TY  - COMP
TI  - miniGAN: a proxy application for generative adversarial networks
AB  - 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.
AU  - Ellis, John
AU  - Rajamanickam, Sivasankaran
DO  - https://doi.org/10.11578/dc.20201030.10
UR  - https://www.osti.gov/doecode/biblio/46929
CY  - United States
PY  - 2020
DA  - 2020-02-13
LA  - English
C1  - Research Org.: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
C2  - Sponsor Org.: USDOE
C4  - Contract Number: NA0003525
ER  -