[petsc-dev] Adding support for new sparse BLAS inspector-executor routines in MKL

[Matthew Knepley]

On Tue, Sep 29, 2015 at 7:55 PM, Barry Smith <bsmith at mcs.anl.gov> wrote:

>
>   Richard,
>
>    Sounds reasonable to me.


Richard, this is somewhat subtle since PETSc's inheritance model is sucky
and somewhat broken. Look at
the factorization classes, AIJMUMPS etc., before coding anything.

  Thanks,

     Matt


>
>   Barry
>
> > On Sep 29, 2015, at 5:16 PM, Richard Mills <richardtmills at gmail.com>
> wrote:
> >
> > Hi Folks,
> >
> > It's come up in discussions a few times that, ideally, the burden of
> supporting very specific optimizations for complex architectures like
> Intel's upcoming Knights Landing MIC processor should be handled as much as
> possible by a library like MKL.  To take some concrete steps towards this,
> I want to add support for using the new sparse BLAS inspector-executor
> ("SpMV 2" -- no idea why they called it that) routines that were added in
> the recent release (11.3) of MKL.  These use an inspector-executor model to
> provide some optimized operations (sparse matrix-vector multiplication,
> sparse matrix-matrix multiplication, triangular solve, sparse matrix
> addition).  Those interested can have a look at the documentation here:
> >
> >   https://software.intel.com/en-us/node/590105
> >
> > I am thinking that the cleanest way to support this is to add a new
> matrix subclass that mostly inherits from MATAIJ (or MATBAIJ), calling the
> inspection ("analysis") routines upon matrix assembly finalization and
> using the "SpMV 2" routines as appropriate.  Then if someone wants to use
> the SpMV 2 stuff, they ensure that their matrix ends up as MATMKLSPMV2 (or
> whatever better name we come up with).  Does this sound reasonable?  If you
> have a better suggestion, please let me hear it.
> >
> > --Richard
>
>


-- 
What most experimenters take for granted before they begin their
experiments is infinitely more interesting than any results to which their
experiments lead.
-- Norbert Wiener
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