It should not be a problem, pseudorandom numbers are used in simulations, like monte-carlo, all the time.
Nim uses xoroshiro[0] algorithm for std/random module and it produces good quality statistically random bits until 5TB of output. And lower 4 bits have a little bias, but it should not matter as we only use upper 64 out of available 128 bits.
Also, I just now realise that xoroshiro-128+ is really cheap, so perhaps my batching optimisation was unnecessary here.
Nim uses xoroshiro[0] algorithm for std/random module and it produces good quality statistically random bits until 5TB of output. And lower 4 bits have a little bias, but it should not matter as we only use upper 64 out of available 128 bits.
Also, I just now realise that xoroshiro-128+ is really cheap, so perhaps my batching optimisation was unnecessary here.
[0] - https://en.wikipedia.org/wiki/Xorshift#xoroshiro