LAPACK has segfault with large matrix SVD.

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LAPACK has segfault with large matrix SVD.

Postby cyrusshaoul » Wed Jan 16, 2013 5:04 am

Dear LAPACK team,

I am sorry that I can't send in a report that uses the LAPACK routines directly, but I am not sure how to do this.
I can report two equivalent reproducible examples, one in numpy and one in R. Both are using LAPACK, to do the SVD within the pseudoinverse calculation:

See the attached code for the reproducible examples.

I am running all of this on a machine with 128Gb or RAM. These matrices require around 40Gb to process. I have tried this with OPENMP num threads of 1 or higher, and
the result is the same.

Do you have any large-memory machines you can use to try and reproduce this? Is there any reason why it does not like large matrices?

Thanks so much,

Cyrus
Attachments
example.crash.for.svd.txt
The code to reproduce the crash in LAPACK.
(424 Bytes) Downloaded 35 times
cyrusshaoul
 
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Re: LAPACK has segfault with large matrix SVD.

Postby cyrusshaoul » Wed Jan 16, 2013 12:25 pm

Also, Here is the screenshot from the crash report that I got when numpy/python finished dumping core (many hours after it crashed.)
I am not sure what DLASD2_ is. Does it help trace the problem?

-Cyrus
Attachments
crash.report.dlasd2.png
crash.report.dlasd2.png (138.54 KiB) Viewed 542 times
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