论文标题
迈向NLP模型的准确和可靠的能源测量
Towards Accurate and Reliable Energy Measurement of NLP Models
论文作者
论文摘要
精确和可靠的能源消耗测量对于在选择和训练大规模NLP模型时做出明智的设计选择至关重要。在这项工作中，我们表明现有的基于软件的能源测量不准确，因为它们不考虑硬件差异以及资源利用如何影响能源消耗。我们使用四个不同模型进行问题回答任务进行能量测量实验。我们通过使用提供高度准确的能量测量值的硬件功率计来量化现有的基于软件的能源测量的误差。我们的关键要点是需要一个更准确的能源估计模型，该模型考虑了硬件变化以及资源利用和能源消耗之间的非线性关系。我们在https://github.com/csarron/sustainlp2020-energy上发布代码和数据。
Accurate and reliable measurement of energy consumption is critical for making well-informed design choices when choosing and training large scale NLP models. In this work, we show that existing software-based energy measurements are not accurate because they do not take into account hardware differences and how resource utilization affects energy consumption. We conduct energy measurement experiments with four different models for a question answering task. We quantify the error of existing software-based energy measurements by using a hardware power meter that provides highly accurate energy measurements. Our key takeaway is the need for a more accurate energy estimation model that takes into account hardware variabilities and the non-linear relationship between resource utilization and energy consumption. We release the code and data at https://github.com/csarron/sustainlp2020-energy.
