CUDA Performance: Maximizing Instruction-Level Parallelism
1–10 of 13 posts
Re: CUDA Performance: Maximizing Instruction-Level Parallelism
#2Why is the on the frontpage? This is just a copy/dumb down of the original presentation. Also, the practical use of this idea is extremely limited.
Also the poster seems to have an agenda; this is just marketing.
Re: CUDA Performance: Maximizing Instruction-Level Parallelism
#3Why is the on the frontpage? This is just a copy/dumb down of the original presentation. Also, the practical use of this idea is extremely limited. Also the poster seems to have an agenda; this is just marketing.
Is this a novelty account? How long has news.yc had these? Is there a policy of quickly banning these things? It certainly doesn't help the quality of discourse here.
Re: CUDA Performance: Maximizing Instruction-Level Parallelism
#4Vasily's approach to CUDA really revolutionized how I think about GPU programming and I'm glad the continuum folks are giving ILP on the GPU a broader audience. Can anyone testify to the quality of continuum's CUDA wrapper? Is it nicer to work with than PyCUDA?
Re: CUDA Performance: Maximizing Instruction-Level Parallelism
#5Why is the on the frontpage? This is just a copy/dumb down of the original presentation. Also, the practical use of this idea is extremely limited. Also the poster seems to have an agenda; this is just marketing.
Is this a novelty account? How long has news.yc had these? Is there a policy of quickly banning these things? It certainly doesn't help the quality of discourse here.
[deleted]
Re: CUDA Performance: Maximizing Instruction-Level Parallelism
#6Why is the on the frontpage? This is just a copy/dumb down of the original presentation. Also, the practical use of this idea is extremely limited. Also the poster seems to have an agenda; this is just marketing.
> Also the poster seems to have an agenda;
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.
Re: CUDA Performance: Maximizing Instruction-Level Parallelism
#7Why is the on the frontpage? This is just a copy/dumb down of the original presentation. Also, the practical use of this idea is extremely limited. Also the poster seems to have an agenda; this is just marketing.
It's hard to see how you could have read the article given your comment. This is certainly not a copy of some original presentation. The article may not be motivated well and certainly can be critiqued on many levels.
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.
Re: CUDA Performance: Maximizing Instruction-Level Parallelism
#8Why is the on the frontpage? This is just a copy/dumb down of the original presentation. Also, the practical use of this idea is extremely limited. Also the poster seems to have an agenda; this is just marketing.
> Also the poster seems to have an agenda; 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…
I'm sorry, and you are right. What I really meant was that I dislike them not giving enough credit, thus making them look smart with Vasily's knowledge. This kind of marketing feels immoral to me.
>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.
Re: CUDA Performance: Maximizing Instruction-Level Parallelism
#9Vasily's approach to CUDA really revolutionized how I think about GPU programming and I'm glad the continuum folks are giving ILP on the GPU a broader audience. Can anyone testify to the quality of continuum's CUDA wrapper? Is it nicer to work with than PyCUDA?
I haven't dealt much with PyCUDA recently, but Continuum's wrapper is interesting in that it compiles python code (or at least a subset thereof) to run natively on the GPU, via LLVM if I'm not mistaken.
As far as I'm aware, PyCUDA only allows Python code to call pre-compiled CUDA kernels.
Re: CUDA Performance: Maximizing Instruction-Level Parallelism
#10Vasily's approach to CUDA really revolutionized how I think about GPU programming and I'm glad the continuum folks are giving ILP on the GPU a broader audience. Can anyone testify to the quality of continuum's CUDA wrapper? Is it nicer to work with than PyCUDA?
I haven't dealt much with PyCUDA recently, but Continuum's wrapper is interesting in that it compiles python code (or at least a subset thereof) to run natively on the GPU, via LLVM if I'm not mistaken. As far as I'm aware, PyCUDA only allows Python code to call pre-compiled CUDA kernels.
A labmate did some great work similar to Continuum's wrapper and has been continuing on now at NVIDIA: http://copperhead.github.io/ . He basically identified an ML-like subset of Python (sort of like asm.js vs js) and specializes it.
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 ;-)