Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training
This addresses data volume challenges for extreme-scale scientific computing, offering energy-efficient training, though it is incremental as it builds on existing sampling methods.
The paper tackles the problem of training models efficiently with less data by developing SICKLE, a framework using maximum entropy sampling, and shows that it can improve model accuracy and reduce energy consumption by up to 38x on turbulence datasets.
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.