Teaching Accelerated Computing and Deep Learning at a Large-Scale with the NVIDIA Deep Learning Institute

Volume 14, Issue 1 (July 2023), pp. 23–30

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@article{jocse-14-1-4,
  author={B\'{a}lint Gyires-T\'{o}th and I\c{s}\imathl \"{O}z and Joe Bungo},
  title={Teaching Accelerated Computing and Deep Learning at a Large-Scale with the NVIDIA Deep Learning Institute},
  journal={The Journal of Computational Science Education},
  year=2023,
  month=jul,
  volume=14,
  issue=1,
  pages={23--30},
  doi={https://doi.org/10.22369/issn.2153-4136/14/1/4}
}

Researchers and developers in a variety of fields have benefited from the massively parallel processing paradigm. Numerous tasks are facilitated by the use of accelerated computing, such as graphics, simulations, visualisations, cryptography, data science, and machine learning. Over the past years, machine learning and in particular deep learning have received much attention. The development of such solutions requires a different level of expertise and insight than that required for traditional software engineering. Therefore, there is a need for novel approaches to teaching people about these topics. This paper outlines the primary challenges of accelerated computing and deep learning education, discusses the methodology and content of the NVIDIA Deep Learning Institute, presents the results of a quantitative survey conducted after full-day workshops, and demonstrates a sample adoption of DLI teaching kits for teaching heterogeneous parallel computing.