Data-parallel flattening by expansion

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

We present a higher-order programmer-level technique for compiling particular kinds of irregular data-parallel problems to parallel hardware. The technique, which we have named “flattening-by-expansion” builds on a number of segmented data-parallel operations but is itself implemented as a higher-order generic function, which makes it useful for many irregular problems. Concretely, the implementation is given in Futhark and we demonstrate the usefulness of the functionality for a number of irregular problems and show that, in practice, the irregular problems are compiled to efficient parallel code that can be executed on GPUs. The technique is useful in any data-parallel language that provides a key set of primitives.

Original languageEnglish
Title of host publicationARRAY 2019 - Proceedings of the 6th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming, co-located with PLDI 2019
EditorsJeremy Gibbons
PublisherAssociation for Computing Machinery
Publication date8 Jun 2019
Pages14-24
ISBN (Electronic)9781450367172
DOIs
Publication statusPublished - 8 Jun 2019
Event6th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming, ARRAY 2019, co-located with PLDI 2019 - Phoenix, United States
Duration: 22 Jun 2019 → …

Conference

Conference6th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming, ARRAY 2019, co-located with PLDI 2019
LandUnited States
ByPhoenix
Periode22/06/2019 → …
SponsorACM SIGPLAN

    Research areas

  • Flattening, Functional programming, GPGPU programming, Irregular nested parallelism

ID: 230447605