What noiselang does it parse, convert to a execution graph then carefully use the Cranelift api to describe the program, and under the hood, it will find different opetimization and generate byte code that runs directly in the host CPU.
The reason for it to be killer is that it allows NoiseLang to run as native speeds, with very little compiler/optimization work. it's a very simple repo.
For the RNG, this was a discovery myself, when profiling, i found the many benchmarks were limited by the speed of the RNG itself, ie, if i could genenrate random numbers faster, the simulation would be faster. xoshiro's next number is computed from its current state. So to get number N+1 you must have finished number N. It's a chain: A → B → C → D. Your CPU can run maybe 6 integer operations per cycle, but a chain only ever offers it one to run. Five of the six lanes sit empty.
I tried to use SIMD to speed this up, but still hit the limit, even if using SIMD, it still had to wait for the next number, a massive speed up came from realizing that i can keep four independent xoshiro256++ states and emits four samples per loop iteration, i += 4. Since the four state-update chains share no registers, the out-of-order core issues them in the same cycles instead of stalling on one serial chain.
SIMD gives you more work per instruction. But I wasn't short on work, I was waiting. A 2-wide xoshiro still needs state N before it can compute state N+1, so the chain is the same length and I wait at every link, I just get two numbers per link instead of one.
And each link costs more. xoshiro rotates a 64-bit word every round, and NEON has no 64-bit rotate, so that becomes three instructions instead of one. Twice the numbers, three times the wait.
Four streams wins because it leaves the chain alone. It just runs four of them at once, and the CPU was already idle enough to overlap them for free.