There's a small percentage of subjects that need more than the 8Gb of memory
(for mris_fix_topology if my memory is correct).
You could increase the memory allocation for all subjects, or you could run
your entire dataset with 8Gb per subject, then re-run the ones that failed with
increased memory.
-Paul
________________________________
From: freesurfer-boun...@nmr.mgh.harvard.edu
<freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of Horn, Mitchell Jacob
<mjh...@mgh.harvard.edu>
Sent: Tuesday, May 3, 2022 2:26 PM
To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Subject: Re: [Freesurfer] Scalable recon workflow
Hi Paul,
Thanks for your speedy response! In that case, I assume 1GB/core is the memory
allocation? Is it helpful to increase that?
Best,
Mitch
________________________________
From: freesurfer-boun...@nmr.mgh.harvard.edu
<freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of Paul Wighton
<p...@corticometrics.com>
Sent: Tuesday, May 3, 2022 1:34 PM
To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Subject: Re: [Freesurfer] Scalable recon workflow
External Email - Use Caution
Hi Mitch,
In our experience, -openmp 8 gives faster recons than -openmp 4, but the gains
after -openmp 8 diminish quickly and aren't worth it.
-Paul
On Tue, May 3, 2022 at 12:51 PM Horn, Mitchell Jacob
<mjh...@mgh.harvard.edu<mailto:mjh...@mgh.harvard.edu>> wrote:
Hi Experts,
If I have 100s of subjects to recon and have access to an HPC with both GPU and
CPU options what is the recommended flags to maximize speed performance? Is …
-parallel -openmp 4 … still the best approach? And if so, is 2GB memory/core
best (keeping it 8GB memory/subject) or is there some better arrangement?
Best,
Mitch
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