Extracting Practical, Actionable Energy Insights from Supercomputer Telemetry and Logs

TL;DR

This paper presents a data analysis method for supercomputer telemetry that reduces data volume significantly while uncovering actionable insights for GPU power efficiency and workload optimization.

Contribution

It introduces a practical approach combining data preprocessing and statistical analysis to optimize GPU power consumption in large-scale supercomputers.

Findings

94% data volume reduction while preserving insights

Identified opportunities to reduce idle power costs

Recommended power strategies at the job level

Abstract

As supercomputers grow in size and complexity, power efficiency has become a critical challenge, particularly in understanding GPU power consumption within modern HPC workloads. This work addresses this challenge by presenting a data co-analysis approach using system data collected from the Polaris supercomputer at Argonne National Laboratory. We focus on GPU utilization and power demands, navigating the complexities of large-scale, heterogeneous datasets. Our approach, which incorporates data preprocessing, post-processing, and statistical methods, condenses the data volume by 94% while preserving essential insights. Through this analysis, we uncover key opportunities for power optimization, such as reducing high idle power costs, applying power strategies at the job-level, and aligning GPU power allocation with workload demands. Our findings provide actionable insights for energy-efficient computing and offer a practical, reproducible approach for applying existing research to optimize system performance.