Is this something new? The code seems to be included as sub-modules of OMR itself, and all the repos[1][2][3] show recent "Initial Commit" messages, so I'm operating on the assumption that it is. If so, I wonder if this is something that might prove useful in other contexts...
[1]: https://github.com/dreamworksanimation/arras4_core
Arras was designed to let multiple machines work on a single frame in parallel. Film renderers still very much leverage the CPU for a lot of reasons, and letting a render run to completion on a single workstation could take hours. Normally this isn’t a problem for batch rendering, which typically happens overnight, for shots that will get reviewed the next day.
But sometimes it’s really nice to have a very immediate, interactive workflow at your desk. Typically you need to use a different renderer designed with a more real-time architecture in mind, and many times that means using shaders that don’t match, so it’s not an ideal workflow.
Arras was designed to be able to give you the best of both worlds. Moonray is perfectly happy to render frames in batch mode, but it can also use Arras to connect dozens of workstations together and have them all work on the same frame in parallel. This basically gives you a film-quality interactive lighting session at your desk, where the final render will match what you see pixel for pixel because ultimately you’re using the same renderer and the same shaders.
Could you go into a bit more detail on how the work is distributed? Is it per tile (or some other screen-space division like macro-tiles or scan-lines)? Per sample pass? (Surely it's not scene distribution like the old Kilauea renderer from Square!) Dynamic or static scheduling? Sorry, so many questions. :-)
I built a scene distribution renderer similar to Kilauea for my masters thesis in school, except with a feed forward shader design which exploited the linear color space to never send the results of computations back up the call stack… kind of neat but yeah, all sorts of reasons why that kind of design would not work well under production workloads. And RAM has gotten so stinking cheap!