# Profiling and Modeling of Power Characteristics of Leadership-Scale HPC System Workloads

arXiv: https://arxiv.org/abs/2402.00729 · HTML: https://mlsanity.com/paper/2402.00729/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [The Unseen AI Disruptions for Power Grids: LLM-Induced Transients](https://mlsanity.com/paper/2409.11416/) (2024)
- [A Practical Two-Stage Framework for GPU Resource and Power Prediction in Heterogeneous HPC Systems](https://mlsanity.com/paper/2604.02158/) (2026)

## Abstract (arXiv)

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.