What is large? If A and B have dimensions of 1000, then the Kronecker product is of size 1,000,000. Do you want the Kronecker product to be explicitly formed or just available as matrix vector products? If just explicitly available then I think you can just store sparse A (for example) completely on all ranks, 10,000 by 10,000 sparse matrix is small for sequential) while B is distributed.
Barry > On Jan 30, 2023, at 2:48 PM, Guglielmo, Tyler Hardy <gugliel...@llnl.gov> > wrote: > > Both matrices (A and B) would be approximately the same size and large. The > use case (for me at least) is to create several large sparse matrices which > will be combined in various ways through Kronecker products. The combination > happens at every time step in an evolution, so it really needs to be fast as > well. I’m thinking mpi/petsc is probably not the most optimal way for > dealing with this, and might just have to work with single node > multi-threading. > > Best, > Tyler > > From: Matthew Knepley <knep...@gmail.com <mailto:knep...@gmail.com>> > Date: Monday, January 30, 2023 at 11:31 AM > To: Guglielmo, Tyler Hardy <gugliel...@llnl.gov <mailto:gugliel...@llnl.gov>> > Cc: Barry Smith <bsm...@petsc.dev <mailto:bsm...@petsc.dev>>, > petsc-users@mcs.anl.gov <mailto:petsc-users@mcs.anl.gov> > <petsc-users@mcs.anl.gov <mailto:petsc-users@mcs.anl.gov>> > Subject: Re: [petsc-users] Kronecker Product > > On Mon, Jan 30, 2023 at 2:24 PM Guglielmo, Tyler Hardy via petsc-users > <petsc-users@mcs.anl.gov <mailto:petsc-users@mcs.anl.gov>> wrote: > Thanks Barry, > > I saw that function, but wasn’t sure how to apply it since the documentation > says that S and T are dense matrices, but in my case all matrices involved > are sparse. Is there a way to work around the dense requirement? > > We don't have parallel sparse-sparse. It would not be too hard to write, but > it would be some work. > > It is hard to understand the use case. Is one matrix much smaller? If not, > and you inherit the distribution from A, it seems > like it might be very suboptimal, and otherwise you would have to > redistribute on the fly and it would get very complicated. > > Thanks, > > Matt > > Best, > Tyler > > From: Barry Smith <bsm...@petsc.dev <mailto:bsm...@petsc.dev>> > Date: Monday, January 30, 2023 at 11:12 AM > To: Guglielmo, Tyler Hardy <gugliel...@llnl.gov <mailto:gugliel...@llnl.gov>> > Cc: petsc-users@mcs.anl.gov <mailto:petsc-users@mcs.anl.gov> > <petsc-users@mcs.anl.gov <mailto:petsc-users@mcs.anl.gov>> > Subject: Re: [petsc-users] Kronecker Product > > > Do you need the explicit sparse representation of the Kronecker product? > Or do you want to apply it as an operator or solve systems with it? If the > latter you can use > https://petsc.org/release/docs/manualpages/Mat/MatCreateKAIJ/#matcreatekaij > <https://urldefense.us/v3/__https:/petsc.org/release/docs/manualpages/Mat/MatCreateKAIJ/*matcreatekaij__;Iw!!G2kpM7uM-TzIFchu!lSQ9WFlYi6PMdfs3WAfEq4ydgCLZtfDgyFy9PjdLNTisCsHtwmVuukcpIv1J0i1EtiQ$> > > Barry > > > > > > On Jan 30, 2023, at 12:53 PM, Guglielmo, Tyler Hardy via petsc-users > <petsc-users@mcs.anl.gov <mailto:petsc-users@mcs.anl.gov>> wrote: > > Hi all, > > I am wondering if there is any functionality for taking Kronecker products of > large sparse matrices that are parallel? MatSeqAIJKron is as close as I have > found, but it seems like this does not work for parallel matrices. Any ideas > here? > > An option could be to make A and B sequential, compute the Kronecker product, > C, then scatter C into a parallel matrix? This seems like a horribly > inefficient procedure. I’m still fairly new to petsc, so thanks for patience > :)! > > Best, > Tyler > > +++++++++++++++++++++++++++++ > Tyler Guglielmo > Postdoctoral Researcher > Lawrence Livermore National Lab > Office: 925-423-6186 > Cell: 210-480-8000 > +++++++++++++++++++++++++++++ > > > > -- > What most experimenters take for granted before they begin their experiments > is infinitely more interesting than any results to which their experiments > lead. > -- Norbert Wiener > > https://www.cse.buffalo.edu/~knepley/ > <https://urldefense.us/v3/__http:/www.cse.buffalo.edu/*knepley/__;fg!!G2kpM7uM-TzIFchu!nK03nUENfNtHoOs8RWmJWJQYJH2IlC_lYQPNn7kV9FsBv2CQKR_VSqbLGRLFShVpKmY$>
