3 ms·

"Part of the difficulty was conceptual -- programmers were not used to having to write code that used the same number of calls to random within the simulation (

• thatjoeoverthr 5y ago
  The random number generator needs to give the same outcomes on both ends so that you don’t need to transfer results. To this end, you have to use the same deterministic generator code, seed and ensure you call it the same number of times on each system. This last point is because queries to a random number generator also change its state. If system A and B start with some seed, but system A calls it 30 times and system B 29 times, the next call will yield a different result on each system.
• transpostmeta 5y ago
  If you call a pseudo-random number generator, you get the next random number in a strictly defined sequence of numbers derived from the seed. So the generator has state, and needs to be called exactly the same number of times in the same order on all clients to make sure that the random events happen the same on all clients.
• short_sells_poo 5y ago
  I suspect it pertains to the fact that’s most RNGs tend to be pseudo-random number generators that maintain an internal state. Each call of the RNG mutates the internal state. If you have two processes, one calls the RNG 2 times and the other calls it 5 times, the process states will have diverged and the simulation is no longer consistent between them. This is true even if the simulation ends up using 1 value out of those RNG calls, because the RNG state itself is implicitly part of the simulation.