FAFabHK·May 2, 2021
I think both the PCG family and the xoroshiro family are faster still.
https://prng.di.unimi.it
https://www.pcg-random.org
ETA: https://github.com/lemire/testingRNG
In Evolution of Random Number Generatorslemire
testingRNG
C2018–2021
lemire / testingRNG
testingRNG
Testing common random-number generators (RNG)
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| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
5 sourcesPCG is not the only game in town when it comes to high-quality reproducible and fast PRNGs: https://github.com/lemire/testingRNG#visual-summary Notably, some newer PRNGs are faster, and still pass all statistical tests.
In Generating random numbers using C++ standard library: the problemsAs usual, it is worth testing and seeing the results empirically. For an excellent project testing these and comparing results, see https://github.com/lemire/testingRNG.
In PCG: a family of better random number generatorsThe linked code (https://github.com/lemire/testingRNG/blob/master/source/aesc...) uses Intel's AES instructions directly. Software AES would not be competitive with purpouse built PRNG algorithms speed-wise.
In Minimalist C LibrariesIt's worth pointing out that his two favorite RNGs (xoroshiro128+/xorshift128+) both fail BigCrush. According to [0] and the associated github [1], for a statistically strong RNG which is still fast, AES-CTR or splitmix64/lehmer64 are probably your best bet, unless you have AVX512, in which case an SIMD-accelerated PCG is the way to go [2]. (The other methods cap out at 1 cycle per byte, while the AVX512 PCG is 1 cycle per 32-bit integer, 4x as fast as the fastest previously tested.) While I don't doubt it could be further accelerated, I've added STL compatibility, templated unrolling, and provided some extra utilities (including random access) in a package based off code from [1] (provided by Samuel Neves) which I now use in most of my projects, and which is available at [3].
[0] https://lemire.me/blog/2017/09/08/the-xorshift128-random-n