kristjaneerik / xorshift

Vectorized xorshift and xoroshiro uniform/binomial RNGs

xorshift: Fast vectorized RNGs for Python

C/Python implementation of Sebastiano Vigna's xoroshiro128+ and xorshift128+ random number generators. These are fast, high quality random bit generators with shorter periods (2^128 - 1) than the usual Mersenne Twister, but also fewer failures on TestU01.

This implementation vectorizes the 64-bit implementations of the xoroshiro128+ and xorshift128+ generators to produce 4 single precision floats per iteration. As currently implemented, they have periods of 2^64, but that is not a fundamental limitation. They're fast: about 5 instructions per uniform float in the inner loop:

(Benchmark results from a 3.1GHz Core i7 13" Retina MacBook Pro, Early 2015)

Benchmarking generation of 131072 Bin(50, 0.25) RVs, 10 iterations
------------------------------------------------------------------
numpy took 15.90 ms/iter,           121.30 ns per float
xoroshiro took 3.88 ms/iter,        29.61 ns per float
xoroshift128plus took 3.46 ms/iter, 26.41 ns per float

Benchmarking generation of 131072 Uniform(0,1) RVs, 10 iterations
-----------------------------------------------------------------
numpy took 1.71 ms/iter,            13.07 ns per float
xoroshiro took 0.11 ms/iter,        0.82 ns per float
xoroshift128plus took 0.10 ms/iter, 0.76 ns per float

Currently, a subset of the numpy.random.RandomState interface is implemented: Xoroshiro or Xorshift128plus generators can be created with a seed (or no seed, seeding from os.urandom(), and they can generate single-precision uniform or binomial variates, of arbitrary shape.

Additionally, the generators can be used in a "no-copy" mode, in which the returned random variates are a window into an internal buffer. This can be useful if you will immediately use the numbers or will directly copy them into another buffer, to avoid an intermediate memory allocation and copy.

API

xorshift.Xoroshiro and xorshift.Xorshift128plus implement the following Generator interface:

Methods

__init__(self, seed=None, copy=True):

Initialize the generator with the given seed. seed may be None or any object defining the __hash__ magic method. If None, the generator will seed itself from os.urandom().

If copy is False, the returned random numbers will be a window into an internally allocated memory buffer; if copy is False, the returned random numbers will be a freshly allocated ndarray. copy=False is faster, but can lead to errors:

rng_nocopy = xorgen.Xoroshiro(copy=False)

nums_1 = rng_nocopy.uniform(size=4)
nums_2 = rng_nocopy.uniform(size=4)
# nums_1 and nums_2 are now windows into the same array, not independent arrays

copy=False can be useful if you will immediately use the results of the RNG or if you will copy them into your own destination:

scatter_array[indices] = rng_nocopy(size=len(indices))

uniform(low=0.0, high=1.0, size=None)

See numpy.random.uniform

binomial(N, p, size=None)

See numpy.random.binomial