Parallel Programming in Haskell Almost for Free: an embedding of Intel's Array Building Blocks
Paper i proceeding, 2012

Nowadays, performance in processors is increased by adding more cores or wider vector units, or by combining accelerators like GPUs and traditional cores on a chip. Programming for these diverse architectures is a challenge. We would like to exploit all the resources at hand without putting too much burden on the programmer. Ideally, the programmer should be presented with a machine model abstracted from the specific number of cores, SIMD width or the existence of a GPU or not. Intel's Array Building Blocks (ArBB) is a system that takes on these challenges. ArBB is a language for data parallel and nested data parallel programming, embedded in C++. By offering a retargetable dynamic compilation framework, it provides vectorisation and threading to programmers without the need to write highly architecture specific code. We aim to bring the same benefits to the Haskell programmer by implementing a Haskell frontend (embedding) of the ArBB system. We call this embedding EmbArBB. We use standard Haskell embedded language procedures to provide an interface to the ArBB functionality in Haskell. EmbArBB is work in progress and does not currently support all of the ArBB functionality. Some small programming examples illustrate how the Haskell embedding is used to write programs. ArBB code is short and to the point in both C++ and Haskell. Matrix multiplication has been benchmarked in sequential C++, ArBB in C++, EmbArBB and the Repa library. The C++ and the Haskell embeddings have almost identical performance, showing that the Haskell embedding does not impose any large extra overheads. Two image processing algorithms have also been benchmarked against Repa. In these benchmarks at least, EmbArBB performance is much better than that of the Repa library, indicating that building on ArBB may be a cheap and easy approach to exploiting data parallelism in Haskell.

Data parallelism

array programming

embedded language

dynamic compilation


Joel Bo Svensson

Chalmers, Data- och informationsteknik, Programvaruteknik

Mary Sheeran

Chalmers, Data- och informationsteknik, Programvaruteknik

1st ACM SIGPLAN Workshop on Functional High Performance Computing, FHPC 2012. Copenhagen, 15 September 2012



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