Reference : Performance Modeling of Weather Forecast Machine Learning for Efficient HPC
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/52591
Performance Modeling of Weather Forecast Machine Learning for Efficient HPC
English
Panner Selvam, Karthick mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN >]
Brorsson, Mats Hakan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN >]
13-Oct-2022
42nd
Performance Modeling of Weather Forecast Machine Learning for Efficient HPC
Panner Selvam, Karthick mailto
Brorsson, Mats Hakan mailto
IEEE
International Conference on Distributed Computing Systems (ICDCS)
1268-1269
Yes
International
978-1-6654-7177-0
Bologna
Italy
2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)
10-07-2022 to 13-07-2022
IEEE
Bologna
Italy
[en] Performance modeling ; Deep learning ; High performance computing
[en] High-performance computing is a prime area for many applications. Majorly, weather and climate forecast applications use the HPC system because it needs to give a good result with low latency. In recent years machine learning and deep learning models have been widely used to forecast the weather. However, to the best of the author’s knowledge, many applications do not effectively utilise the HPC system for training, testing, validation, and inference of weather data. Our experiment is to conduct performance modeling and benchmark analysis of weather and climate forecast machine learning models and determine the characteristics between the application, model and the underlying HPC system. Our results will help the researchers improvise and optimise the weather forecast system and use the HPC system efficiently.
University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >
Fonds National de la Recherche - FnR
Maelstrom
Researchers ; Students
http://hdl.handle.net/10993/52591
10.1109/ICDCS54860.2022.00127
FnR ; FNR15092355 > Mats Brorsson > MAELSTROM > Machine Learning For Scalable Meteorology And Climate > 01/04/2021 > 31/03/2023 > 2020

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