Energy conservation for GPU-CPU architectures with dynamic workload division and frequency scaling

In recent years, GPU-CPU heterogeneous architectures have been increasingly adopted in high performance computing, because of their capabilities of providing high computational throughput. However, the energy consumption is a major concern due to the large scale of such kind of systems. There are a few existing efforts that try to lower the energy consumption of GPU-CPU architectures, but they address either GPU or CPU in an isolated manner and thus cannot achieve maximized energy savings. In this paper, we propose GreenGPU, a holistic energy management framework for GPU-CPU heterogeneous architectures. Our solution features a two-tier design. In the first tier, GreenGPU dynamically splits and distributes workloads to GPU and CPU based on the workload characteristics, such that both sides can finish approximately at the same time. We comparatively discuss four dynamic workload allocation algorithms: a Simple Heuristic with fixed step size, an Improved Heuristic with adaptive step size, and two binary search-style algorithms. As a result, the energy wasted on idling and waiting for the slower side to finish is minimized. In the second tier, GreenGPU dynamically throttles the frequencies of GPU cores and memory in a coordinated manner, based on their utilizations, for maximized energy savings with only marginal performance degradation. Likewise, the frequency and voltage of the CPU are scaled similarly. We implement GreenGPU using the CUDA framework on two real hardware testbeds. Experiment results show that GreenGPU achieves 21.04% average energy savings and outperforms several well-designed baselines.