CLIP: Cluster-Level Intelligent Power Coordination for Power-Bounded Systems

High performance computing systems will need to operate with certain power budgets while maximizing performance in the exascale era. Such systems are built with power aware components, whose collective peak power may exceed the specified power budget. Cluster level power bounded computing addresses this power challenge by coordinating power among components within compute nodes and further adjusting the number of participating nodes. It offers more space to increase system performance by utilizing the available power budget more efficiently within and across the nodes.In this paper, we present the design of a hierarchical multi-dimensional power aware allocation framework, CLIP, for power bounded parallel computing on multicore-based computer clusters. The framework satisfies the specified power bound by managing the power distribution among nodes at the cluster level, and among sockets, cores and NUMA memory modules at the node level. The power allocation is enforced with multiple complementary power management techniques, including memory power level setting, thread concurrency throttling, and core-thread affinity. We present an application characterization method based on applications' scalability and an associated performance model, which can accurately determine the optimal number of participating compute nodes and components, and their power distribution for given applications. Experimental results on a Haswell-based computer cluster show that the proposed scheduler outperforms compared methods by over 20% on average for various power budgets.