Parallel Programming in Haskell Almost for Free: an embedding of Intel's Array Building Blocks
Paper in 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.