论文标题
了解HPC量表人工智能的能耗
Understanding the Energy Consumption of HPC Scale Artificial Intelligence
论文作者
论文摘要
本文有助于更好地理解HPC量表人工智能（AI）的能源消耗折衷，更具体地说是深度学习（DL）算法。为此，我们开发了基准跟踪器，这是一种基准测试工具，可以评估HPC环境中DL算法的速度和能耗。我们利用硬件计数器和Python库通过软件收集能源信息，这使我们能够启动已知的AI基准工具，并评估众多DL算法和模型的能源消耗。通过实验运动，我们展示了一个案例示例，说明了基准跟踪器测量训练和推理DL算法的计算速度和能耗的潜力，以及基准跟踪器的潜力，可以帮助更好地理解HPC平台中DL算法的能量行为。这项工作是向前迈出的一步，可以更好地了解HPC中深度学习的能源消耗，并且还提供了一种新工具，可以帮助HPC DL开发人员在速度和能源消耗方面更好地平衡HPC基础架构。
This paper contributes towards better understanding the energy consumption trade-offs of HPC scale Artificial Intelligence (AI), and more specifically Deep Learning (DL) algorithms. For this task we developed benchmark-tracker, a benchmark tool to evaluate the speed and energy consumption of DL algorithms in HPC environments. We exploited hardware counters and Python libraries to collect energy information through software, which enabled us to instrument a known AI benchmark tool, and to evaluate the energy consumption of numerous DL algorithms and models. Through an experimental campaign, we show a case example of the potential of benchmark-tracker to measure the computing speed and the energy consumption for training and inference DL algorithms, and also the potential of Benchmark-Tracker to help better understanding the energy behavior of DL algorithms in HPC platforms. This work is a step forward to better understand the energy consumption of Deep Learning in HPC, and it also contributes with a new tool to help HPC DL developers to better balance the HPC infrastructure in terms of speed and energy consumption.
