Mar 28 2026 APP 2.0.0-beta40 will be released in 7 days.
It did take a long time to have the work finished on this and it will have a major performance boost of 30-50% over 2.0.0-beta39 from calibration to integration. We extensively optimized many critical parts of APP. All has been tested to guarantee correct optimizations. Drizzle and image resampling is much faster for instance, those modules have been completely rewritten. Much less memory usage. LNC 2.0 will be released which works much better and faster than LNC in it's current state. And more, all will be added to the release notes in the coming weeks...
Update on the 2.0.0 release & the full manual
We are getting close to the 2.0.0 stable release and the full manual. The manual will soon become available on the website and also in PDF format. Both versions will be identical and once released, will start to follow the APP release cycle and thus will stay up-to-date to the latest APP version.
Once 2.0.0 is released, the price for APP will increase. Owner's license holders will not need to pay an upgrade fee to use 2.0.0, neither do Renter's license holders.
Hi. I have an M3 Max MacBook Pro with 64Gb of RAM. When I'm processing images, for most of the time the CPU and RAM are barely being used. Is there a way that I can maximise usage of the hardware so that I can get the processing done faster? Thanks in advance.
Hi John @sventek,
Most tasks in APP will use all CPU threads available. RAM usage will be limited, it will only use the amount of of RAM that is needed to perform the task.
Some parts, like the Local Normalization Correction in 6) integrate, can only use 1 CPU thread at the moment, which we are aware of. It is very difficult to implement a multi-threaded version of LNC, but it is on our Todo list.
If I run an integration on my mac mini M1 of 100s of lights with calibration frames, it is very fast and CPU usage is high as expected.
Which APP version are you using?
Mabula
Hi Mabula. I'm using beta 29. Yes, it certainly seems to use a high number of threads on some parts during the process, but not all of them. With most of the tasks, it uses very few of the available resources and takes a while to hear the gong to indicate that processing is over.
