Power-Aware Job Scheduling on Heterogeneous Multicore Architectures

This paper presents a power-aware scheduling algorithm based on efficient distribution of the computing workload to the resources on heterogeneous CPU-GPU architectures. The scheduler manages the resources of several computing nodes with a view to reducing the peak power. The algorithm can be used in concert with adjustable power state software services in order to further reduce the computing cost during high demand periods. Although our study relies on GPU workloads, the approach can be extended to other heterogeneous computer architectures. The algorithm has been implemented in a real CPU-GPU heterogeneous system. Experiments prove that the approach presented reduces peak power by 10 percent compared to a system without any power-aware policy and by up to 24 percent with respect to the worst case scenario with an execution time increase in the range of 2 percent. This leads to a reduction in the system and service costs.