Fine-Grained Energy Consumption Characterization and Modeling

Energy costs comprise a significant fraction of the total cost of ownership of a large supercomputer. As with performance, energy-efficiency is not an attribute of a compute resource alone; it is a function of a resource-workload combination. The operation mix and locality characteristics of the applications in the workload affect the energy consumption of the resource. Our experiments confirm that data locality is the primary source of variation in energy requirements. The major contributions of this work include a method for performing fine-grained power measurements on high performance computing (HPC) resources, a benchmark infrastructure that exercises specific portions of the node in order to characterize operation energy costs, and a method of combining application information with independent energy measurements in order to estimate the energy requirements for specific application-resource pairings. A verification study using the NAS parallel benchmarks and S3D shows that our model has an average prediction error of 7.4%.