LLMs for Energy-Efficient Code: Emerging Results and Future Directions (2024)

LLMs for Energy-Efficient Code: Emerging Results and Future Directions (2024)#

This paper was led by Huiyun Peng and Akhil Gupte (Purdue University), working with James C. Davis and Yung-Hsiang Lu. I am one of the key leaders of this research project. The work is available as an arXiv preprint (arXiv:2410.09241).

Abstract / Summary#

Large language models are widely used to generate code, but the energy efficiency of that code is rarely considered. This paper presents early empirical results on whether LLM-generated code is energy-efficient, what factors influence it, and what prompting or fine-tuning strategies can improve it. The findings are preliminary but point toward a clear research agenda at the intersection of green computing and AI-assisted software development.

Background#

Software energy consumption is a growing concern. Data centers consume significant electricity, and the code running on them matters. At the same time, LLMs like GitHub Copilot and ChatGPT are changing how code gets written — developers increasingly accept generated code with minimal modification. If generated code is systematically less energy-efficient than hand-written code, that is a problem worth understanding and fixing. Prior work has studied energy efficiency in traditional software engineering contexts, but the LLM code generation setting is new.

Key Contributions#

  • An empirical measurement study comparing the energy consumption of LLM-generated code against human-written equivalents across a benchmark suite.

  • Evidence that LLM-generated code is not automatically energy-efficient and in some cases introduces measurable overhead.

  • An evaluation of prompting strategies (e.g., explicitly requesting efficient implementations) and their effect on generated code’s energy profile.

  • A research roadmap for making energy efficiency a first-class metric in LLM-assisted programming tools.

Findings#

LLM-generated code varied considerably in energy efficiency depending on the model, prompt, and task. Naive code generation — asking an LLM to implement a function without energy-related guidance — often produced correct but inefficient solutions. Targeted prompting strategies that explicitly request efficiency improved results in some cases, but inconsistently. The findings suggest that current LLMs do not have a strong internal model of energy efficiency and that this will need to be explicitly trained or elicited.

Key Take-Aways#

Energy efficiency is a blind spot in current LLM-based coding tools. As these tools become more prevalent, the code they generate will run at scale, and efficiency differences that seem small per-request can aggregate into significant energy costs. This work is an early step toward benchmarks and training signals that incorporate energy as an objective alongside correctness and performance.

Citation#

Peng, H., Gupte, A., Eliopoulos, N. J., Ho, C. C., Mantri, R., Deng, L., Jiang, W., Thiruvathukal, G. K., Davis, J. C., & Lu, Y.-H. (2024). Large language models for energy-efficient code: Emerging results and future directions. arXiv:2410.09241. https://arxiv.org/abs/2410.09241

@misc{peng_llm_energycode_2024,
  author        = {Peng, Huiyun and Gupte, Akhil and Eliopoulos, Nicholas J. and Ho, Chi-Chang and Mantri, Rohan and Deng, Leyong and Jiang, Wenxin and Thiruvathukal, George K. and Davis, James C. and Lu, Yung-Hsiang},
  title         = {Large language models for energy-efficient code: {Emerging} results and future directions},
  year          = {2024},
  eprint        = {2410.09241},
  archivePrefix = {arXiv},
  primaryClass  = {cs.SE},
  url           = {https://arxiv.org/abs/2410.09241}
}
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