An adaptive GPU performance and power model

Abstract

Benefiting from integrating massive parallel processors, Graphics Processing Units(GPUs) have become prevalent computing devices for general-purpose parallel applications - so called GPGPU computing. While providing powerful computation capability, GPGPUs are power hungry. Almost half of the total amount of a GPGPU-based system power is consumed by GPGPU, which has seriously hindered the application of GPGPUs. As such, it's essential to build an accurate and robust model to analyze the performance and power consumption of GPGPUs. In this paper, we propose an adaptive performance and power consumption model by using random forest algorithm. The model is based on the overall GPU architecture performance counters, including multi-processor, memory access pattern and bandwidth metrics, and adapts to different NVIDIA GPU architectures. The results demonstrate that our model can achieve an average accuracy with prediction error 2.1% and 3.2% for the performance and power consumption, respectively. Furthermore, by identifying the most important impact factors and quantifying their contributions, our proposed approach can help GPGPU programmers and architects get quick insights on the performance and power consumption of GPGPU systems.