Extracting Practical, Actionable Energy Insights from Supercomputer Telemetry and Logs
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908372788314112 |
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| author | Cornelius, Melanie Cross, Greg Shilpika, Shilpika Dearing, Matthew T. Lan, Zhiling |
| author_facet | Cornelius, Melanie Cross, Greg Shilpika, Shilpika Dearing, Matthew T. Lan, Zhiling |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14796 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extracting Practical, Actionable Energy Insights from Supercomputer Telemetry and Logs Cornelius, Melanie Cross, Greg Shilpika, Shilpika Dearing, Matthew T. Lan, Zhiling Distributed, Parallel, and Cluster Computing Performance 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. |
| title | Extracting Practical, Actionable Energy Insights from Supercomputer Telemetry and Logs |
| topic | Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2505.14796 |