Performance Modeling of Weather Forecast Machine Learning for Efficient HPC
English
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 >]
13-Oct-2022
42nd
Performance Modeling of Weather Forecast Machine Learning for Efficient HPC
Panner Selvam, Karthick
Brorsson, Mats Hakan
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.
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