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-bounces@nmr.mgh.harvard.edu <freesurfer-bounces@nmr.mgh.harvard.edu> on behalf of Horn, Mitchell Jacob <MJHORN@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-bounces@nmr.mgh.harvard.edu <freesurfer-bounces@nmr.mgh.harvard.edu> on behalf of Paul Wighton <paul@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
 

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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 <MJHORN@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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