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NLR HPC Eagle GPU Node Metrics

Ganglia node metrics and iLO (Integrated Lights Out) power data captured from six representative Eagle GPU nodesThe Eagle HPC operated at NLR from 2019 through 2024. Eagle was a 2,000-node, 8-petaflop system. This dataset is a representative sample of metrics for 6 of the GPU node...

Clark, S. National Laboratory of the Rockies

Jan 29, 2026
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Lignin Forcefield Source Datafiles

Three archives are provided here comprising the development of a CHARMM36-compatible lignin forcefield suitable for use in biomolecular simulations. One archive parameters.zip contains the output topology and parameter files in CHARMM format suitable for use in constructing simula...

Crowley, M. et al National Renewable Energy Laboratory

Apr 30, 2018
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NLR HPC Kestrel Jobs Data

Overview: Anonymized job-level records from the Kestrel HPC system at the National Laboratory of the Rockies (NLR). Each record represents a Slurm batch job with scheduling metadata, resource requests, utilization, energy estimates, and efficiency metrics. Sensitive fields (user, ...

Clark, S. et al National Laboratory of the Rockies

Mar 17, 2026
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NLR HPC Eagle Jobs Data and Additional Energy Metrics

Overview: Anonymized job-level records from the Eagle high-performance computing (HPC) system at the National Laboratory of the Rockies (NLR). Each record represents a Slurm batch job with scheduling metadata, resource requests, resource utilization, CPU/GPU energy consumption, an...

Clark, S. et al National Laboratory of the Rockies

Mar 17, 2026
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Dataset of Generative AI Workload Power Profiles

This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of r...

Vercellino, R. et al National Laboratory of the Rockies

Mar 31, 2026
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BUTTER Empirical Deep Learning Dataset

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four le...

Tripp, C. et al National Renewable Energy Laboratory

May 20, 2022
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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 63,52...

Tripp, C. et al National Renewable Energy Laboratory

Dec 30, 2022
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INTEGRATE Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design techno...

Vijayakumar, G. et al National Renewable Energy Laboratory (NREL)

May 04, 2021
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TEAMER: Underwater Target Detection Software Demonstration on the RivGen Turbine

This repository includes data, object detection models, and processing scripts necessary to evaluate the accuracy of the object detection models created for the underwater target detection software demonstration on the RivGen turbine project and to reproduce the performance metric...

Joslin, J. et al MarineSitu

Dec 17, 2024
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