CascadeRng — deterministic pseudo-random numbers

Seed-reproducible: the same seed, the same parameters and the same player actions replay the same run.

local Rng = require("CascadeRng")
local r = Rng.new(20250922)
r.next()          -- a float in [0, 1)
r.int(1, 6)       -- an integer in [1, 6]
r.chance(0.3)     -- true 30% of the time
r.getState()      -- snapshot the stream position

Why it is arithmetic only

The obvious choice — a xorshift over bit32 — costs about half a microsecond per draw in this VM, because every bit32 call crosses the native boundary. A tick makes roughly one draw per cell, so that alone was three times the cost of the entire simulation.

The Lehmer (Park–Miller) generator here is one multiply and a remainder, both exact in a double because the product stays inside 2^53:

s = (s * 48271 + 11) % 2147483647

Measured at roughly ten nanoseconds per draw — fifty times cheaper than the xorshift, with the same statistical job to do.

Period

2^31 − 2 draws, which is about twenty-five minutes of continuous simulation at full rate on a 256×256 board. Past that the stream repeats; the run is still fully deterministic, and a cellular automaton this chaotic has long since lost any resemblance to the earlier pass.

hash2

The same trade, for the procedural fields: a few multiplies and a remainder with every intermediate inside 2^53, so the growth field and the terrain generate identically on every machine and cost nothing.