[digiKam-users] GPU usage (with OpenCL), moving my pictures disk (=albums) structure, and facial recognition

[Tim Carr]

Hi - I'm relatively new to DigiKam, but really enjoying using it (I used to
just work in Mac Photos).  I'm working on migrating my 20,000+ photos into
the program, running an Intel Mac Ventura 13.1 on an SSD disk, and using
DigiKam v. 7.9.  A got a few newbie questions (and I've RTFM'ed most of the
documentation! <grin>).  Please let me know if it's preferable that I break
these up into seperate email questions or there's a better forum to ask
this stuff:

- I've noticed when doing whole-collection activities like facial
recognition/detection, I can use all my processor cores (which it does),
but very little usage of the GPU.  Is that by design, rather than tapping
into the power the GPU can provide?  Or DigiKam just doesn't really use
much of that by default regardless of whats available?

- I've seen the notes about disabling OpenCL on Mac's, but in futzing
around with things, leaving that enabled doesn't seem to break anything.
Is leaving that enabled something that might help performance?

- I exported all my pictures out of Mac Photos using OSXPhotos, and created
a directory structure that has Year/Month/Day.  I've realized that creates
a TON of separate albums.  I'd prefer just using a few albums but
incorporating tagging to help segregate things.  Can I just move those
physical files through the OS, and then DigiKam will automatically
recognize the different layout but retain all the database information?

- I've seen some of the documentation saying NoSQL is good for collections
up to 100K or so.  Is there any noticeable performance increase if I
migrate to either a local MySQL engine on the Mac, or offloading that to a
whole separate server (on my LAN) for that DB activity?

- What are the conditions you want to use the Tools/Maintenance option to
clear and re-build all facial recognition data?  I've gone through the
process of approving/ignoring most of my collection.  Is that something
that would improve future recognition of what's already been built, or is
that really only something if you notice you're not getting very good
results from scans?

Thanks so much!
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