CUDA and Applications to Task-based Programming

dc.contributor.authorKerbl, Bernharden_US
dc.contributor.authorKenzel, Michaelen_US
dc.contributor.authorWinter, Martinen_US
dc.contributor.authorSteinberger, Markusen_US
dc.contributor.editorHahmann, Stefanieen_US
dc.contributor.editorPatow, Gustavo A.en_US
dc.date.accessioned2022-04-22T11:45:55Z
dc.date.available2022-04-22T11:45:55Z
dc.date.issued2022
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.en_US
dc.description.sectionheadersTutorials
dc.description.seriesinformationEurographics 2022 - Tutorials
dc.identifier.doi10.2312/egt.20221053
dc.identifier.isbn978-3-03868-172-4
dc.identifier.issn1017-4656
dc.identifier.urihttps://doi.org/10.2312/egt.20221053
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egt20221053
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleCUDA and Applications to Task-based Programmingen_US
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