2026-08-11T08:34:49Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:1769 2024-03-06T10:33:05Z ddc:004 open_access

Harnessing the Multicores: Nested Data Parallelism in Haskell Peyton Jones, Simon Leshchinskiy, Roman Keller, Gabriele Chakravarty, Manuel M T Nested data parallelism Vectorisation Haskell Program transformation If you want to program a parallel computer, a purely functional language like Haskell is a promising starting point. Since the language is pure, it is by-default safe for parallel evaluation, whereas imperative languages are by-default unsafe. But that doesn\'t make it easy! Indeed it has proved quite difficult to get robust, scalable performance increases through parallel functional programming, especially as the number of processors increases. A particularly promising and well-studied approach to employing large numbers of processors is data parallelism. Blelloch\'s pioneering work on NESL showed that it was possible to combine a rather flexible programming model (nested data parallelism) with a fast, scalable execution model (flat data parallelism). In this paper we describe Data Parallel Haskell, which embodies nested data parallelism in a modern, general-purpose language, implemented in a state-of-the-art compiler, GHC. We focus particularly on the vectorisation transformation, which transforms nested to flat data parallelism. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Simon Peyton Jones and Roman Leshchinskiy and Gabriele Keller and Manuel M T Chakravarty 2008 Is Part Of LIPIcs, Volume 2, IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (2008) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.FSTTCS.2008.1769 urn:nbn:de:0030-drops-17697 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2008.1769 eng https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode