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Comment by CoolGuySteve | Hacker News Reader

R doesn't even work that well on Intel, at least in Ubuntu. Recompiling the package with AVX support often leads to a 30% performance increase on modern CPUs.

IMO the R base package should dynlink different shared libraries for different processors since vector extensions are mostly tailored to the kind of floating point numerical work that R does.

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That's deliberate though, when you distribute software you choose the lowest common denominator in general, and that's SSE2 for 64 bit machines.

Many linear algebra libraries will handle this at runtime for you (OpenBLAS & MKL do this for example). You generally only need to use specialized builds of these if you don't want to have to ship extra code paths you won't use.

See, this is why Gentoo is the right way to manage an OS. /s

The SIMD makes little difference to the bulk of the system, but you want dynamic dispatch where it does, at least on a typical HPC system, which isn't heterogeneous five years down the line, and in things like libc generally.

When you install Python's numpy, I'm pretty sure it chooses a pre-built package based on your hardware, and if it doesn't have one I think it's pretty easy to get it to build the best one from scratch.

It generally installs MKL if you install a wheel (i.e. 'pip install numpy') these days, which dynamic dispatches based on the processor. It's been criticised a bit though because MKL doesn't perform as well on AMD hardware without setting some environment variables in the older versions, although it looks like they've added kernels that target AMD hardware recently.

The thing that makes a significant difference is BLAS, and it's easy to substitute. There are some old numbers at https://loveshack.fedorapeople.org/blas-subversion.html#_add... Most of it is unlikely to benefit much from -mavx and vectorization, but I have no numbers. -fno-semantic-interposition is probably a better candidate, which I've not got round to trying.

Looks like I need to compile R and my libraries with AVX2 support.

# list available instructions

    $ gcc -march=native -dM -E - < /dev/null | egrep "SSE|AVX" | sort

This post has some info, but no benchmarks. https://stackoverflow.com/questions/37213060/does-r-leverage...

Same can be said for Python, tensorflow jumps to mind.

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