ECP Report: Update on Proxy Applications and Vendor Interactions

Technical Report · Wed Apr 01 00:00:00 EDT 2020

DOI:https://doi.org/10.2172/1608914· OSTI ID:1608914

Ang, Jim ^[1]; Sweeney, Christine ^[2]; Wolf, Michael ^[3]; Ellis, John (Austin) ^[3]; Ghosh, Sayan ^[1]; Kagawa, Ai ^[4]; Huang, Yunzhi ^[1]; Rajamanickam, Sivasankaran ^[3]; Ramakrishnaiah, Vinay ^[2]; Schram, Malachi ^[1]; Yoo, Shinjae ^[4]

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1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
4. Brookhaven National Lab. (BNL), Upton, NY (United States)

The ExaLearn miniGAN team (Ellis and Rajamanickam) have released miniGAN, a generative adversarial network(GAN) proxy application, through the ECP proxy application suite. miniGAN is the first machine learning proxy application in the suite (note: the ECP CANDLE project did previously release some benchmarks) and models the performance for training generator and discriminator networks. The GAN's generator and discriminator generate plausible 2D/3D maps and identify fake maps, respectively. miniGAN aims to be a proxy application for related applications in cosmology (CosmoFlow, ExaGAN) and wind energy (ExaWind). miniGAN has been developed so that optimized mathematical kernels (e.g., kernels provided by Kokkos Kernels) can be plugged into to the proxy application to explore potential performance improvements. miniGAN has been released as open source software and is available through the ECP proxy application website (https://proxyapps.exascaleproject.ordecp-proxy-appssuite/) and on GitHub (https://github.com/SandiaMLMiniApps/miniGAN). As part of this release, a generator is provided to generate a data set (series of images) that are inputs to the proxy application.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000; NA0003525
• OSTI ID: 1608914
• Report Number(s): SAND-2020-3852R; 685187
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING