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

Abstract

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 graph- ics, simulations, visualisations, cryptography, data science, and machine learning. Over the past years, machine learning and in particular deep learning have received much attention. The de- velopment 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 ofa quantitative survey conducted after full-day workshops, and demonstrates a sample adoption of DLI teaching kits for teaching heterogeneous parallel computing.