EnergiBridge: Empowering Software Sustainability through Cross-Platform Energy Measurement
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Dec 2023.
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Citing papers per year: 2024: 2; 2025: 2; 2026: 2.
6 papers.
- 2024Impact of ML Optimization Tactics on Greener Pre-Trained ML Modelscited by 2
- 2025Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizationscited by 1
- 2024Insights into resource utilization of code small language models serving with runtime engines and execution providerscited by 0
- 2025Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledgecited by 0
- 2026Sustainability Is Not Linear: Quantifying Performance, Energy, and Privacy Trade-offs in On-Device Intelligencecited by 0
- 2026Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Taskcited by 0
- 2026Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Task18 Aug 2026
- 2026Sustainability Is Not Linear: Quantifying Performance, Energy, and Privacy Trade-offs in On-Device Intelligence27 Mar 2026
- 2025Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations26 Mar 2025
- 2025Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge14 Jan 2025
- 2024Insights into resource utilization of code small language models serving with runtime engines and execution providers19 Dec 2024
- 2024Impact of ML Optimization Tactics on Greener Pre-Trained ML Models19 Sep 2024