Harnessing the Multicores: Nested Data Parallelism in Haskell

Authors Simon Peyton Jones, Roman Leshchinskiy, Gabriele Keller, Manuel M T Chakravarty

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Simon Peyton Jones
Roman Leshchinskiy
Gabriele Keller
Manuel M T Chakravarty

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Simon Peyton Jones, Roman Leshchinskiy, Gabriele Keller, and Manuel M T Chakravarty. Harnessing the Multicores: Nested Data Parallelism in Haskell. In IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science. Leibniz International Proceedings in Informatics (LIPIcs), Volume 2, pp. 383-414, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)


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.
  • Nested data parallelism
  • Vectorisation
  • Haskell
  • Program transformation


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