FPS dips usually line up with big ecological events (population blooms, mass reproduction, lots of giant organisms, pathogen waves). The sim and rendering are decoupled, so you get (hopefully) brief spikes when a lot happens at once, then it settles again as populations crash/thin out. It’s normal behavior, not a runaway bug.
I’ve spent a lot of time optimizing hot paths and getting calculations down, but when the ecology goes a bit wild, you’ll still see temporary spikes. If you want to focus on stats only, you can switch to Simmer mode (no graphs) or turn off the arena, which reduces rendering work quite a bit.
Yes, some parts are inherently O(n²) (mate finding, crowd density, predator/prey proximity, pathogen spread). Ecology needs pairwise relationships.
To keep it sane, I don’t do naive all-vs-all. I use:
Zone-based spatial indexing so most checks only run against local neighbors (roughly n/16 instead of n). Temporal caching of indices so they’re not rebuilt every tick. Statistical sampling for crowd density at high population (estimate from a fixed-size sample instead of full scans).
So in practice it’s closer to O(n² / k), with k ≈ 16–50 depending on zone layout and population. You still see spikes during blooms, but it’s usually 10–30× faster than naive pairwise checks.
FPS dips usually line up with big ecological events (population blooms, mass reproduction, lots of giant organisms, pathogen waves). The sim and rendering are decoupled, so you get (hopefully) brief spikes when a lot happens at once, then it settles again as populations crash/thin out. It’s normal behavior, not a runaway bug.
I’ve spent a lot of time optimizing hot paths and getting calculations down, but when the ecology goes a bit wild, you’ll still see temporary spikes. If you want to focus on stats only, you can switch to Simmer mode (no graphs) or turn off the arena, which reduces rendering work quite a bit.
I guess some O(n^2) algorithm.
Yes, some parts are inherently O(n²) (mate finding, crowd density, predator/prey proximity, pathogen spread). Ecology needs pairwise relationships.
To keep it sane, I don’t do naive all-vs-all. I use:
Zone-based spatial indexing so most checks only run against local neighbors (roughly n/16 instead of n). Temporal caching of indices so they’re not rebuilt every tick. Statistical sampling for crowd density at high population (estimate from a fixed-size sample instead of full scans).
So in practice it’s closer to O(n² / k), with k ≈ 16–50 depending on zone layout and population. You still see spikes during blooms, but it’s usually 10–30× faster than naive pairwise checks.
> Yes, some parts are inherently O(n²) (mate finding, crowd density, predator/prey proximity, pathogen spread). Ecology needs pairwise relationships.
You might be able to do a bit better for some of these, check this out https://en.wikipedia.org/wiki/N-body_simulation
Essentially the idea in some optimizations is to divide the space into cells, and so you can discard relations with elements in far away cells.