FLEXDP: flexible frequency scaling for energy-delay product optimization of GPU applications

Dynamic frequency scaling is broadly available among different modern computer architectures, making it possible to improve the performance and energy efficiency of an application by carefully setting the core frequency. However, while an exhaustive tuning is feasible on simple single-kernel applications, in real-world applications comprised of multiple tasks, the set of possible frequency setting combinations is too large to be exhaustively evaluated. This work deals with the problem of optimizing a multi-task GPU application with frequency scaling. We focus on different scalarizations of the problem by optimizing for performance, energy consumption, as well as energy-delay product (EDP) and energy-delay-two product (ED2P). We propose FLEXDP, a new flexible framework that finds the optimal core-frequency configuration over multiple kernels, allowing multiple frequency changes between kernel executions, and taking change overheads into account. The proposed approaches are evaluated on an NVIDIA Titan X. Experimental results on five applications demonstrate that FLEXDP outperforms the default and autoboost configurations with respect to performance, energy efficiency, EDP, and ED2P