[petsc-dev] (no subject)

[Matthew Knepley]

On Fri, Sep 22, 2017 at 12:06 AM, Richard Tran Mills <rtmills at anl.gov>
wrote:

> Thanks for sharing this, Barry. I haven't had time to read their paper,
> but it looks worth a read.
>
> Hong, since many machine-learning or data-mining problems can be cast as
> linear algebra problems (several examples involving eigenproblems come to
> mind), I'm guessing that there must be several people using PETSc (with
> SLEPc, likely) in this this area, but I don't think I've come across any
> published examples. What have others seen?
>

http://epubs.siam.org/doi/abs/10.1137/S1052623400374379

   Matt


> Most of the machine learning and data-mining papers I read seem employ
> sequential algorithms or, at most, algorithms targeted at on-node
> parallelism only. With available data sets getting as large and easily
> available as they are, I'm surprised that there isn't more focus on doing
> things with distributed parallelism. One of my cited papers is on a
> distributed parallel k-means implementation I worked on some years ago: we
> didn't do anything especially clever with it, but today it is still one of
> the *only* parallel clustering publications I've seen.
>
> I'd love to 1) hear about what other machine-learning or data-mining
> applications using PETSc that others have come across and 2) hear about
> applications in this area where people aren't using PETSc but it looks like
> they should!
>
> Cheers,
> Richard
>
> On Thu, Sep 21, 2017 at 12:51 PM, Zhang, Hong <hongzhang at anl.gov> wrote:
>
>> Great news! According to their papers, MLSVM works only in serial. I am
>> not sure what is stopping them using PETSc in parallel.
>>
>> Btw, are there any other cases that use PETSc for machine learning?
>>
>> Hong (Mr.)
>>
>> > On Sep 21, 2017, at 1:02 PM, Barry Smith <bsmith at mcs.anl.gov> wrote:
>> >
>> >
>> > From: Ilya Safro isafro at g.clemson.edu
>> > Date: September 17, 2017
>> > Subject: MLSVM 1.0, Multilevel Support Vector Machines
>> >
>> > We are pleased to announce the release of MLSVM 1.0, a library of fast
>> > multilevel algorithms for training nonlinear support vector machine
>> > models on large-scale datasets. The library is developed as an
>> > extension of PETSc to support, among other applications, the analysis
>> > of datasets in scientific computing.
>> >
>> > Highlights:
>> > - The best quality/performance trade-off is achieved with algebraic
>> > multigrid coarsening
>> > - Tested on academic, industrial, and healthcare datasets
>> > - Generates multiple models for each training
>> > - Effective on imbalanced datasets
>> >
>> > Download MLSVM at https://github.com/esadr/mlsvm
>> >
>> > Corresponding paper: Sadrfaridpour, Razzaghi and Safro "Engineering
>> > multilevel support vector machines", 2017,
>> > https://arxiv.org/pdf/1707.07657.pdf
>> >
>>
>>
>


-- 
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

http://www.caam.rice.edu/~mk51/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.mcs.anl.gov/pipermail/petsc-dev/attachments/20170922/43ec8c2d/attachment-0001.html>