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Title: miniGAN: a proxy application for generative adversarial networks

Abstract

miniGAN is a python-based machine learning proxy application for generative adversarial networks, developed through the Exascale Computing Project's (ECP) ExaLearn project. It will be included in the main ECP proxy application and the machine learning proxy application suite. It is a proxy for ECP cosmological(CosmoFlow, ExaGAN) and wind energy(ExaWind) applications. miniGAN will be distributed to ECP hardware vendors as part of hardware codesign. miniGAN uses the Numpy/PyTorch/TensorFlow/Keras/Horovod frameworks and libraries. It also relies on the Kokkos and Kokkos-Kernels packages developed here at Sandia Labs. SAND2020-2038 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Developers:
 [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Release Date:
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Programming Languages:
C++
Version:
1.0.0
Licenses:
BSD 3-clause "New" or "Revised" License
Sponsoring Org.:
USDOE

Primary Award/Contract Number:
NA0003525
Code ID:
46929
Site Accession Number:
SCR#2460
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Country of Origin:
United States

Citation Formats

Ellis, John, Rajamanickam, Sivasankaran, and USDOE. miniGAN: a proxy application for generative adversarial networks. Computer software. https://www.osti.gov//servlets/purl/1700663. Vers. 1.0.0. USDOE. 18 Feb. 2020. Web. doi:10.11578/dc.20201030.10.
Ellis, John, Rajamanickam, Sivasankaran, & USDOE. (2020, February 18). miniGAN: a proxy application for generative adversarial networks (Version 1.0.0) [Computer software]. https://www.osti.gov//servlets/purl/1700663. https://doi.org/10.11578/dc.20201030.10
Ellis, John, Rajamanickam, Sivasankaran, and USDOE. miniGAN: a proxy application for generative adversarial networks. Computer software. Version 1.0.0. February 18, 2020. https://www.osti.gov//servlets/purl/1700663. doi:https://doi.org/10.11578/dc.20201030.10.
@misc{osti_1700663,
title = {miniGAN: a proxy application for generative adversarial networks, Version 1.0.0},
author = {Ellis, John and Rajamanickam, Sivasankaran and USDOE},
abstractNote = {miniGAN is a python-based machine learning proxy application for generative adversarial networks, developed through the Exascale Computing Project's (ECP) ExaLearn project. It will be included in the main ECP proxy application and the machine learning proxy application suite. It is a proxy for ECP cosmological(CosmoFlow, ExaGAN) and wind energy(ExaWind) applications. miniGAN will be distributed to ECP hardware vendors as part of hardware codesign. miniGAN uses the Numpy/PyTorch/TensorFlow/Keras/Horovod frameworks and libraries. It also relies on the Kokkos and Kokkos-Kernels packages developed here at Sandia Labs. SAND2020-2038 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.},
url = {https://www.osti.gov//servlets/purl/1700663},
doi = {10.11578/dc.20201030.10},
url = {https://www.osti.gov/biblio/1700663}, year = {Tue Feb 18 00:00:00 EST 2020},
month = {Tue Feb 18 00:00:00 EST 2020},
note =
}