6 ms·

Ah yes, Xorshift, the RANDU [1] of the 21st century [2]. There is no real use case for better non-CS generators, as explained by adrian_b back in 2021 [3]. [1

Ah yes, Xorshift, the RANDU [1] of the 21st century [2]. There is no real use case for better non-CS generators, as explained by adrian_b back in 2021 [3]. [1] https://en.wikipedia.org/wiki/RANDU https://en.wikipedia.org/wiki/RANDU [2] https://arxiv.org/abs/1908.10020 https://arxiv.org/abs/1908.10020 [3] https://news.ycombinator.com/item?id=28886698 https://news.ycombinator.com/item?id=28886698

moregrist 2mo ago
I’m not sure what point you’re trying to make, exactly, but a use case for better non-CS generators has always been stochastic simulation, especially simulation/sampling approaches that are bound by the number and quality of uniform variates per second. As someone who has spent considerable time working in these areas, I still appreciate advances.
- saithound 2mo ago
  > 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.
  - dgacmu 2mo ago
    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.
    - saithound 2mo ago
      When was the last time a new PRNG helped you clearly identify a performance bottleneck? As in, you were using state of the art generator X, and you couldn't see the performance bottleneck, but updating to a newer (faster, or same speed but higher quality) generator Y, and could subsequently identify the performance bottleneck? If you're using PCG, not in the last 12 years. (In a parallel comment I suggest trying AES-CTR for this use case)
      - dgacmu 2mo ago
        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. :)
        Straw 2mo ago
        Most 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?
      - saithound 2mo ago
        dgacmu: 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.
mallets 2mo ago
I don't know about all that, but I use Marsaglias for generating noise samples in MCUs like Pico. It's the fastest option there is for such devices.
- AlanZucconi 2mo ago
  I agree! For many games (especially the ones running on old devices), xorshifts are pretty good! Not all applications need cryptographically safe generators! And sometimes it's ok to trade complexity for speed! But I agree that if you're building something new aimed for modern devices, Marsaglia's xorshift128 wouldn't be my first choice! But I'd definitely want it in my RNG library for backward compatibility!
  - WalterGillman 1mo ago
    If you are interested in performant retro PRNGs, you can find a link to mine in my profile, if I am not mistaken. The counter is based on xorshift. As of the time of release it passed all tests that could be passed with only a 2^32-1 period. On Z80 it was only about as fast as RC4 which is also a good option in terms of performance and quality but whereas RC4 has a huge state, this one only has a 32bit state, the rest staying in ROM.
kevin_thibedeau 1mo ago
> no real use case Yes there is. Not every system has the need or the resources to maintain a secure random sequence. You may also want a reproducible pseudo-random sequence in generative code that logs the seeds. Because of the misguided attitude that nobody needs these features, everyone who does need them has to roll their own now.
- Dylan16807 1mo ago
  > Not every system has the need or the resources to maintain a secure random sequence. I'm sure there's something, but that category has to be shrinking every year. What does such a system look like this decade, that needs random numbers but can't easily implement something like AES? > You may also want a reproducible pseudo-random sequence in generative code that logs the seeds. Because of the misguided attitude that nobody needs these features, everyone who does need them has to roll their own now. I don't know what difficulty you're referring to. Basically every CSPRNG can be seeded easily and you can log the seed.

moregrist 2mo ago

I’m not sure what point you’re trying to make, exactly, but a use case for better non-CS generators has always been stochastic simulation, especially simulation/sampling approaches that are bound by the number and quality of uniform variates per second. As someone who has spent considerable time working in these areas, I still appreciate advances.

saithound 2mo ago
> 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.
- dgacmu 2mo ago
  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.
  - saithound 2mo ago
    When was the last time a new PRNG helped you clearly identify a performance bottleneck? As in, you were using state of the art generator X, and you couldn't see the performance bottleneck, but updating to a newer (faster, or same speed but higher quality) generator Y, and could subsequently identify the performance bottleneck? If you're using PCG, not in the last 12 years. (In a parallel comment I suggest trying AES-CTR for this use case)
    - dgacmu 2mo ago
      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. :)
      - Straw 2mo ago
        Most 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?
    - saithound 2mo ago
      dgacmu: 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.

mallets 2mo ago

I don't know about all that, but I use Marsaglias for generating noise samples in MCUs like Pico. It's the fastest option there is for such devices.

AlanZucconi 2mo ago
I agree! For many games (especially the ones running on old devices), xorshifts are pretty good! Not all applications need cryptographically safe generators! And sometimes it's ok to trade complexity for speed! But I agree that if you're building something new aimed for modern devices, Marsaglia's xorshift128 wouldn't be my first choice! But I'd definitely want it in my RNG library for backward compatibility!
- WalterGillman 1mo ago
  If you are interested in performant retro PRNGs, you can find a link to mine in my profile, if I am not mistaken. The counter is based on xorshift. As of the time of release it passed all tests that could be passed with only a 2^32-1 period. On Z80 it was only about as fast as RC4 which is also a good option in terms of performance and quality but whereas RC4 has a huge state, this one only has a 32bit state, the rest staying in ROM.

kevin_thibedeau 1mo ago

> no real use case Yes there is. Not every system has the need or the resources to maintain a secure random sequence. You may also want a reproducible pseudo-random sequence in generative code that logs the seeds. Because of the misguided attitude that nobody needs these features, everyone who does need them has to roll their own now.

Dylan16807 1mo ago
> Not every system has the need or the resources to maintain a secure random sequence. I'm sure there's something, but that category has to be shrinking every year. What does such a system look like this decade, that needs random numbers but can't easily implement something like AES? > You may also want a reproducible pseudo-random sequence in generative code that logs the seeds. Because of the misguided attitude that nobody needs these features, everyone who does need them has to roll their own now. I don't know what difficulty you're referring to. Basically every CSPRNG can be seeded easily and you can log the seed.