Department of Energy
Dataset Description
The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects.
BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.
Organization: Department of Energy
Organization URL: https://catalog.data.gov/organization/energy
Last updated: 2024-10-07T15:12:02Z
Tags: BUTTER, BUTTER-E, benchmark, computational science, deep learning, efficient, empirical deep learning, empirical machine learning, energy, energy consumption, energy efficiency, energy use, green computing, machine learning, model, network structure, neural networks, node-level, power, power consumption, training, training efficiency
