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Numerical weather model BRAMS evaluation on many-core architectures: a micro and macro vision

Authors:

Eugênio Sper De Almeida,

Michael Anthony Bauer,

Alvaro Luiz FazendaAuthors Info & Claims

International Journal of Computational Science and Engineering, Volume 12, Issue 4

Pages 330 - 340

https://doi.org/10.1504/IJCSE.2016.076940

Published: 01 January 2016 Publication History

Abstract

This paper investigates the performance of a weather forecasting application Brazilian developments on the regional atmospheric modelling system - BRAMS on high performance computing HPC clusters with a multi-core architecture. We simulated atmosphere conditions over South America for 24 hours ahead using the BRAMS, aiming to understand the impact of different architectural configurations on performance and scalability. Our analyses consider execution in intra-node and inter-node configurations of a cluster with 24 cores per node. Results reveal differences in the BRAMS performance caused by interconnection. The BRAMS may get better performance by using a newer version of MPI library implementation one-copy schema and improving spatial resolution.

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Cited By

(2018)Predicting rainfall using neural netsInternational Journal of Computational Science and Engineering10.5555/3292834.329283517:4(353-364)Online publication date: 20-Dec-2018
https://dl.acm.org/doi/10.5555/3292834.3292835
(2018)Automatic identification and classification of Palomar Transient Factory astrophysical objects in GLADEInternational Journal of Computational Science and Engineering10.1504/IJCSE.2018.1001495516:4(337-349)Online publication date: 20-Dec-2018
https://dl.acm.org/doi/10.1504/IJCSE.2018.10014955

Numerical weather model BRAMS evaluation on many-core architectures: a micro and macro vision

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Published In

cover image International Journal of Computational Science and Engineering

International Journal of Computational Science and Engineering Volume 12, Issue 4

January 2016

105 pages

ISSN:1742-7185

EISSN:1742-7193

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Inderscience Publishers

Geneva 15, Switzerland

Publication History

Published: 01 January 2016

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Cited By

(2018)Predicting rainfall using neural netsInternational Journal of Computational Science and Engineering10.5555/3292834.329283517:4(353-364)Online publication date: 20-Dec-2018
https://dl.acm.org/doi/10.5555/3292834.3292835
(2018)Automatic identification and classification of Palomar Transient Factory astrophysical objects in GLADEInternational Journal of Computational Science and Engineering10.1504/IJCSE.2018.1001495516:4(337-349)Online publication date: 20-Dec-2018
https://dl.acm.org/doi/10.1504/IJCSE.2018.10014955

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