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Re: guile-user Digest, Vol 193, Issue 29

From: Zelphir Kaltstahl
Subject: Re: guile-user Digest, Vol 193, Issue 29
Date: Wed, 26 Dec 2018 12:43:48 +0100
User-agent: Mozilla/5.0 (X11; Linux x86_64; rv:60.0) Gecko/20100101 Thunderbird/60.2.1

On 25.12.18 18:00, 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: 
> (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.

Hi Tk,

That looks interesting. I've only used OpenMP once in a university
lecture, in a pretty simple way (just adding some annotations to some
loops to parallelize them) on a normal desktop machine. I have a few
questions about your library:

What are the requirements in terms of installed software? (For example:
Do I need something Fortran installed? Or is GCC handling it all?)

What do you mean by "on HPC platforms"? Does that imply, that it would
not produce efficient execution on desktop machines?

Thanks for sharing your project, I will have to take a look at how to
use it.



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