Prediction of Time-to-Solution in Material Science Simulations Using Deep Learning-Reference-Cited by-同舟云学术

Prediction of Time-to-Solution in Material Science Simulations Using Deep Learning

Published:2019-06-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Platform for Advanced Scientific Computing Conference
language:
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Author:

Pittino Federico¹,Bonfà Pietro²,Bartolini Andrea¹,Affinito Fabio³,Benini Luca⁴,Cavazzoni Carlo³

Affiliation:

1. Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, Bologna, Italy

2. Department of Mathematical, Physical and Computer Sciences, University of Parma, Parma, Italy

3. CINECA, Casalecchio di Reno (Bologna), Italy

4. Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, Bologna, Italy Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland

Funder

Horizon 2020 Framework Programme

H2020 Future and Emerging Technologies

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3324989.3325720

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3. P. Blaha K. Schwarz G. Madsen D. Kvasnicka and J. Luitz "Wien2k: An augmented plane wave plus local orbitals program for calculating crystal properties (technical university of vienna vienna 2001) " Google Scholar. P. Blaha K. Schwarz G. Madsen D. Kvasnicka and J. Luitz "Wien2k: An augmented plane wave plus local orbitals program for calculating crystal properties (technical university of vienna vienna 2001) " Google Scholar.

4. Survey on prediction models of applications for resources provisioning in cloud

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