Reference : Performance Analysis and Benchmarking of a Temperature Downscaling Deep Learning Model

	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/10993/54334

Title :	Performance Analysis and Benchmarking of a Temperature Downscaling Deep Learning Model
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 :	Mar-2023
Main document title :	31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Naples, Italy 1-3 March 2023
Peer reviewed :	Yes
Audience :	International
Event name :	31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing
Event date :	01-03-2023 to 03-03-2023
Event place (city) :	Naples
Event country :	Italy
Keywords :	[en] Performance Analysis ; Roofline Model ; Weather Forecast ; Deep Learning Benchmark
Abstract :	[en] We are presenting here a detailed analysis and performance characterization of a statistical temperature downscaling application used in the MAELSTROM EuroHPC project. This application uses a deep learning methodology to convert low-resolution atmospheric temperature states into high-resolution. We have performed in-depth profiling and roofline analysis at different levels (Operators, Training, Distributed Training, Inference) of the downscaling model on different hardware architectures (Nvidia V100 & A100 GPUs). Finally, we compare the training and inference cost of the downscaling model with various cloud providers. Our results identify the model bottlenecks which can be used to enhance the model architecture and determine hardware configuration for efficiently utilizing the HPC. Furthermore, we provide a comprehensive methodology for in-depth profiling and benchmarking of the deep learning models.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SEDAN - Service and Data Management in Distributed Systems
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	MAELSTROM
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/54334
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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