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<p>Hello,</p>
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<p>When computing a matrix product of the type R = D.DT using MatMatTransposeMult() I find I get rather different results depending on the number of processes. In one example using a data set that is small compared to the application I get Frobenius norms |R|
= 1.047e3 on a single process, 1.0363e3 on a single HPC node (40 cores), and 9.7307e2 on two nodes.</p>
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<p>I have ascertained that the single process result is indeed the correct one (i.e., eigenvectors of R form a proper basis for the columns of D), so naturally I'd love to be able to reproduce this result across different parallel setups. How might I achieve
this?</p>
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<p>I'm attaching MWE code and the data set used for the example.</p>
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<p>Thanks in advance!</p>
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<p>Best Regards</p>
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<p>Peder Jørgensgaard Olesen</p>
<p><span style="font-size:10pt; color:rgb(117,123,128)">PhD Student, Turbulence Research Lab</span></p>
<p><span style="font-size:10pt; color:rgb(117,123,128)">Dept. of Mechanical Engineering</span></p>
<p><span style="font-size:10pt; color:rgb(117,123,128)">Technical University of Denmark</span></p>
<p><span style="font-size:10pt; color:rgb(117,123,128)">Niels Koppels Allé</span></p>
<p><span style="font-size:10pt; color:rgb(117,123,128)">Bygning 403, Rum 105</span></p>
<p><span style="font-size:10pt; color:rgb(117,123,128)">DK-2800 Kgs. Lyngby</span></p>
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