Maximizing Power-Constrained Supercomputing Throughput

Maximizing supercomputing throughput within power and cooling limits is a key challenge for exascale systems, which are increasingly constrained by power rather than performance. Effective power management is essential. Whereas power capping has been well-known to increase energy efficiency and reduce energy costs, power variability has emerged as an orthogonal driving force on cost through service pricing models and increased electronic component wear out. This paper presents a performance-power-efficiency model that combines application performance, empirical power usage, power variability, and energy efficiency in a single methodology to enable optimization of HPC system operation. Using seven workloads and three microbenchmarks, we demonstrate the ability of our methodology to understand performance and energy efficiency through power capping on NVIDIA A100 GPUs and motivate future system design and execution policies. We show that power capping can reduce power spikes without sacrificing energy efficiency, and power capping can potentially improve power-constrained system throughput by 1.8× based on capped maximum node power and 2.5× based on peak node power usage.