An energy efficient task scheduling scheme for heterogeneous GPU-enhanced clusters

GPU-enhanced clusters as mainstream components in the HPC field are expected to be heterogeneous in system layer and node layer as the evolvement of processing elements (CPUs and GPUs) and the expansion of nodes. In this paper, we proposed a flexible energy efficient task scheduling scheme for heterogeneous tasks in the heterogeneous GPU-enhanced clusters. A system model and a task model for the heterogeneous clusters are formulated in this paper. According to the node selection policy based on GPUs utilization of the particular task, it can decrease the static energy consumption of GPUs in idle status. By the division of task types and buddy allocation, it can improve the utilization of the CPU resource to increase energy efficiency. It can change the dynamic voltage and frequency scaling feature in accordance to the load of system. What's more, the scheme can switch between load balancing and dynamic resource scaling easily and is friendly and compatible with algorithm-level and instruction-level energy optimization for GPUs.