Re: Matrix multiplication performance

Riccardo Rossi <[email protected]>
Newsgroups gmane.comp.lib.boost.ublas
Message-ID <CAOVdALxv99vFqGbCpmcGmq+5Fgnr+zv-H9Vt_fPcRgijRCOXEQ@mail.gmail.com>
I would like to say that as a user, native implementation is way better
than bindings.

Blas is always a nasty dependence to have in a project...and headeronly
libs are the ideal situation.

I think for example this justifies the success of eigen over alternatives.

Cheers
Riccardo
On 24 Jan 2016 10:18, "Oswin Krause" <[email protected]>
wrote:

> Hi,
>
> I would still vote for the route to rewrite uBLAS based on BLAS bindings
> and providing a reasonable default implementation that also works well
> without memory assumptions.The main reason is that only having a fast gemm
> implementation does not really improve things, given that BLAS level 3 is a
> quite large beast.
>
> Im still willing to donate my partial uBLAS rewrite, unfortunately I am a
> bit short on time to polish it(just finished my phd and have a huge load of
> work on my desk). But if someone opened a git-branch for that i could try
> to make the code ready (porting my implementation back to boost namespaces
> etc).
>
>
> On 2016-01-23 18:53, palik imre wrote:
>
>> Hi All,
>>
>> what's next?  I mean what is the development process for ublas?
>>
>> Now we have a C-like implementation that outperforms both the
>> mainline, and the branch version (axpy_prod).  What will we do with
>> that?
>>
>> As far as I see we have the following options:
>>
>> 1) Create a C++ template magic implementation out of it.  But for
>> this, at the least we would need compile-time access to the target
>> instruction set.  Any idea how to do that?
>>
>> 2) Create a compiled library implementation out of it, and choose the
>> implementation run-time based on the CPU capabilities.
>>
>> 3) Include some good defaults/defines, and hope the user will use
>> them.
>>
>> 4) Don't include it, and do something completely different.
>>
>> What do you think?
>>
>> Cheers,
>>
>> Imre
>>
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