<div dir="ltr"><div dir="ltr">On Mon, Jan 30, 2023 at 2:24 PM Guglielmo, Tyler Hardy via petsc-users <<a href="mailto:petsc-users@mcs.anl.gov">petsc-users@mcs.anl.gov</a>> wrote:<br></div><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div class="msg1559822710429064425">
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<p class="MsoNormal"><span style="font-size:11pt">Thanks Barry,<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt">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?</span></p></div></div></div></blockquote><div><br></div><div>We don't have parallel sparse-sparse. It would not be too hard to write, but it would be some work.</div><div><br></div><div>It is hard to understand the use case. Is one matrix much smaller? If not, and you inherit the distribution from A, it seems</div><div>like it might be very suboptimal, and otherwise you would have to redistribute on the fly and it would get very complicated.</div><div><br></div><div> Thanks,</div><div><br></div><div> Matt</div><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div class="msg1559822710429064425"><div lang="EN-US" style="overflow-wrap: break-word;"><div class="m_1068516836359424776WordSection1">
<p class="MsoNormal"><span style="font-size:11pt">Best,<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt">Tyler<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
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<p class="MsoNormal" style="margin-bottom:12pt"><b><span style="font-size:12pt;color:black">From:
</span></b><span style="font-size:12pt;color:black">Barry Smith <<a href="mailto:bsmith@petsc.dev" target="_blank">bsmith@petsc.dev</a>><br>
<b>Date: </b>Monday, January 30, 2023 at 11:12 AM<br>
<b>To: </b>Guglielmo, Tyler Hardy <<a href="mailto:guglielmo2@llnl.gov" target="_blank">guglielmo2@llnl.gov</a>><br>
<b>Cc: </b><a href="mailto:petsc-users@mcs.anl.gov" target="_blank">petsc-users@mcs.anl.gov</a> <<a href="mailto:petsc-users@mcs.anl.gov" target="_blank">petsc-users@mcs.anl.gov</a>><br>
<b>Subject: </b>Re: [petsc-users] Kronecker Product<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt"> 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 <a href="https://urldefense.us/v3/__https:/petsc.org/release/docs/manualpages/Mat/MatCreateKAIJ/*matcreatekaij__;Iw!!G2kpM7uM-TzIFchu!lSQ9WFlYi6PMdfs3WAfEq4ydgCLZtfDgyFy9PjdLNTisCsHtwmVuukcpIv1J0i1EtiQ$" target="_blank">https://petsc.org/release/docs/manualpages/Mat/MatCreateKAIJ/#matcreatekaij</a><u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt"> Barry<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
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<p class="MsoNormal"><b><span style="font-size:10.5pt;font-family:Menlo;color:rgb(180,36,25)"><br>
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<p class="MsoNormal"><span style="font-size:11pt">On Jan 30, 2023, at 12:53 PM, Guglielmo, Tyler Hardy via petsc-users <<a href="mailto:petsc-users@mcs.anl.gov" target="_blank">petsc-users@mcs.anl.gov</a>> wrote:<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt">Hi all,<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt"> <u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt">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? <u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt"> <u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt">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 :)!<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt"> <u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt">Best,<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt">Tyler<span class="m_1068516836359424776apple-converted-space"> </span><u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt"> <u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt">+++++++++++++++++++++++++++++<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt">Tyler Guglielmo<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt">Postdoctoral Researcher<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt">Lawrence Livermore National Lab<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt">Office: 925-423-6186<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt">Cell: 210-480-8000<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt">+++++++++++++++++++++++++++++<u></u><u></u></span></p>
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<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
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</div></blockquote></div><br clear="all"><div><br></div>-- <br><div dir="ltr" class="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div>What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead.<br>-- Norbert Wiener</div><div><br></div><div><a href="http://www.cse.buffalo.edu/~knepley/" target="_blank">https://www.cse.buffalo.edu/~knepley/</a><br></div></div></div></div></div></div></div></div>