Benchmark-Based Study of CPU/GPU Power-Related Features Through JAX and TensorFlow

Energy has become a critical resource in the modern computing landscape, making power management a central focus in High-Performance Computing (HPC) and Artificial Intelligence (AI). While power management techniques like <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynamic Voltage and Frequency Scaling (DVFS), Power Capping</i>, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ACPI/P-State CPU governors</i> are well-established, their effectiveness is significantly influenced by the high-level structure of software frameworks. This paper presents a comprehensive empirical study of this interplay, evaluating the three aforementioned power management techniques on a dual-socket Intel Xeon "Ice Lake" CPU, a single-socket AMD EPYC "Zen3" CPU, and an NVIDIA A100 GPU. We run a suite of computational kernels using both TensorFlow and JAX to expose how framework-specific design choices mediate hardware-level power controls. Our results reveal that the best strategy for energy efficiency is highly context-dependent and relies on the specific combination of hardware, workload, and framework.We find that DVFS is the most effective on both Intel Xeon and AMD EPYC platforms, delivering significant <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Energy-Delay Product (EDP)</i> reductions with minimal performance loss. In contrast, Power Capping is the most efficient technique for NVIDIA A100. A key finding is the notable influence of the software stack; for instance, JAX exhibits operational instability at the lowest GPU frequencies on the A100, while there is no limitation with TensorFlow under identical conditions. Our findings provide operational platform-specific guidance for practitioners, expose crucial robustness considerations for framework developers, and highlight the necessity of considering the software stack as an active variable in energy-aware computing.