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Performance Modeling of Weather Forecast Machine Learning for Efficient HPC
PANNER SELVAM, Karthick; BRORSSON, Mats Hakan
2022In International Conference on Distributed Computing Systems (ICDCS), Italy 10-13 July 2022
Peer reviewed
 

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Keywords :
Performance modeling; Deep learning; High performance computing
Abstract :
[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.
Research center :
University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >
Disciplines :
Computer science
Author, co-author :
PANNER SELVAM, Karthick ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN
BRORSSON, Mats Hakan  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN
External co-authors :
no
Language :
English
Title :
Performance Modeling of Weather Forecast Machine Learning for Efficient HPC
Publication date :
13 October 2022
Event name :
2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)
Event organizer :
IEEE
Event place :
Bologna, Italy
Event date :
10-07-2022 to 13-07-2022
Audience :
International
Main work title :
International Conference on Distributed Computing Systems (ICDCS), Italy 10-13 July 2022
Publisher :
IEEE, Bologna, Italy
Edition :
42nd
ISBN/EAN :
978-1-6654-7177-0
Pages :
1268-1269
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
FnR Project :
FNR15092355 - Machine Learning For Scalable Meteorology And Climate, 2020 (01/04/2021-31/03/2024) - Mats Brorsson
Name of the research project :
MAELSTROM
Funders :
FNR - Fonds National de la Recherche
Available on ORBilu :
since 29 October 2022

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