of efficiency rankings, based on single and multiple criteria. Our results show interesting findings, such as how slower/faster languages can consume less/more energy, and how memory usage influences energy consumption. We also present a simple way to use our results to provide software engineers and practitioners support in deciding which language to use when energy efficiency is a concern. In addition, we further validate our results and rankings against implementations from a chrestomathy program repository, Rosetta Code., by reproducing our methodology and benchmarking system. This allows us to understand how the results and conclusions from our rigorously and well defined benchmarked programs compare to those based on more representative and real-world implementations. Indeed our results show that the rankings do not change apart from one programming language. © 2021 Elsevier B.V.2021-03-24T10:48:22Z2021-01-01T00:00:00Z2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/12117http://dx.doi.org/10.1016/j.scico.2021.102609engCouto,MJoão Alexandre SaraivaFernandes,JPJácome Costa CunhaRua,RPereira,RRibeiro,Finfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:19:47ZPortal AgregadorONG
dc.title.none.fl_str_mv Ranking programming languages by energy efficiency
title Ranking programming languages by energy efficiency
spellingShingle Ranking programming languages by energy efficiency
Couto,M
title_short Ranking programming languages by energy efficiency
title_full Ranking programming languages by energy efficiency
title_fullStr Ranking programming languages by energy efficiency
title_full_unstemmed Ranking programming languages by energy efficiency
title_sort Ranking programming languages by energy efficiency
author Couto,M
author_facet Couto,M
João Alexandre Saraiva
Fernandes,JP
Jácome Costa Cunha
Rua,R
Pereira,R
Ribeiro,F
author_role author
author2 João Alexandre Saraiva
Fernandes,JP
Jácome Costa Cunha
Rua,R
Pereira,R
Ribeiro,F
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Couto,M
João Alexandre Saraiva
Fernandes,JP
Jácome Costa Cunha
Rua,R
Pereira,R
Ribeiro,F
description This paper compares a large set of programming languages regarding their efficiency, including from an energetic point-of-view. Indeed, we seek to establish and analyze different rankings for programming languages based on their energy efficiency. The goal of being able to rank programming languages based on their energy efficiency is both recent, and certainly deserves further studies. We have taken rigorous and strict solutions to 10 well defined programming problems, expressed in (up to) 27 programming languages, from the well known Computer Language Benchmark Game repository. This repository aims to compare programming languages based on a strict set of implementation rules and configurations for each benchmarking problem. We have also built a framework to automatically, and systematically, run, measure and compare the energy, time, and memory efficiency of such solutions. Ultimately, it is based on such comparisons that we propose a series of efficiency rankings, based on single and multiple criteria. Our results show interesting findings, such as how slower/faster languages can consume less/more energy, and how memory usage influences energy consumption. We also present a simple way to use our results to provide software engineers and practitioners support in deciding which language to use when energy efficiency is a concern. In addition, we further validate our results and rankings against implementations from a chrestomathy program repository, Rosetta Code., by reproducing our methodology and benchmarking system. This allows us to understand how the results and conclusions from our rigorously and well defined benchmarked programs compare to those based on more representative and real-world implementations. Indeed our results show that the rankings do not change apart from one programming language. © 2021 Elsevier B.V.
publishDate 2021
dc.date.none.fl_str_mv 2021-03-24T10:48:22Z
2021-01-01T00:00:00Z
2021
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/12117
http://dx.doi.org/10.1016/j.scico.2021.102609
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http://dx.doi.org/10.1016/j.scico.2021.102609
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