It's not critical but if you gave me something that behaved statistically like PCG (i.e., I didn't fret about whether it was going to cause me weird problems) but was twice as fast I'd be happy and would shift to it - it would speed up profiling and measuring and that would be nice. We still find ourselves often pre-generating a list into memory to keep the prng entirely off of the measurement path. It wouldn't be magic, but I don't need magic. I like nice things that make my life a little easier in a small corner of my research. :)
replyMost modern RNGs should be faster than memory bandwidth (when optimized), so unless your list is small enough to fit in cache, its unclear if this is faster?
replydgacmu: if you're writing C on x64, try AES-128-CTR (AES-NI, 8 way) using the header wmmintrin.h which has hardware accelerated primitives for this. An LLM can implement the RNG for you based on this comment if you want to test it out quickly. It should be faster than PCG, and higher quality.
replyWill do. I'm on vacation right now and losing my laptop for a few days, but seems worth trying. My recollection from the RNGs a decade ago (I'm dating myself) was that the AES approaches had higher latency but were quite decent, though slower than PCG. Curious how that's evolved.
reply> When was the last time a new PRNG helped you clearly identify a performance bottleneck?
replyWhile not a bottleneck as such, I contributed to a photorealistic path tracer using the Metropolis algorithm[1], and we got a 10-15% increase in samples/second when we switched from a decent to a much faster and better PRNG. Like you we didn't think the performance of it mattered much until we profiled it.
Granted this was a decade or so ago, would be interesting to compare the state of the art PRNGs.
Anyway, just pointing out that there can be real-world cases.
[1]: https://en.wikipedia.org/wiki/Metropolis_light_transport