Malcolm Wallace:
(Also... Haskell on the GPU. It's been talked about for years, but will it ever actually happen?)
gpu is just set of simd-like instructions. so the reason why you will never see haskell on gpu is the same as why you will never see it implemented via simd instructions :D
Because SIMD/GPU deals only with numbers, not pointers, you will not see much _symbolic_ computation being offloaded to these arithmetic units. But there are still great opportunities to improve Haskell's speed at numerics using them. And some symbolic problems can be encoded using integers.
There are at least two current (but incomplete) projects in this area: Sean Lee at UNSW has targetted Data Parallel Haskell for an Nvidia GPGPU, and Joel Svensson at Chalmers is developing a Haskell- embedded language for GPU programming called Obsidian.
We have a paper about the UNSW project now. It is rather high-level, but has some performance figures of preliminary benchmarks: http://www.cse.unsw.edu.au/~chak/papers/LCGK09.html BTW, this is currently independent of Data Parallel Haskell. It is a flat data-parallel array language embedded in Haskell. The language is restricted in a manner that we can generate GPU code (CUDA to be precise) from it. In the longer run, we want to turn this into a backend of Data Parallel Haskell, but that will require quite a bit more work. Manuel