  In the face of surging power demands for exascale HPC systems, this work
tackles the critical challenge of understanding the impact of software-driven
power management techniques like Dynamic Voltage and Frequency Scaling (DVFS)
and Power Capping. These techniques have been actively developed over the past
few decades. By combining insights from GPU benchmarking to understand
application power profiles, we present a telemetry data-driven approach for
deriving energy savings projections. This approach has been demonstrably
applied to the Frontier supercomputer at scale. Our findings based on three
months of telemetry data indicate that, for certain resource-constrained jobs,
significant energy savings (up to 8.5%) can be achieved without compromising
performance. This translates to a substantial cost reduction, equivalent to
1438 MWh of energy saved. The key contribution of this work lies in the
methodology for establishing an upper limit for these best-case scenarios and
its successful application. This work sheds light on potential energy savings
and empowers HPC professionals to optimize the power-performance trade-off
within constrained power budgets, not only for the exascale era but also
beyond.
