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Generating Code from Textual Description of Functionality
Šamánek, Jan ; Fajčík, Martin (oponent) ; Smrž, Pavel (vedoucí práce)
As machine learning and neural network models continue to grow, there is an increasing demand for GPU-accelerated resources and algorithms to support them. Large language models have the potential to assist with this task, as they are already used as coding assistants for popular programming languages. If these models could also learn less commonly used paradigms like CUDA, they could help develop and maintain the necessary systems. This thesis aims to explore the capabilities of modern language models for learning CUDA as a programming paradigm and creating a training corpus specifically for this purpose.

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