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EnergiBridge: Empowering Software Sustainability through Cross-Platform Energy Measurement

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arxiv 2312.13897 v1 pith:NZUMTDSW submitted 2023-12-21 cs.SE

June Sallou , Luís Cruz , Thomas Durieux This is my paper · ORCID
classification cs.SE
keywords softwareenergibridgeenergyconsumptioncross-platformdiverseengineeringgithub
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In the continually evolving realm of software engineering, the need to address software energy consumption has gained increasing prominence. However, the absence of a platform-independent tool that facilitates straightforward energy measurements remains a notable gap. This paper presents EnergiBridge, a cross-platform measurement utility that provides support for Linux, Windows, and MacOS, as well as Intel, AMD, and Apple ARM CPU architectures. In essence, EnergiBridge serves as a bridge between energy-conscious software engineering and the diverse software environments in which it operates. It encourages a broader community to make informed decisions, minimize energy consumption, and reduce the environmental impact of software systems. By simplifying software energy measurements, EnergiBridge offers a valuable resource to make green software development more lightweight, education more inclusive, and research more reproducible. Through the evaluation, we highlight EnergiBridge's ability to gather energy data across diverse platforms and hardware configurations. EnergiBridge is publicly available on GitHub: https://github.com/tdurieux/EnergiBridge, and a demonstration video can be viewed at: https://youtu.be/-gPJurKFraE.

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Cited by 3 Pith papers

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    A new energy debugging methodology identifies musl's memcpy implementation as the main cause of Alpine's higher Redis energy consumption for small memory copies.

  2. Generating Energy-Efficient Code via Large-Language Models -- Where are we now?

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    Across 363 solutions on three hardware platforms, no LLM consistently matched a green software expert's energy efficiency, though some LLM-prompt combinations beat ordinary human code.

  3. Insights into resource utilization of code small language models serving with runtime engines and execution providers

    cs.SE 2024-12 conditional novelty 5.0 of 10

    Serving configuration choice, especially runtime engine and execution provider, significantly changes energy, time, and resource use of code SLMs, with TORCH+CUDA most efficient on the tested hardware.

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