Power-Aware Computing on GPGPU Systems Using ML Classification Techniques

General-purpose graphical processing units (GPGPUs) have become increasingly valuable platforms for accelerating parallel deep learning applications in recent years. GPUs are energy efficient if the software utilizes all usable resources; however, there are no hardware frameworks to adjust resources according to the application’s requirements. In this paper, we address a robust methodology for optimizing the structure of GPGPUs using machine learning (ML) techniques. The candidate model allows the use of output measurements from the base GPU configuration to determine the optimal GPU resources between three classes (large, medium, and small). We infer from the experimental results that, while we can achieve substantial power-aware computing, they can be significant while running the program on its suitable GPU setup. The ML-classification approach is tested under different CUDA application workloads to validate its effectiveness as well as GPU energy consumption and instruction per cycle metrics.