More Types for Nested Data Parallel Programming

Manuel M. T. Chakravarty and Gabriele Keller

In Philip Wadler, editor, Proceedings of the Fifth ACM SIGPLAN International Conference on Functional Programming, pages 94-105, ACM Press, 2000.

This paper generalises the flattening transformation - a technique for the efficient implementation of nested data parallelism - and reconciles it with main stream functional programming. Nested data parallelism is significantly more expressive and convenient to use than the flat data parallelism typically used in conventional parallel languages like High Performance Fortran and C*. The flattening transformation of Blelloch and Sabot is a key technique for the efficient implementation of nested parallelism via flat parallelism, but originally it was severely restricted, as it did not permit general sum types, recursive types, higher-order functions, and separate compilation. Subsequent work, including some of our own, generalised the transformation and allowed higher-order functions and recursive types. In this paper, we take the final step of generalising flattening to cover the full range of types available in modern languages like Haskell and ML; furthermore, we enable the use of separate compilation. In addition, we present a completely new formulation of the transformation, which is based on the standard lambda calculus notation, and replace a previously ad-hoc transformation step by a systematic generic programming technique. First experiments demonstrate the efficiency of our approach.

Preprint PostScript version (12 pages).

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