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BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

Publicly accessible License 

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

National Renewable Energy Laboratory. (2022). BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset [data set]. Retrieved from https://dx.doi.org/10.25984/2329316.
Export Citation to RIS
Tripp, Charles, Perr-Sauer, Jordan, Bensen, Erik, Gafur, Jamil, Nag, Ambarish, and Purkayastha, Avi. BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset. United States: N.p., 30 Dec, 2022. Web. doi: 10.25984/2329316.
Tripp, Charles, Perr-Sauer, Jordan, Bensen, Erik, Gafur, Jamil, Nag, Ambarish, & Purkayastha, Avi. BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset. United States. https://dx.doi.org/10.25984/2329316
Tripp, Charles, Perr-Sauer, Jordan, Bensen, Erik, Gafur, Jamil, Nag, Ambarish, and Purkayastha, Avi. 2022. "BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset". United States. https://dx.doi.org/10.25984/2329316. https://data.openei.org/submissions/5991.
@div{oedi_5991, title = {BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset}, author = {Tripp, Charles, Perr-Sauer, Jordan, Bensen, Erik, Gafur, Jamil, 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.}, doi = {10.25984/2329316}, url = {https://data.openei.org/submissions/5991}, journal = {}, number = , volume = , place = {United States}, year = {2022}, month = {12}}
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

Charles Tripp

National Renewable Energy Laboratory

Jordan Perr-Sauer

National Renewable Energy Laboratory

Erik Bensen

National Renewable Energy Laboratory

Jamil Gafur

National Renewable Energy Laboratory

Ambarish Nag

National Renewable Energy Laboratory

Avi Purkayastha

National Renewable Energy Laboratory

DOE Project Details

Project Name National Renewable Energy Laboratory (NREL) Lab Directed Research and Development (LDRD)

Project Number GO0028308

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