Energy efficiency has become one of the top design criteria for current computing systems. The dynamic voltage and frequency scaling (DVFS) has been widely adopted by laptop computers, servers, and mobile devices to conserve energy, while the GPU DVFS is still at a certain early age. This paper aims at exploring the impact of GPU DVFS on the application performance and power consumption, and furthermore, on energy conservation. We survey the state-of-the-art GPU DVFS characterizations, and then summarize recent research works on GPU power and performance models. We also conduct real GPU DVFS experiments on NVIDIA Fermi and Maxwell GPUs. According to our experimental results, GPU DVFS has significant potential for energy saving. The effect of scaling core voltage/frequency and memory voltage/frequency depends on not only the GPU architectures, but also the characteristic of GPU applications.
Taming Asynchronous CPU-GPU Coupling for Frequency-aware Latency Estimation on Mobile Edge
Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；INTR Thrust, System Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州）系统枢纽INTR团队）
Energy-Efficient Multimodal Inference Serving with Tri-serve
Department of Computer Science and Engineering, University of California, Riverside（加州大学河滨分校计算机科学与工程系）；NVIDIA（英伟达）；Department of Computer Science and Engineering, Indian Institute of Technology, Bhubaneswar（布巴内斯瓦尔印度理工学院计算机科学与工程系）；Department of Electrical and Computer Engineering, University of California, Riverside（加州大学河滨分校电气与计算机工程系）
