Posts Tagged ‘Haskell’

Paper on Multicore Programming in Haskell – A. Al Zain, P. Trinder, M. Aswad, G. Michaelson, K. Hammond and J. Berthold

A. Al Zain, P. Trinder, M. Aswad, G. Michaelson, K. Hammond and J. Berthold “Low-Pain, High-Gain Multicore Programming in Haskell: Coordinating Irregular Symbolic Computations on MultiCore Architectures” – http://www.macs.hw.ac.uk/~greg/publications/

Tags: functional programming, Haskell

GPU Kernels as Data-Parallel Array Computations in Haskell

This paper provides a brief overview of approach to GPU programming with Haskell by embedding GPU kernels with a domain-specific language of data-parallel array computations in Haskell code. The paper also presents some preliminary benchmarks.

We present a novel high-level parallel programming model aimed at graphics processing units (GPUs). We embed GPU kernels as data-parallel array computations in the purely functional language Haskell. GPU and CPU computations can be freely interleaved with the type system tracking the two different modes of computation. The embedded language of array computations is sufficiently limited that our system can automatically extract these computations and compile them to efficient GPU code. In this paper, we outline our approach and present the results of a few preliminary benchmarks.

http://www.cse.unsw.edu.au/~chak/papers/LCGK09.html

Tags: functional programming, GPU, Haskell

Simon Peyton Jones – A Tutorial on Parallel and Concurrent Programming in Haskell

Simon Peyton Jones and Satnam Singh, Advanced Functional Progamming Summer School, Nijmegen, May 2008, LNCS

This practical tutorial introduces the features available in Haskell for writing parallel and concurrent programs. We first describe how to write semi-explicit parallel programs by using annotations to express opportunities for parallelism and to help control the granularity of parallelism for effective execution on modern operating systems and processors. We then describe the mechanisms provided by Haskell for writing explicitly parallel programs with a focus on the use of software transactional memory to help share information between threads. Finally, we show how nested data parallelism can be used to write deterministically parallel programs which allows programmers to use rich data types in data parallel programs which are automatically transformed into flat data parallel versions for efficient execution on multi-core processors.

http://research.microsoft.com/en-us/um/people/simonpj/papers/parallel/index.htm

Tags: functional programming, Haskell