[SciPy-Dev] Enhancement Request: SuperLU_dist/SuperLU_mt replace current scipy.sparse.superLU

[Neil Godber]

Hi Dev-List,

Regarding Replace superLU sequential with superLU dist · Issue #14096 ·
scipy/scipy (github.com) <https://github.com/scipy/scipy/issues/14096>,
would it be preferable to retain the sequential version a,d simply adding
the mt version as an  option or would should simply swap out sequential for
mt?

Cheers,
Neil

On Sun, 30 May 2021 at 09:40, Neil Godber <njgodber at gmail.com> wrote:

> Hi Ralf,
>
> Saw your comment on the issue. Is there anything else I can do to help?
> Unfortunately my C/C++ skills are lacking so I'm not sure how much help I
> on the actual integration itself.
>
> Cheers,
> Neil
>
> On Sat, 29 May 2021 at 17:00, Neil Godber <njgodber at gmail.com> wrote:
>
>> Hi Ralf + ScipyDev,
>>
>> Dr Li has helpfully uploaded the SuperLU_mt code base to github
>> https://github.com/xiaoyeli/superlu_mt . Dr Li indicated that most
>> development effort is focused on _dt, but that sequential and mt remain
>> supported and bug reports will be attended to.
>>
>> Cheers,
>> Neil
>>
>> On Mon, 24 May 2021 at 11:09, Neil Godber <njgodber at gmail.com> wrote:
>>
>>> Hi Ralf,
>>>
>>> Thanks for your email and happy to assist in whatever way I can. I've
>>> reached out to Dr Xiaoyei Li of Lawrence Berkley who appears to be the
>>> principal maintainer of the code base with a request to host the _mt code
>>> on github. Given the other two versions are already hosted I can't see that
>>> being an issue. I've also queried whether developments are done in sync and
>>> if so, if there is a pending patching for mt in the similar fashion to
>>> sequential.
>>>
>>> I will revert with further information as I receive it.
>>>
>>> Cheers,
>>> Neil
>>>
>>> On Mon, 24 May 2021 at 04:33, Ralf Gommers <ralf.gommers at gmail.com>
>>> wrote:
>>>
>>>>
>>>>
>>>> On Fri, May 21, 2021 at 2:11 AM Neil Godber <njgodber at gmail.com> wrote:
>>>>
>>>>> Hi dev-list,
>>>>>
>>>>> Further last, realised that I failed to link to the issue itself
>>>>> despite linking to virtually everything else:
>>>>>
>>>>> Replace superLU sequential with superLU dist · Issue #14096 ·
>>>>> scipy/scipy (github.com) <https://github.com/scipy/scipy/issues/14096>
>>>>>
>>>>> Cheers,
>>>>> Neil
>>>>>
>>>>> On Fri, 21 May 2021 at 10:04, Neil Godber <njgodber at gmail.com> wrote:
>>>>>
>>>>>> Hi dev-list,
>>>>>>
>>>>>
>>>> Thanks for the proposal Neil.
>>>>
>>>>
>>>>>> I've logged an enhancement request on github to bring the
>>>>>> parallel versions of SuperLU into scipy. As requested I'm circulating the
>>>>>> enhancement here to solicit comment/feedback/engagement.
>>>>>>
>>>>>> Content of request is as follows (with light editing and links):
>>>>>>
>>>>>> "Is your feature request related to a problem? Please describe.
>>>>>>
>>>>>> The direct solution of sparse matrices is a common problem that
>>>>>> arises across many domains. Current scipy.sparse.linalg provides spsolve
>>>>>> and splu to solve such classes of problems. Neither solution appears to
>>>>>> utilise more than one or two threads leaving a lot of the available compute
>>>>>> on modern machines unused. Other solutions (MUMPS, Pardiso) exploit
>>>>>> multicore processors but are either minimally supported and/or locked
>>>>>> behind proprietary libraries.
>>>>>>
>>>>>> Describe the solution you'd like
>>>>>> Fortunately SuperLU has two multithreaded implementations superLU_mt
>>>>>> and superLU_dt, both actively maintained (SuperLU: Home Page
>>>>>> (nersc.gov) <https://portal.nersc.gov/project/sparse/superlu/>).
>>>>>> Ideally SciPy would replace the current sequential implementation with one
>>>>>> of the parallel implementations to yield large performance improvements.
>>>>>>
>>>>> The relevant one seems to be superLU_mt. The `_dt` flavor is for
>>>> distributed computing, which is out of scope for SciPy. superLU_mt says it
>>>> has both pthreads and OpenMP interfaces - we'd prefer pthreads (and
>>>> actually forbid OpenMP within SciPy).
>>>>
>>>> The last release of superLU_mt is v3.1, from March 2016. Our current
>>>> copy of SuperLU is 5.2.1, from May 2016 - but then there's a bunch of
>>>> patches applied to it, and then
>>>> https://github.com/scipy/scipy/pull/12243 did a sync with SuperLU
>>>> master in May 2020. So it would be necessary to figure out if we can get
>>>> superLU_mt from upstream master as well, and that we don't lose bug fixes
>>>> we applied since 2016. Could you look into that Neil? And do you know if
>>>> the parallel and sequential versions are developed in sync or not?
>>>>
>>>> Cheers,
>>>> Ralf
>>>>
>>>>
>>>> Describe alternatives you've considered
>>>>>> pyMKL (Pardiso dwfmarchant/pyMKL: Python wrappers to Intel MKL
>>>>>> routines (github.com) <https://github.com/dwfmarchant/pyMKL>) - MKL
>>>>>> with issues associated with that for non Intel processors, unmaintained
>>>>>> relatively to scipy, PyTrilinos (implements all three superLU types but
>>>>>> only available on osx and linux PyTrilinos | Trilinos
>>>>>> <https://trilinos.github.io/pytrilinos.html>), petsc (petsc4py PETSc
>>>>>> / petsc · GitLab <https://gitlab.com/petsc/petsc>) (implements all
>>>>>> three superLU types but only available on osx and linux). All are
>>>>>> relatively inaccessible compared to ubiquity and accessibility of scipy.
>>>>>>
>>>>>> Additional context (e.g. screenshots)
>>>>>> Anecdotally large number of projects use various non scipy sparse
>>>>>> solvers to achieve high level of performance. This comes with considerable
>>>>>> time and maintenance investment. It would be fantastic for scipy to provide
>>>>>> a 'performance competitive' sparse direct solver out of the hood as it does
>>>>>> for dense matrices."
>>>>>>
>>>>>>
>>>>>> Cheers,
>>>>>>
>>>>>> Neil
>>>>>>
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