[petsc-dev] Why no SpGEMM support in AIJCUSPARSE and AIJVIENNACL?

Karl Rupp rupp at iue.tuwien.ac.at
Fri Oct 4 04:54:25 CDT 2019

Hi Richard,

>>> Do you have any experience with nsparse?
>>> https://github.com/EBD-CREST/nsparse
>>> I've seen claims that it is much faster than cuSPARSE for sparse
>>> matrix-matrix products.
>> I haven't tried nsparse, no.
>> But since the performance comes from a hardware feature (cache), I 
>> would be surprised if there is a big performance leap over ViennaCL. 
>> (There's certainly some potential for some tweaking of ViennaCL's 
>> kernels; but note that even ViennaCL is much faster than cuSPARSE's 
>> spGEMM on average).
>> With the libaxb-wrapper we can just add nsparse as an operations 
>> backend and then easily try it out and compare against the other 
>> packages. In the end it doesn't matter which package provides the best 
>> performance; we just want to leverage it :-)
> I'd be happy to add support for this (though I suppose I should play 
> with it first to verify that it is, in fact, worthwhile). Karl, is your 
> branch with libaxb ready for people to start using it, or should we wait 
> for you to do more with it? (Or, would you like any help with it?)

I still need to add the matrix class to the merge request. Should only 
take me a couple of hours, but I've got an extremely important deadline 
on October 15 that will prevent me from doing anything before then.

> I'd like to try to add support for a few things like cuSPARSE SpGEMM 
> before I go to the Summit hackathon, but I don't want to write a bunch 
> of code that will be thrown away once your libaxb approach is in place.

I should be able to provide a good playground on time for the Summit 
hackathon. In the meantime you can try the matrix market reader of 
nsparse directly and see what you get, especially compared to cuSPARSE 
and MKL.

Best regards,

>>> Karl Rupp via petsc-dev <petsc-dev at mcs.anl.gov> writes:
>>>> Hi Richard,
>>>> CPU spGEMM is about twice as fast even on the GPU-friendly case of a
>>>> single rank: 
>>>> http://viennacl.sourceforge.net/viennacl-benchmarks-spmm.html
>>>> I agree that it would be good to have a GPU-MatMatMult for the sake of
>>>> experiments. Under these performance constraints it's not top priority,
>>>> though.
>>>> Best regards,
>>>> Karli
>>>> On 10/3/19 12:00 AM, Mills, Richard Tran via petsc-dev wrote:
>>>>> Fellow PETSc developers,
>>>>> I am wondering why the AIJCUSPARSE and AIJVIENNACL matrix types do not
>>>>> support the sparse matrix-matrix multiplication (SpGEMM, or 
>>>>> MatMatMult()
>>>>> in PETSc parlance) routines provided by cuSPARSE and ViennaCL,
>>>>> respectively. Is there a good reason that I shouldn't add those? My
>>>>> guess is that support was not added because SpGEMM is hard to do 
>>>>> well on
>>>>> a GPU compared to many CPUs (it is hard to compete with, say, Intel 
>>>>> Xeon
>>>>> CPUs with their huge caches) and it has been the case that one would
>>>>> generally be better off doing these operations on the CPU. Since the
>>>>> trend at the big supercomputing centers seems to be to put more and 
>>>>> more
>>>>> of the computational power into GPUs, I'm thinking that I should 
>>>>> add the
>>>>> option to use the GPU library routines for SpGEMM, though. Is there 
>>>>> some
>>>>> good reason to *not* do this that I am not aware of? (Maybe the 
>>>>> CPUs are
>>>>> better for this even on a machine like Summit, but I think we're at 
>>>>> the
>>>>> point that we should at least be able to experimentally verify this.)
>>>>> --Richard

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