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Energy efficiency across programming languages: how do energy, time, and memory relate?

descriptionPublicationkeyboard_double_arrow_right Conference object , Article , Other literature type 23 Oct 2017 Portugal Publisher:ACMJournal:Proceedings of the 10th ACM SIGPLAN International Conference on Software Language EngineeringFunded by:FCT | INESC TEC - INESC Technol..., FCT | SFRH/BD/112733/2015

Authors: Pereira, Rui; Couto, Marco; Ribeiro, Francisco; Rua, Rui; Cunha, Jácome Miguel Costa; Fernandes, João; Saraiva, João;

doi: 10.1145/3136014.3136031

handle: 1822/65359

Energy efficiency across programming languages: how do energy, time, and memory relate?

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Abstract

This paper presents a study of the runtime, memory usage and energy consumption of twenty seven well-known software languages. We monitor the performance of such languages using ten different programming problems, expressed in each of the languages. Our results show interesting findings, such as, slower/faster languages consuming less/more energy, and how memory usage influences energy consumption. Finally, we show how to use our results to provide software engineers support to decide which language to use when energy efficiency is a concern.

Country

Portugal

Related Organizations

Keywords

Language Benchmarking, Science & Technology, Energy Efficiency, Green Software, Programming Languages

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	152
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%