Profiling and Modeling of Power Characteristics of Leadership-Scale HPC System Workloads
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ID: 281937
2024
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Abstract
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.
| Reference Key |
wang2024profiling
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| Authors | Ahmad Maroof Karimi; Naw Safrin Sattar; Woong Shin; Feiyi Wang |
| Journal | arXiv |
| Year | 2024 |
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