The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

TL;DR

This empirical study investigates how GPU Dynamic Voltage and Frequency Scaling (DVFS) affects energy consumption and performance in deep learning, revealing significant energy savings across various GPU architectures and DNN configurations.

Contribution

The paper provides a comprehensive empirical analysis of GPU DVFS impact on deep learning, highlighting optimal frequency settings for energy efficiency during training and inference.

Findings

Optimal core frequency reduces energy consumption by up to 23.1%.

Energy savings during inference range from 19.6% to 26.4%.

GPU DVFS can significantly enhance energy efficiency in deep learning workflows.

Abstract

Over the past years, great progress has been made in improving the computing power of general-purpose graphics processing units (GPGPUs), which facilitates the prosperity of deep neural networks (DNNs) in multiple fields like computer vision and natural language processing. A typical DNN training process repeatedly updates tens of millions of parameters, which not only requires huge computing resources but also consumes significant energy. In order to train DNNs in a more energy-efficient way, we empirically investigate the impact of GPU Dynamic Voltage and Frequency Scaling (DVFS) on the energy consumption and performance of deep learning. Our experiments cover a wide range of GPU architectures, DVFS settings, and DNN configurations. We observe that, compared to the default core frequency settings of three tested GPUs, the optimal core frequency can help conserve 8.7%$\sim$23.1% energy consumption for different DNN training cases. Regarding the inference, the benefits vary from 19.6%$\sim$26.4%. Our findings suggest that GPU DVFS has great potentials to help develop energy efficient DNN training/inference schemes.