Power management has become a crucial focus in the modern computing landscape, considering that {\em energy} is increasingly recognized as a critical resource. This increased the importance of all topics related to {\em energy-aware computing}. This paper presents an experimental study of three prevalent power management techniques that are {\em power limitation, frequency limitation}, and {\em ACPI/P-State governor modes} (OS states related to power consumption). Through a benchmark approach with a set of six computing kernels, we investigate {\em power/performance} trade-off with various hardware units and software frameworks (mainly TensorFlow and JAX). Our experimental results show that {\em frequency limitation} is the most effective technique to improve {\em Energy-Delay Product (EDP)}, which is a convolution of energy and running time. We also observe that running at the highest frequency compared to a reduced one could lead to a reduction of factor $\frac{1}{10}$ in EDP. Another noticeable fact is that frequency management shows a consistent behavior with different CPUs, whereas opposite effects sometimes occur between TensorFlow (TF) and JAX with the same power management settings.

翻译：在现代计算环境中，能源日益被视为关键资源，这使得功耗管理成为至关重要的焦点，从而提升了所有与能耗感知计算相关课题的重要性。本文通过实验研究了三种主流功耗管理技术：功率限制、频率限制以及ACPI/P状态调控模式（与功耗相关的操作系统状态）。我们采用基准测试方法，通过一组六个计算内核，探究了不同硬件单元与软件框架（主要是TensorFlow和JAX）下的功耗/性能权衡关系。实验结果表明，频率限制是改善能量延迟积（EDP，即能耗与运行时间的卷积）的最有效技术。我们还观察到，与降频运行相比，以最高频率运行可使EDP降低至原来的十分之一。另一个值得注意的现象是：频率管理在不同CPU上表现出一致的行为，而在相同功耗管理设置下，TensorFlow与JAX之间有时会产生相反的效果。