English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Discontinuous Galerkin (DG) methods for the numerical solution of partialdifferential equations have enjoyed considerable success because they are bothflexible and robust: They allow arbitrary unstructured geometries and easycontrol of accuracy without compromising simulation stability. In a recentpublication, we have shown that DG methods also adapt readily to execution onmodern, massively parallel graphics processors (GPUs). A number of qualities ofthe method contribute to this suitability, reaching from locality of reference,through regularity of access patterns, to high arithmetic intensity. In thisarticle, we illuminate a few of the more practical aspects of bringing DG ontoa GPU, including the use of a Python-based metaprogramming infrastructure thatwas created specifically to support DG, but has found many uses across alldisciplines of computational science.

High-Order Discontinuous Galerkin Methods by GPU Metaprogramming