一句话：通过因果模型与实验控制混杂因素，表明编程语言实现选择除执行时间外对能耗无显著影响。
论文追问编程语言选择是否影响能耗。此前高可见度研究将语言与能耗关联，并被学术界和工业界因果误读，用于支持或采用某些语言。本文建立细粒度因果模型，刻画语言选择与能耗间的复杂关系，并纳入此前被忽视的因素：语言与其实现的区分、应用实现本身、活跃核心数、内存活动等；若不控制这些因素，能耗测量会被显著扭曲。作者通过实证实验、改进的方法学以及对异常值的仔细检查，发现控制上述因素后，先前工作中的显著差异消失。分析表明，编程语言实现的选择除通过执行时间间接影响外，对能耗没有显著影响。
Does the choice of programming language affect energy consumption? Previous highly visible studies have established associations between certain programming languages and energy consumption. A causal misinterpretation of this work has led academics and industry leaders to use or support certain languages based on their claimed impact on energy consumption. This paper tackles this causal question directly: it develops a detailed causal model capturing the complex relationship between programming language choice and energy consumption. This model identifies and incorporates several critical but previously overlooked factors that affect energy usage. These factors, such as distinguishing programming languages from their implementations, the impact of the application implementations themselves, the number of active cores, and memory activity, can significantly skew energy consumption measurements if not accounted for. We show—via empirical experiments, improved methodology, and careful examination of anomalies—that when these factors are controlled for, notable discrepancies in prior work vanish. Our analysis suggests that the choice of programming language implementation has no significant impact on energy consumption beyond execution time.
DOI 原文 ·
@inproceedings{paperbot9333,
title = {It’s Not Easy Being Green: On the Energy Efficiency of Programming Languages},
author = {Nicolas van Kempen and Hyuk-Je Kwon and Dung Tuan Nguyen and Emery D. Berger},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00131}
}