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Analyzing Programming Languages' Energy Consumption: An Empirical Study

Published: 28 September 2017 Publication History

Abstract

Motivation: The energy efficiency of it-related products, from the software perspective, has gained vast popularity the recent years and paved a new emerging research field. However, there is limited number of research works regarding the energy consumption of relatively small programming tasks. This knowledge is critical to be known especially in cases where millions of small tasks are running in parallel on multiple devices all around the globe.
Goal: In this preliminary study, we aim to identify energy implications of small, independent tasks developed in different programming languages; compiled, semi-compiled, and interpreted ones.
Method: To achieve our purpose, we collected, refined, compared, and analyzed a number of implemented tasks from Rosetta Code, that is a publicly available Repository for programming chrestomathy.
Results: Our analysis shows that among compiled programming languages such as C, C++, Java, and Go offer the highest energy efficiency for all of our tested tasks compared to C#, VB.Net, and Rust. Regarding interpreted programming languages PHP, Ruby, and JavaScript exhibit the most energy savings compared to Swift, R, Perl, and Python.

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  • (2024)On the Energy Consumption of CPythonQuality of Information and Communications Technology10.1007/978-3-031-70245-7_14(194-209)Online publication date: 11-Sep-2024
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Published In

cover image ACM Other conferences
PCI '17: Proceedings of the 21st Pan-Hellenic Conference on Informatics
September 2017
322 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • Greek Com Soc: Greek Computer Society
  • University of Thessaly: University of Thessaly, Volos, Greece

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 September 2017

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Author Tags

  1. Energy Efficiency
  2. Energy Optimization
  3. Programming Languages

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • Marie Sklodowska-Curie Innovative Training Networks

Conference

PCI 2017
PCI 2017: 21st PAN-HELLENIC CONFERENCE ON INFORMATICS
September 28 - 30, 2017
Larissa, Greece

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Overall Acceptance Rate 190 of 390 submissions, 49%

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Cited By

View all
  • (2024)A Systematic Review of Multi-Objective Evolutionary Algorithms Optimization FrameworksProcesses10.3390/pr1205086912:5(869)Online publication date: 26-Apr-2024
  • (2024)A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT DevicesApplied Sciences10.3390/app1413545914:13(5459)Online publication date: 24-Jun-2024
  • (2024)On the Energy Consumption of CPythonQuality of Information and Communications Technology10.1007/978-3-031-70245-7_14(194-209)Online publication date: 11-Sep-2024
  • (2023)Analyzing the Resource Usage Overhead of Mobile App Development FrameworksProceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering10.1145/3593434.3593487(152-161)Online publication date: 14-Jun-2023
  • (2023)A Comparative Study of Programming Languages for a Real-Time Smart Grid Application2023 IEEE Green Energy and Smart Systems Conference (IGESSC)10.1109/IGESSC59090.2023.10321761(1-6)Online publication date: 13-Nov-2023
  • (2023)Evaluating FFT performance of the C and Rust Languages on Raspberry Pi platforms2023 57th Annual Conference on Information Sciences and Systems (CISS)10.1109/CISS56502.2023.10089631(1-6)Online publication date: 22-Mar-2023
  • (2023)RJoules: An Energy Measurement Tool for R2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00207(2026-2029)Online publication date: 11-Sep-2023
  • (2022)A Meta-analytical Comparison of Energy Consumed by Two Different Programming LanguagesFrontiers in Software Engineering10.1007/978-3-030-93135-3_12(176-200)Online publication date: 6-Jan-2022

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