Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Brewer, Wesley Gopalakrishnan, Murali Meena Maiterth, Matthias Kashi, Aditya Choi, Jong Youl Zhang, Pei Nichols, Stephen Balin, Riccardo Couchman, Miles Kops, Stephen de Bruyn Yeung, P. K. Dotson, Daniel Uma-Vaideswaran, Rohini Oral, Sarp Wang, Feiyi |
| author_facet | Brewer, Wesley Gopalakrishnan, Murali Meena Maiterth, Matthias Kashi, Aditya Choi, Jong Youl Zhang, Pei Nichols, Stephen Balin, Riccardo Couchman, Miles Kops, Stephen de Bruyn Yeung, P. K. Dotson, Daniel Uma-Vaideswaran, Rohini Oral, Sarp Wang, Feiyi |
| contents | With the end of Moore's law and Dennard scaling, efficient training increasingly requires rethinking data volume. Can we train better models with significantly less data via intelligent subsampling? To explore this, we develop SICKLE, a sparse intelligent curation framework for efficient learning, featuring a novel maximum entropy (MaxEnt) sampling approach, scalable training, and energy benchmarking. We compare MaxEnt with random and phase-space sampling on large direct numerical simulation (DNS) datasets of turbulence. Evaluating SICKLE at scale on Frontier, we show that subsampling as a preprocessing step can, in many cases, improve model accuracy and substantially lower energy consumption, with observed reductions of up to 38x. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03872 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training Brewer, Wesley Gopalakrishnan, Murali Meena Maiterth, Matthias Kashi, Aditya Choi, Jong Youl Zhang, Pei Nichols, Stephen Balin, Riccardo Couchman, Miles Kops, Stephen de Bruyn Yeung, P. K. Dotson, Daniel Uma-Vaideswaran, Rohini Oral, Sarp Wang, Feiyi Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing With the end of Moore's law and Dennard scaling, efficient training increasingly requires rethinking data volume. Can we train better models with significantly less data via intelligent subsampling? To explore this, we develop SICKLE, a sparse intelligent curation framework for efficient learning, featuring a novel maximum entropy (MaxEnt) sampling approach, scalable training, and energy benchmarking. We compare MaxEnt with random and phase-space sampling on large direct numerical simulation (DNS) datasets of turbulence. Evaluating SICKLE at scale on Frontier, we show that subsampling as a preprocessing step can, in many cases, improve model accuracy and substantially lower energy consumption, with observed reductions of up to 38x. |
| title | Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training |
| topic | Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2508.03872 |