Energy-Aware Automatic Tuning of Many-Core Platform via Gradient Descent
Samer Akiki, Zhiliu Yang, Chen Liu, Jie Tang, Shaoshan Liu · 2018
Even though attaining high performance has been the user's pursuit traditionally, in the many-core era, the emphasis has shifted towards controlling the power and energy consumption, so as to maintain a satisfying performance while consuming an acceptable amount of energy. This paper describes an auto-tuning algorithm for the energy efficiency optimization of many-core platform, in this case, a Graphic Processing Unit (GPU). We employed gradient descent algorithm as the basis for this optimization. Metrics such as energy and energy delay product (EDP) are examined using programs representing different types of workloads such as sequential, parallel and hybrid. Based on the experimental results, our method achieves the level of savings over 15% in terms of energy consumption when compared with the default on-board governors that also adjust the voltage and frequency of the GPU. Our approach shows an advantage when optimizing towards EDP as well. This shows the effectiveness of our proposed approach.