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Data-Parallel Training of Generative Adversarial Networks on HPC Systems for HEP Simulations
In the field of High Energy Physics (HEP), simulating the interaction of particle detector materials is a compute-intensive task, that currently uses 50% of the computing resources globally available as part of the Worldwide LCH Computing Grid (WLCG). Since some level of approximation is acceptable,...
| Autores principales: | , , , |
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| Lenguaje: | eng |
| Publicado: |
2018
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1109/hipc.2018.00026 http://cds.cern.ch/record/2838905 |