Reference : Performance Modeling of Weather Forecast Machine Learning for Efficient HPC

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/52591

Title :	Performance Modeling of Weather Forecast Machine Learning for Efficient HPC
Language :	English
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 >]
Publication date :	13-Oct-2022
Edition :	42nd
Main document title :	Performance Modeling of Weather Forecast Machine Learning for Efficient HPC
Author, co-author :	Panner Selvam, Karthick
	Brorsson, Mats Hakan
Publisher :	IEEE
Collection and collection volume :	International Conference on Distributed Computing Systems (ICDCS)
Pages :	1268-1269
Peer reviewed :	Yes
Audience :	International
ISBN :	978-1-6654-7177-0
City :	Bologna
Country :	Italy
Event name :	2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)
Event date :	10-07-2022 to 13-07-2022
Event organizer :	IEEE
Event place (city) :	Bologna
Event country :	Italy
Keywords :	[en] 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 centres :	University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	Maelstrom
Target :	Researchers ; Students
Permalink :	http://hdl.handle.net/10993/52591
DOI :	10.1109/ICDCS54860.2022.00127
FnR project :	FnR ; FNR15092355 > Mats Brorsson > MAELSTROM > Machine Learning For Scalable Meteorology And Climate > 01/04/2021 > 31/03/2023 > 2020

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