Comment by romwell | Hacker News Reader

Comment by romwell | Hacker News Reader

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Well, ignoring the presence of "(,)" in Fortran is exactly what one should do.

Recall that FORTRAN stands for FORmula TRANslator.

FORTRAN is built for math. It makes math expressions — particularly, linear algebra stuff — easy to write, and running very, very fast. There are much better tools for text processing (there's perl, python, pandas, and everything in between). But for raw math, there's Fortran.

That's why everything is still powered by Fortran.

Yes, from Matlab to your fancy neural network, numpy, scipi, scikit-learn, etc., the actual computational core - BLAS/LAPACK is Fortran-based.

That's why Matlab feels like Fortran. And that's why numpy semantics are different from python's: because it is heavily inspired by Matlab, and, like Matlab, wraps FORTRAN code in a friendly manner.

But in the end, it's FORTRAN all the way down. Even in Julia.

(And that's how I ended up fixing a minor bug in Google's FORTRAN sparse svd package, PROPACK[1], in 2019. Well, PROPACK was written before Google existed, but they have hired the author for a reason. The bug was only in the way an error message displeased the internal memory leak checker; in other words, FORTRAN text I/O was an issue. Stay away from that, and it's the best tool for the job.)

[1] http://sun.stanford.edu/~rmunk/PROPACK/

> But in the end, it's FORTRAN all the way down. Even in Julia.

That's not true. None of the Julia differential equation solver stack is calling into Fortran anymore. We have our own BLAS tools that outperform OpenBLAS and MKL in the instances we use it for (mostly LU-factorization) and those are all written in pure Julia. See https://github.com/YingboMa/RecursiveFactorization.jl, https://github.com/JuliaSIMD/TriangularSolve.jl, and https://github.com/JuliaLinearAlgebra/Octavian.jl. And this is one part of the DiffEq performance story. The performance of this of course is all validated on https://github.com/SciML/SciMLBenchmarks.jl

Is the recursive comparison against OpenBLAS using ReLAPACK or the normal(?) one? I don't remember the story on building with that or know the different algorithms.

Does Julia not use OpenBLAS at all now?

Julia defaults to OpenBLAS but libblastrampoline makes it so that `using MKL` flips it to MKL on the fly. See the JuliaCon video for more details on that (https://www.youtube.com/watch?v=t6hptekOR7s). The recursive comparison is against OpenBLAS/LAPACK and MKL, see this PR for some (older) details: https://github.com/YingboMa/RecursiveFactorization.jl/pull/2... . What it really comes down to in the end is that OpenBLAS is rather bad, and MKL is optimized for Intel CPUs but not for AMD CPUs, so when the best CPUs are now all AMD CPUs, having a new set of BLAS tools and mixing that with recursive LAPACK tools is either as good or better on most modern systems. Then we see this in practice even when we build BLAS into Sundials for 1,000 ODE chemical reaction networks (https://benchmarks.sciml.ai/html/Bio/BCR.html).

The question was whether the comparison was against OpenBLAS built with recursive LAPACK routines (not the default, I've now checked). I don't know whether that would use the same algorithm.

I don't see great attraction in "flipping" to MKL. (In general I switch BLASes by dynamic linking if necessary.) I'm puzzled by the value of "rather bad" for OpenBLAS, at least for BLAS [1], but it seemed OK against MKL for LAPACK-based R benchmarks I measured on sandybridge. That said, I don't understand why people avoid AMD's BLAS/LAPACK (BLIS/libflame with enhancements intended for merging) on AMD systems. In this context: BLIS studiously avoids Fortran for some reason, even for test the Fortran interface.

I have nothing against Julia, incidentally; I have the original Dylan book.

1. https://git.sr.ht/~fx/blis/tree/performance/docs/Performance...

It doesn't look like you're measuring factorization performance? OpenBLAS matrix-matrix multiplication is fine, it just falls apart when going to things like Cholesky and LU. Also it falls apart on smaller problems, like <100x100 cases.

> not the default, I've now checked

Whatever the Julia default build is doing, so probably not the recursive LAPACK routines then if that's how it's being built. If there's a better default that's worth an issue.

> That said, I don't understand why people avoid AMD's BLAS/LAPACK

There just isn't a BLIS wrapper into Julia right now, and it's much easier to just write new BLAS tools than to build wrappers IMO. It makes it very easy to customize to nonstandard Julia number types too. But I do think that BLIS is a great project and I would like to see it replace OpenBLAS as the default. There's been some discussion to make it as easy as MKL (https://github.com/JuliaLinearAlgebra/BLIS.jl/issues/3).

OK, no, I measured level 3 BLAS, and it sounded like a blanket dismissal of OpenBLAS. I'm surprised at it falling apart at all, but I haven't considered its LAPACK much and I value understanding from measurement. The set of R LA benchmarks I ran on sandybridge in response to theories about the Microsoft R distribution did include Cholesky. (I think the operations are a thin wrapper over LAPACK ones.) In that case OB was actually better than MKL for Cholesky with the versions I had then, so I'm puzzled. Probably I should revisit that sometime.

OB seems to me a better general default than BLIS, though its threading implementation may not be robust. BLIS is even broken on POWER9, and I don't remember whether the dynamic micro-architecture selection ever added for other than x86.

For really small matrices on x86, I'd front BLAS with libxsmm, but I don't remember where it starts winning, or know the threshold for BLIS' non-packed kernels.

Anyway, thanks for the info. I should be embarrassed never to have made time to look seriously at Julia.

Yeah no worries. You might want to join in the community at least because this kind of discussion is par for the course on the Slack. You might find a nice home here!

> though its threading implementation may not be robust

That's one of the main issues I have with it. Its poor threading behavior coupled with a bad default (https://github.com/JuliaLang/julia/issues/33409 one of my biggest pet peeve issues right now) really degrades real user performance.

> Even in Julia.

Unless you're using [Octavian.jl](https://github.com/JuliaLinearAlgebra/Octavian.jl) or such for your linear algebra in place of BLAS. But yes, it is always interesting how many people do not know how much of modern software is built on Fortran!

This is a reasonable argument, but it implies that FORTRAN is in fact not a good language for beginners; almost all beginners care about IO / text processing, and almost none care about linear algebra. (Especially if one is talking about beginners-in-general, as opposed to engineering types who are learning to use programming as a tool.)

Have you ever thought it was because the tutorials you read were geared towards a type of programmer that had different goals from a computational programmer?

Sure, but that still means FORTRAN is an easy language if your intro to programming isn't centered around text processing.

And for many people, it isn't/hasn't been/doesn't have to be.

That's the author's take as well:

>The idea of introductory languages is not necessarily that they are suited to commercial endeavours, but rather that they are (i) easy to implement algorithm in, (ii) readable, and (iii) do not contain things that will confuse and bewilder.

What high performance BLAS do you have written in Fortran? I don't know of one (though I think I could write one with decent performance on simple targets). It seems the rule that this will come up whenever Fortran is pushed, though I don't want to disparage Fortran after so long.

Yeah it's funny because they are all C these days.

Well, C plus assembler, or maybe non-standard machine-specific intrinsics, for the ones I know. They at least seem to need control over prefetching.

As a data processing language, IDL/GDL is even closer to Fortran. It's essentially a vectorized, interpreted, simplified Fortran. In many ways I still prefer it to Python these days.

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