References of "Panner Selvam, Karthick 50054550"
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See detailPerformance Analysis and Benchmarking of a Temperature Downscaling Deep Learning Model
Panner Selvam, Karthick UL; Brorsson, Mats Hakan UL

in 31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Naples, Italy 1-3 March 2023 (2023, March)

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 ... [more ▼]

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. [less ▲]

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See detailPerformance Modeling of Weather Forecast Machine Learning for Efficient HPC
Panner Selvam, Karthick UL; Brorsson, Mats Hakan UL

in International Conference on Distributed Computing Systems (ICDCS), Italy 10-13 July 2022 (2022, October 13)

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 ... [more ▼]

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. [less ▲]

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