BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset
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.
Citation Formats
TY - DATA
AB - 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.
AU - Tripp, Charles
A2 - Perr-Sauer, Jordan
A3 - Bensen, Erik
A4 - Gafur, Jamil
A5 - Nag, Ambarish
A6 - Purkayastha, Avi
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO - 10.25984/2329316
KW - energy
KW - power
KW - green computing
KW - neural networks
KW - machine learning
KW - training
KW - benchmark
KW - deep learning
KW - empirical deep learning
KW - empirical machine learning
KW - energy consumption
KW - training efficiency
KW - energy efficiency
KW - efficient
KW - power consumption
KW - BUTTER
KW - model
KW - BUTTER-E
KW - node-level
KW - network structure
KW - energy use
KW - computational science
LA - English
DA - 2022/12/30
PY - 2022
PB - National Renewable Energy Laboratory
T1 - BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset
UR - https://doi.org/10.25984/2329316
ER -
Tripp, Charles, et al. BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset. National Renewable Energy Laboratory, 30 December, 2022, Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2329316.
Tripp, C., Perr-Sauer, J., Bensen, E., Gafur, J., Nag, A., & Purkayastha, A. (2022). BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset. [Data set]. Open Energy Data Initiative (OEDI). National Renewable Energy Laboratory. https://doi.org/10.25984/2329316
Tripp, Charles, Jordan Perr-Sauer, Erik Bensen, Jamil Gafur, Ambarish Nag, and Avi Purkayastha. BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset. National Renewable Energy Laboratory, December, 30, 2022. Distributed by Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2329316
@misc{OEDI_Dataset_5991,
title = {BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset},
author = {Tripp, Charles and Perr-Sauer, Jordan and Bensen, Erik and Gafur, Jamil and Nag, Ambarish and Purkayastha, Avi},
abstractNote = {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.},
url = {https://data.openei.org/submissions/5991},
year = {2022},
howpublished = {Open Energy Data Initiative (OEDI), National Renewable Energy Laboratory, https://doi.org/10.25984/2329316},
note = {Accessed: 2025-04-25},
doi = {10.25984/2329316}
}
https://dx.doi.org/10.25984/2329316
Details
Data from Dec 30, 2022
Last updated Oct 7, 2024
Submitted Mar 8, 2024
Organization
National Renewable Energy Laboratory
Contact
Charles Tripp
303.275.4082
Authors
Research Areas
Keywords
energy, power, green computing, neural networks, machine learning, training, benchmark, deep learning, empirical deep learning, empirical machine learning, energy consumption, training efficiency, energy efficiency, efficient, power consumption, BUTTER, model, BUTTER-E, node-level, network structure, energy use, computational scienceDOE Project Details
Project Name National Renewable Energy Laboratory (NREL) Lab Directed Research and Development (LDRD)
Project Number GO0028308
