alt Hacker News> I’m not sure what point you’re trying to make,
Have you skimmed the linked thread?
> especially simulation/sampling approaches that are bound by the number and quality of uniform variates per second
Sorry, nobody does stochastic simulations where the number of uniform random numbers obtained per second is any sort of bottleneck. If you've spent considerable time on stochastic simulation, you already know this.
But even if you insist that you alone are doing some very weird stochastic simulation which is somehow bottlenecked on sourcing random numbers fast enough, the falling in planes phenomenon linked above would make xorshift-type generators a poor choice for most sorts of simulations. It introduces spatial correlations into any sort of lattice dynamics simulation (Ising model, percolation) and every high dimensional Monte Carlo integration. Beyond falling in the planes, since xorshift is linear over GF(2), it is also a particularly bad choice for nondeterministic cellular automata and Boolean dynamical systems which use parity, bit masks, or xors.
AES-CTR throughput on a modern CPU is higher than that of xoshiro256++, and much higher quality. No advances in non-CS PRNGs can beat that while maintaining the same quality. If your stochastic simulation is bottlenecked on random bits, CSPRNGs are still the way to go, and they don't interact in nasty ways with any dynamical system you can actually sinulate quickly.
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This is a very weird hill to die on.
I do a lot of testing and designing of things like hash tables and filters, and having a really fast, non-CS generator is incredibly useful for being able to clearly identify performance bottlenecks in designs. PCG has been spectacularly useful for that purpose for me.
> Sorry, nobody does stochastic simulations where the number of uniform random numbers obtained per second is any sort of bottleneck. If you've spent considerable time on stochastic simulation, you already know this.
Actually, I spent a considerable amount of time in my doctorate and postdoc doing this.
Any kind of MCMC sampling of a simple model tends to be bound by the rate you can draw variates.
Examples of this include: Gillespie simulations of chemical kinetics, Ising and Potts lattice models (including their roughly bazillion variations), and anything resembling bootstrap or permutation sampling.
Just because your problems aren’t bound by the rate of drawing uniform variates doesn’t mean that these problems don’t exist. It just means that you have a narrow view.