# Unveiling the Energy Vampires: A Methodology for Debugging Software Energy Consumption

arXiv: https://arxiv.org/abs/2412.10063 · HTML: https://mlsanity.com/paper/2412.10063/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2024.

## Built on by (most cited first, 4 of 4)

- [Generating Energy-Efficient Code via Large-Language Models -- Where are we now?](https://mlsanity.com/paper/2509.10099/) (2025)
- [Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations](https://mlsanity.com/paper/2503.20126/) (2025)
- [SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Models](https://mlsanity.com/paper/2507.15663/) (2025)
- [Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Task](https://mlsanity.com/paper/2608.17515/) (2026)

## Abstract (arXiv)

Energy consumption in software systems is becoming increasingly important, especially in large-scale deployments. However, debugging energy-related issues remains challenging due to the lack of specialized tools. This paper presents an energy debugging methodology for identifying and isolating energy consumption hotspots in software systems. We demonstrate the methodology's effectiveness through a case study of Redis, a popular in-memory database. Our analysis reveals significant energy consumption differences between Alpine and Ubuntu distributions, with Alpine consuming up to 20.2% more power in certain operations. We trace this difference to the implementation of the memcpy function in different C standard libraries (musl vs. glibc). By isolating and benchmarking memcpy, we confirm it as the primary cause of the energy discrepancy. Our findings highlight the importance of considering energy efficiency in software dependencies and demonstrate the capability to assist developers in identifying and addressing energy-related issues. This work contributes to the growing field of sustainable software engineering by providing a systematic approach to energy debugging and using it to unveil unexpected energy behaviors in Alpine.