Energy-aware automatic tuning on many-core platform via adaptive evolution
by Chen Liu; Zhiliu Yang; Yijun Jiang
International Journal of Embedded Systems (IJES), Vol. 14, No. 1, 2021

Abstract: 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 to maintain a satisfying performance while consuming an acceptable amount of energy. This applies to both high performance and mobile computing platforms. To achieve this goal, we propose evolution algorithm based automatic tuning as one feasible solution for energy-aware computing on many-core microprocessors. In this paper, we present several auto-tuning approaches employing differential evolution (DE) algorithms and genetic algorithm (GA). Our target is to approach the optimal setting of different power islands on a many-core platform as fast as possible when running multiple programs. Comparing with brutal-force approaches, our solution has the advantage of fast converging speed without the need to traverse the entire search space, and runtime tuning without a priori knowledge of the software workload. Our experimental results show that adaptive differential evolution algorithm is able to achieve reduced energy consumption as well as better energy-delay product (EDP) than other representative algorithms that we examined. Based on the results we obtained, we believe adaptive evolution based auto-tuning approach is an effective method towards energy-efficient computing on many-core platforms.

Online publication date: Tue, 22-Dec-2020

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