It looks more like optimized kernels for some operations, rather than extended functionality. Which is to say, using it shouldn't produce any lock-in for well structured projects -- it is like changing which BLAS library you've linked to.

Not sure what kind of secret sauce they've included, but it is Intel so their specific advantage is that they know everything about their processors and can provide really low level optimizations which might not necessarily be super portable.

I listened to an interesting CPPCast episode where they interviewed someone from Intel's compiler team.

(I'm just guessing that a lot of the benefit here comes from building with Intel's compiler rather than GCC.)

It sounded like the bulk of the benefits they get are just from using profile-guided optimization to maximize the cache-friendliness of the code. I would guess those kinds of optimizations are readily portable to any CPU with a similar layout and cache sizes. I would not expect, though, that they are actively detrimental (compared to whatever the official sklearn builds are doing) on CPUs that have a different cache layout.

Huh, wasn't aware of CPPCast, it seems neat. My podcast listening has mostly been politics, just because they seem to be in much greater supply. Now I just need to find a fortran cast. They could call it... FORTCAST.

There isn't one that I know of, but the recent CPPCast episode on Fortran was very good.

I know people keep saying Intel is dead, but it's not entirely accurate imo.

All of my machines still use Intels (other than my SBCs). So installing this and running it is trivial.

Intel is still a major contributor to the Linux kernel. Thus, all their CPUs have first-class support for it. AMD fired all their Linux engineers some time back. They never rehired them to my knowledge.

Then there's things like this (MKL libraries are another). Intel spends a lot more money on development of these little libraries which does meaningfully speed up processes. Those processes affect my day-to-day work as a software engineer.

That adds up when I have to deploy on the cloud. ARM is not quite there yet and little hiccups at deploy time are a pain when the cost difference is not so significant relative to the hourly cost of my time. Linus Torvalds pointed this out about ARM, stating it couldn't ever take off unless it took off on the desktop.

> AMD fired all their Linux engineers some time back. They never rehired them to my knowledge.

My understanding is that AMD regularly contributes to the Linux kernel for their CPU and GPU lines. How would they do this without Linux engineers?

AMD has had multiple hiring rounds for Linux kernel engineers and their efforts regarding GPU support were never interrupted, so I dunno where you got that AMD fired "all their Linux engineers".

I don't think anyone is saying Intel is actually currently dead. They're clearly not. But their trajectory is not headed the right way.

They claim API compatibility with standard scikit-learn. If that’s true, you can optionally run with sklearnx, or not, without any rewriting of code. Sounds fair to me.

Intel has done similar work before in the C/Fortran world; see BLAS, LAPACK, and FFTW vs MKL.

This is not Intel specific according to https://intel.github.io/scikit-learn-intelex/system-requirem...

Just requires an x86 processor with "at least one of SSE2, AVX, AVX2, AVX512 instruction sets."