分析代码助手能耗影响因素，提出节能配置优化方案
Green My LLM: Studying the key factors affecting the energy consumption of code assistants
- 通过模拟20名开发者使用GitHub Copilot的场景，研究并发数、模型大小等对能耗的影响
- 发现大量生成请求被取消或拒绝，存在显著计算浪费
- 给出具体配置建议，不同场景下可实现显著能效提升
近年来，大型语言模型（LLMs）在生成高质量代码方面取得显著进展，已集成至开发者的集成开发环境（IDE）中作为代码助手。此类工具如GitHub Copilot可提供实时代码建议，大幅提升开发效率。然而，这些工具的环境影响，特别是其能源消耗，仍令人担忧。本文通过模拟开发者与GitHub Copilot的交互行为，并分析多种配置因素，研究基于LLM的代码助手的能耗问题。我们收集了20名开发者的开发轨迹数据，并开展大规模软件项目开发仿真，以测量不同场景下的能耗情况。研究发现，代码助手的能耗与性能受并发开发者数量、模型规模、量化方法及流式传输等多种因素影响。值得注意的是，大量生成请求被开发者取消或拒绝，表明存在可减少的冗余计算。基于上述发现，本文提出了针对不同使用场景的可操作配置优化建议，证明通过合理调整可实现显著的能源节约。
原文摘要 · Abstract (English)
In recent years,Large Language Models (LLMs) have significantly improved in generating high-quality code, enabling their integration into developers' Integrated Development Environments (IDEs) as code assistants. These assistants, such as GitHub Copilot, deliver real-time code suggestions and can greatly enhance developers' productivity. However, the environmental impact of these tools, in particular their energy consumption, remains a key concern. This paper investigates the energy consumption of LLM-based code assistants by simulating developer interactions with GitHub Copilot and analyzing various configuration factors. We collected a dataset of development traces from 20 developers and conducted extensive software project development simulations to measure energy usage under different scenarios. Our findings reveal that the energy consumption and performance of code assistants are influenced by various factors, such as the number of concurrent developers, model size, quantization methods, and the use of streaming. Notably, a substantial portion of generation requests made by GitHub Copilot is either canceled or rejected by developers, indicating a potential area for reducing wasted computations. Based on these findings, we share actionable insights into optimizing configurations for different use cases, demonstrating that careful adjustments can lead to significant energy savings.
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