AIMeter可量化AI模型能耗与碳排放，助力绿色AI研究
AIMeter: Measuring, Analyzing, and Visualizing Energy and Carbon Footprint of AI Workloads
- 集成多维度指标，实时监测训练与推理的能耗与碳排
- 支持硬件性能与模型表现的关联分析，定位效率瓶颈
- 开源工具链，适配主流框架，推动可持续AI发展
人工智能，特别是大语言模型（LLMs）的快速发展，引发了对其训练和推理过程中能源消耗与碳排放的广泛关注。然而，现有测量与报告工具往往碎片化，缺乏系统性指标整合，且对多指标间相关性分析支持有限。本文提出AIMeter，一个全面的软件工具包，用于测量、分析和可视化AI工作负载中的能源使用、功耗、硬件性能及碳排放。通过无缝集成现有AI框架，AIMeter提供标准化报告，并导出细粒度时间序列数据，支持基准测试与可复现性研究，且以轻量方式实现。它进一步支持硬件指标与模型性能之间的深入相关性分析，有助于识别性能瓶颈并提升效率。通过解决现有工具的关键局限，AIMeter推动研究社区在关注模型性能的同时，重视环境影响，促进更可持续的‘Green AI’实践。代码已开源：https://github.com/SusCom-Lab/AIMeter。
原文摘要 · Abstract (English)
The rapid advancement of AI, particularly large language models (LLMs), has raised significant concerns about the energy use and carbon emissions associated with model training and inference. However, existing tools for measuring and reporting such impacts are often fragmented, lacking systematic metric integration and offering limited support for correlation analysis among them. This paper presents AIMeter, a comprehensive software toolkit for the measurement, analysis, and visualization of energy use, power draw, hardware performance, and carbon emissions across AI workloads. By seamlessly integrating with existing AI frameworks, AIMeter offers standardized reports and exports fine-grained time-series data to support benchmarking and reproducibility in a lightweight manner. It further enables in-depth correlation analysis between hardware metrics and model performance and thus facilitates bottleneck identification and performance enhancement. By addressing critical limitations in existing tools, AIMeter encourages the research community to weigh environmental impact alongside raw performance of AI workloads and advances the shift toward more sustainable "Green AI" practices. The code is available at https://github.com/SusCom-Lab/AIMeter.
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