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Energy Model for Low-Power Cluster

Energy efficiency in high performance computing (HPC) systems is a relevant issue nowadays, which is approached from multiple edges and components (network, I/O, resource management, etc). HPC industry turned its focus towards embedded and low-power computational infrastructures (of RISC architecture processors) to improve energy efficiency, therefore, we use an ARM-based cluster, known as millicluster, designed to achieve high energy efficiency with low power. We provide a model for energy consumption estimation based on experimental data, obtained of measurements performed during a benchmarking process that represents a real-world workload, such as scientific computing algorithms of artificial intelligence. The energy model enables power prediction of tasks in low-power nodes with high accuracy, and its implementation in a job scheduling algorithm of HPC, facilitates the optimization of energy consumption and performance metrics at the same time.