  In the exascale era in which application behavior has large power & energy
footprints, per-application job-level awareness of such impression is crucial
in taking steps towards achieving efficiency goals beyond performance, such as
energy efficiency, and sustainability.
  To achieve these goals, we have developed a novel low-latency job power
profiling machine learning pipeline that can group job-level power profiles
based on their shapes as they complete. This pipeline leverages a comprehensive
feature extraction and clustering pipeline powered by a generative adversarial
network (GAN) model to handle the feature-rich time series of job-level power
measurements. The output is then used to train a classification model that can
predict whether an incoming job power profile is similar to a known group of
profiles or is completely new. With extensive evaluations, we demonstrate the
effectiveness of each component in our pipeline. Also, we provide a preliminary
analysis of the resulting clusters that depict the power profile landscape of
the Summit supercomputer from more than 60K jobs sampled from the year 2021.
