NREL High-Performance Computing Facility

National Laboratory: 
National Renewable Energy Laboratory
Computational Tools Class: 
Data Tools
Structure-Properties
Description: 

NREL's high-performance computing (HPC) system and User Application, Data and Learning Support Capability enable researchers to exploit the potential of the largest HPC environment in the world dedicated to advancing renewable energy and energy efficiency technologies research. The 2500-node, 2.3 PFlOps Peregrine system hosts a wide range of functionality for materials science computing to support everything from single-server interactive work through scalable batch simulation, data analytics and learning. Beyond general capabilities in technical computing and HPC, NREL’s Computational Science Center also hosts expertise in modeling, simulation, and analysis in chemistry, physics, and materials science.

Capability Bounds: 

Datasets 100's TB; job sizes best at or below 256 nodes to balance throughput and capability.

Unique Aspects: 

Facility focus on technical computing, with high-performance scalable computing an important but non-exclusive part. Shared licensing and deployment of common ISV and open-source codes across materials science and chemistry to address scales from quantum to continuum.

Availability: 

Access to Peregrine is obtained via allocation requests. Details of expertise available may be found here.

Single Point of Contact: 

Name: Chris Chang, Sr. Scientist, Computational Science Center
Email: christopher.chang@nrel.gov
Phone: (303) 275-3751

References: 
  1. Regimbal K, I Carpenter, CH Chang & S Hammond (2015) Peregrine at the National Renewable Energy Laboratory. In Contemporary High Performance Computing: From Petascale to Exascale Vol. 2, JS Vetter, ed. Chapman & Hall/CRC Computational Science Series. Taylor & Francis. https://www.crcpress.com/Contemporary-High-Performance-Computing-From-Petascale-toward-Exascale/Vetter/p/book/9781498700627
  2. McKinney RW, P Gorai, V Stevanović & ES Toberer (2017) Search for New Thermoelectric Materials with Low Lorenz Number. J. Mater. Chem. A 5: 17302–17311. http://dx.doi.org/10.1039/C7TA04332E
  3. Brandt RE, RC Kurchin, V Steinmann, D Kitchaev, C Roat, S Levcenco, G Ceder, T Unold & T Buonassisi (2017) Rapid Photovoltaic Device Characterization through Bayesian Parameter Estimation. Joule 1(4): 843–856. http://dx.doi.org/10.1016/j.joule.2017.10.001