Energy Aware Scheduler of Single/Multi-Node Jobs Considering CPU Node Heterogeneity

Modern CPUs suffer from power efficiency heterogeneity, which can result in additional energy cost or performance loss. On the other hand, future supercomputers are expected to be power constrained. This paper focuses on energy aware scheduling algorithms targeted on two situations considering this node heterogeneity. In single-node situation, workload consists of various single-node jobs, Combinatorial Optimization Algorithm saves energy by calculating a local optimal power efficiency node allocation plan from KM (Kuhn-Munkres) algorithm. In multi-node situation, power cap causes load unbalancing in multi-node jobs due to the node heterogeneity. Sliding Window Algorithm targets on reducing such unbalancing by sliding window. Proposed algorithms are evaluated in the simulation and real supercomputer environment. In single-node situation, Combinatorial Optimization Algorithm achieved up to 2.92% saving. For the multi-node situation, workload is designed based on real historic workload, and up to 5.36% saving was achieved by Sliding Window Algorithm.