Airavat: Improving Energy Efficiency of Heterogeneous Applications
Trinayan Baruah1,a, Yifan Sun1,b, Shi Dong1,c, David Kaeli1,d and Norm Rubin2
1Northeastern University, Boston (MA), USA
atbaruah@ece.neu.edu
byifansun@ece.neu.edu
cshidong@ece.neu.edu
dkaeli@ece.neu.edu
2NVIDIA Research
nrubin@nvidia.com
ABSTRACT
An emerging class of applications attempt to make use of both the CPU and GPU in a heterogeneous system. The peak performance for these applications is achieved when both the CPU and GPU are used collaboratively. However, along with this increased gain in performance, power and energy management is a larger challenge. In this paper we address the issue of executing applications that utilize both the CPU and GPU in an energy efficient way. Towards this end, we propose a power management framework named Airavat that tunes the CPU, GPU and memory frequencies, synergestically, in order to improve the energy efficiency of collaborative CPU-GPU applications. Airavat uses machine learning‐based prediction models, combined with feedback based Dynamic Voltage and Frequency Scaling to improve the energy efficiency of such applications. We demonstrate our framework on the NVIDIA Jetson TX1 and observe an improvement in terms of Energy Delay Product (EDP) by 24% with negligible performance loss.
