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Enhancing Intelligent Data Acquisition in JET’s Correlation Reflectometer Using GPU Parallelism

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This research paper explores the use of graphic processing units (GPUs) to improve the performance of intelligent data acquisition systems in the Joint European Torus (JET) correlation reflectometer. By leveraging the parallel computing capabilities of GPUs, the authors present methodologies for efficient data handling through CPU-to-GPU and GPU-to-CPU transfers. This approach enhances real-time data processing, leading to more effective monitoring and analysis in fusion experiments. The study involves collaboration among institutions focusing on advancing fusion technologies and optimizing computational resources.

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Enhancing Intelligent Data Acquisition in JET’s Correlation Reflectometer Using GPU Parallelism

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Presentation Transcript

  1. Exploiting graphic processing units parallelism to improve intelligent data acquisition system performance in JET’s correlation reflectometer J. Nieto1, G. de Arcas1, J. Vega2,M. Ruiz1, J.M. López1, E. Barrera1, A. Murari3, A. Fonseca4, and JET EFDA contributors 1 Universidad Politécnica de Madrid 2 Asociación EURATOM/CIEMAT para Fusión 3 Consorzio RFX – Associazione EURATOM ENEA per la Fusione 4 Associação EURATOM / IST

  2. iDAQ

  3. Objectives and methodology

  4. Implementation Resources setup Transfer CPU->GPU Free resources Transfer GPU->CPU DLL in CUDA device

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