Power capping of CPU-GPU heterogeneous systems through coordinating DVFS and task mapping

Future computer systems are built under much stringent power budget due to the limitation of power delivery and cooling systems. To this end, sophisticated power management techniques are required. Power capping is a technique to limit the power consumption of a system to the predetermined level, and has been extensively studied in homogeneous systems. However, few studies about the power capping of CPU-GPU heterogeneous systems have been done yet. In this paper, we propose an efficient power capping technique through coordinating DVFS and task mapping in a single computing node equipped with GPUs. In CPU-GPU heterogeneous systems, settings of the device frequencies have to be considered with task mapping between the CPUs and the GPUs because the frequency scaling can incurs load imbalance between them. To guide the settings of DVFS and task mapping for avoiding power violation and the load imbalance, we develop new empirical models of the performance and the maximum power consumption of a CPU-GPU heterogeneous system. The models enable us to set near-optimal settings of the device frequencies and the task mapping in advance of the application execution. We evaluate the proposed technique with five data-parallel applications on a machine equipped with a single CPU and a single GPU. The experimental result shows that the performance achieved by the proposed power capping technique is comparable to the ideal one.