CUDA and Applications to Task-based Programming

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dc.contributor.authorKenzel, Michaelen_US
dc.contributor.authorKerbl, Bernharden_US
dc.contributor.authorWinter, Martinen_US
dc.contributor.authorSteinberger, Markusen_US
dc.contributor.editorO'Sullivan, Carol and Schmalstieg, Dieteren_US
dc.date.accessioned2021-04-25T15:49:27Z
dc.date.available2021-04-25T15:49:27Z
dc.date.issued2021
dc.description.abstractSince its inception, the CUDA programming model has been continuously evolving. Because the CUDA toolkit aims to consistently expose cutting-edge capabilities for general-purpose compute jobs to its users, the added features in each new version reflect the rapid changes that we observe in GPU architectures. Over the years, the changes in hardware, growing scope of built-in functions and libraries, as well as an advancing C++ standard compliance have expanded the design choices when coding for CUDA, and significantly altered the directives to achieve peak performance. In this tutorial, we give a thorough introduction to the CUDA toolkit, demonstrate how a contemporary application can benefit from recently introduced features and how they can be applied to task-based GPU scheduling in particular. For instance, we will provide detailed examples of use cases for independent thread scheduling, cooperative groups, and the CUDA standard library, libcu++, which are certain to become an integral part of clean coding for CUDA in the near future. https://cuda-tutorial.github.io/en_US
dc.description.sectionheadersTutorials
dc.description.seriesinformationEurographics 2021 - Tutorials
dc.identifier.doi10.2312/egt.20211037
dc.identifier.isbn978-3-03868-135-9
dc.identifier.issn1017-4656
dc.identifier.pages11-15
dc.identifier.urihttps://doi.org/10.2312/egt.20211037
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egt20211037
dc.publisherThe Eurographics Associationen_US
dc.titleCUDA and Applications to Task-based Programmingen_US
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