  Increased reliance on graphics processing units (GPUs) for high-intensity
computing tasks raises challenges regarding energy consumption. To address this
issue, dynamic voltage and frequency scaling (DVFS) has emerged as a promising
technique for conserving energy while maintaining the quality of service (QoS)
of GPU applications. However, existing solutions using DVFS are hindered by
inefficiency or inaccuracy as they depend either on dynamic or static
information respectively, which prevents them from being adopted to practical
power management schemes. To this end, we propose a novel energy efficiency
optimizer, called DSO, to explore a light weight solution that leverages both
dynamic and static information to model and optimize the GPU energy efficiency.
DSO firstly proposes a novel theoretical energy efficiency model which reflects
the DVFS roofline phenomenon and considers the tradeoff between performance and
energy. Then it applies machine learning techniques to predict the parameters
of the above model with both GPU kernel runtime metrics and static code
features. Experiments on modern DVFS-enabled GPUs indicate that DSO can enhance
energy efficiency by 19% whilst maintaining performance within a 5% loss
margin.
