Phase-Based Frequency Scaling for Energy-Efficient Heterogeneous Computing

Energy efficiency has been a major challenge for exascale computing. Frequency scaling is a powerful technique to achieve energy savings in modern heterogeneous systems, and can be applied either at a coarse granularity, by application, or at a fine granularity, by setting the frequency for each computational kernel. The chosen granularity significantly impacts the performance and energy consumption of applications due to frequency-change overhead.We propose a novel phase-based method that minimizes the frequency-change overhead and improves performance and energy efficiency on heterogeneous multi-GPU systems. Our approach detects different phases through application profiling and DAG analysis, and sets an optimal frequency for each phase. Our methodology also considers MPI programs, where the overhead can be hidden by overlapping frequency-change with communication. Experimental results show up to 37% energy saving and 1.87x speedup for various benchmarks on a single GPU, and 68% energy saving and 3.63x speedup on two multi-GPU applications.