An energy model for graphics processing units

Abstract

We present an energy model for a graphics processing unit (GPU) that is based on the amount and type of work performed in various parts of the unit. By designing and running directed tests on a GPU, we measure the energy consumed when performing different arithmetic and memory operations, allowing us to accurately predict the energy that any arbitrary mix of operations will take. With some knowledge of how data travels through and is transformed by the graphics pipeline, we can predict how many of each operation will occur for a given scene, leading to an estimate of the energy usage. We validate our model against different types of existing graphical applications. With an average difference of 3% from measured energy under typical workloads, our model can be used for various purposes. In this work, we explore and present two use cases: 1) predicting the energy performance of applications on a different architecture, and 2) exploring the energy efficiency of different algorithms to achieve the same graphical effect.