As far as I can tell, this is single threaded CPU only, right? I wonder if it wouldn't be more appealing to do it parallel on the GPU instead when starting a new physics engine today, especially given how hard it is to change that later on.
The problems are that: 1) There may not be enough islands to make full use of the available threads. 2) For many frames there might be a single island which is particularly expensive to solve, so the benefit from parallelisation is reduced.
The broad-phase itself might benefit significantly from parallelisation though.
Of course, pathological cases can generate a ton of batches. Usually such implementations will use a final 'catchall' batch of some kind after N normal batches. That final batch can be handled sequentially or with a fully parallel jacobi-ish solver (which typically has worse convergence than PGS/SI, but it's usually not a problem in context).
It's extremely easy to get started with the basics, and then there's a long ramp of increasingly difficult features you can implement. So if you're doing it for fun/learning, it's a great project.
One of my first programming projects when I started learning Java in high school was a 2D physics system. Just took the equations from my physics class and implemented them in code and bam, you have particle physics. And then I figured out how to make those particles into circles with collision and added infinite lines for them to bounce off of, without really knowing what I was doing.
Then in an early CS class in college my team for a project did a similar engine but with arbitrary equilateral polygons for colliders. We had someone who already knew the math for doing those intersection checks, so it wasn't too hard.
But yeah: things obviously get hard at a certain point, but it's not one of those projects where you don't see any satisfying results until you've mastered it.
There's no doubt that GPUs have the flops/bandwidth advantage, and even all the overheads of adapting a physics engine to a GPU won't completely eliminate those advantages. If a company needed an absolute-maximum-performance backend simulation on computers they built to spec, it could definitely make sense.
But when you move into the realm of game client simulation, it's harder to justify the use of GPUs. Very, very few gamers have multiple GPUs, and the one GPU they do have is often already quite busy.
Ignoring all the nightmarish issues around driver bugs and vendor compatibility, it comes down a question of comparative advantage. GPUs are extremely good at graphics. They can also do a pretty darn good job at physics. Or sound path tracing. Or machine learning inference. Or any number of other high throughput workloads. There's a limited budget, and there's no reason to leave a beefy CPU idle. What task allocation gives you the most bang for the buck? Would your average gamer be okay with worse graphics, just because the GPU is needed to perform physics simulation in order to play the game? The answer will sometimes be yes, but it's something to be careful about.
Plus, especially with the latest surge in CPU performance and competition courtesy of AMD, CPUs can do a pretty darn good job of running physics simulations per dollar.