For me, the big surprise is that Copperhead departs from NESL-like flattening transformations (e.g., those used by Data Parallel Haskell.) It's a bit less surprising when you realize the creator is a GPU expert :)
Edit: Vasily, the guy behind the paper advertised in Continuum's blog post, is also from our lab ;-)
Also the poster seems to have an agenda; this is just marketing.
But it describes a specific reason why using a high-level language to directly program the GPU can be extremely useful --- you can easily build, test, and iterate on execution order to improve performance. Hardware is changing, and we need better tools to write code for it.
The author uses CUDA Python, but you could do similar things with PyCUDA --- it's the emphasis on the scheduling that is the relevant point.
True. Most people who take the time to write content that they publish on the internet have an agenda.
> this is just marketing.
False. This is an informative and useful summary of a 75-page deeply technical presentation into a few screens of text, and shows actual Python code (and benchmarks) to demonstrate the principles.
> Also, the practical use of this idea is extremely limited.
Care to elaborate? A substantive discussion about the subject of the original post would actually be constructive and add value for the HN community.
>Care to elaborate? This really isn't the right place for that so I didn't bother. The right place would be a thread/forum talking about the original presentation. Also there wouldn't much to elaborate since my opinion was based on general insight, it is not a provable fact.
