Numba: High-Performance Python with CUDA Acceleration

Re:Numba: High-Performance Python with CUDA Acceleration by fnl on Thursday September 21, 2017 @10:51AM (#15303982)

The source of my confusion is that the last time I looked into LLVM's SIMD support was in the context of looking at Rust, a bit more than a year ago or so, and back then my conclusion was that neither (Rust or LLVM, then in version 3) are very good tools for that. It seems I was very wrong, at least on the LLVM side.

EDIT: Sorry, to reply to your question, my concern is not GCC vs. clang; If you want max out your vector ops, I would suggest you should compare to ICC as the "standard", at least on Intel CPUs.

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Re:Numba: High-Performance Python with CUDA Acceleration by radarsat1 on Thursday September 21, 2017 @01:42PM (#15305777)

Yep, I understand about icc. I actually wonder what is so difficult about optimising the way icc does, what does it actually do so much better than gcc? Anyone know of a good analysis?

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Re:Numba: High-Performance Python with CUDA Acceleration by fnl on Thursday September 21, 2017 @02:49PM (#15306313)

I'd mostly attribute that to the MKL and their ability to just have to deal with their own instructions. But that's just an "educated guess".

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Re:Numba: High-Performance Python with CUDA Acceleration by mattnewton on Thursday September 21, 2017 @08:42PM (#15308530)

Probably also having full time people working in the same company as the hardware guys who's job is to make the hardware look good.

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Re:Numba: High-Performance Python with CUDA Acceleration by radarsat1 on Friday September 22, 2017 @09:36AM (#15312010)

I understand the social reasons, but I was wondering more about what compiler does in terms of technical achievements that goes beyond what gcc/clang can successfully generate. Surely this must be something that can be studied empirically.

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