Re: Matrix or array operations library

[Tk]

‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
On Monday, 24 December 2018 23:01, Zelphir Kaltstahl <address@hidden> wrote:

> Hello Guile Users,
>
> Is there some library, that enables high performance matrix operations
> or even n-dimensional array operations? I am thinking of something like
> NumPy in the Python ecosystem. I think NumPy in turn also uses some
> lower level thing to do what it does. I think OpenBLAS or MKL, depending
> on the architecture. I wonder if there is any wrapper around OpenBLAS
> for Guile or something similar.
>
> I am writing a little code for matrix operations and currently I am
> using Guile arrays, as they are made of vectors and have constant access
> time, which is already great. My guess is, that this would be the right
> choice if using pure Guile. I am writing data abstraction procedures, so
> that later on I could exchange what is used to represent the data.
>
> Maybe, if there is something like NumPy or lower level, I should use
> that instead? (Would I have to learn how to use FFI first?)
>
> Or maybe Guile's implementation is already so fast, that it would not
> make that much difference to use a lower level thing?
>
> Currently I have only a little experimental program, started today, so
> no huge plan. OK, one can fantasize about stuff like Pandas data frames
> in Guile, but I have no illusion, that it is a work of a few days or
> even weeks. It would be nice to learn, how to use a low level thing or
> maybe even Pandas, if there are any such bindings for Guile. I could
> make the implementation use different representations, depending on a
> parameter or something like that.
>
> Regards,
>
> Zelphir


I took a different route. Instead of merely binding functionality in a lower 
level language, I use Guile to generate lean-and-mean modern Fortran code. The 
building blocks can be found here: https://gitlab.com/codetk/schemetran (wanted 
to save this for potluck, but here it goes ... ). Fortran compilers take care 
of efficient execution on HPC platforms. Actually, it is beyond me why anyone 
would bother with any other programming language when it comes to expressing 
maths efficiently.

I built a pseudo-spectral Navier-stokes solver that can work on MPI, shared-mem 
(OpenMP), and hopefully soon GPU/Xeon accelerators (OpenMP 4) atop of 
schemetran. I still need to see about publishing it under an Libre licence.