parallel-numpy-rng

A multi-threaded random number generator backed by Numpy RNG, with parallelism provided by Numba.

Overview

Uses the "fast-forward" capability of the PCG-family of RNG, as exposed by the new-style Numpy RNG API, to generate random numbers in a multi-threaded manner. The key is that you get the same random numbers regardless of how many threads were used.

Only a two types of random numbers are supported right now: uniform and normal. More could be added if there is demand, although some kinds, like bounded random ints, are hard to parallelize in the approach used here.

Example + Performance

import numpy as np
import parallel_numpy_rng

seed = 123
parallel_rng = parallel_numpy_rng.default_rng(seed)
numpy_rng = np.random.default_rng(seed)

%timeit numpy_rng.random(size=10**9, dtype=np.float32)                           # 2.85 s
%timeit parallel_rng.random(size=10**9, dtype=np.float32, nthread=1)             # 3.34 s
%timeit parallel_rng.random(size=10**9, dtype=np.float32, nthread=128)           # 67.8 ms

%timeit numpy_rng.standard_normal(size=10**8, dtype=np.float32)                  # 1.12 s
%timeit parallel_rng.standard_normal(size=10**8,dtype=np.float32, nthread=1)     # 1.85 s
%timeit parallel_rng.standard_normal(size=10**8, dtype=np.float32, nthread=128)  # 43.5 ms

Note that the parallel_rng is slower than Numpy when using a single thread, because the parallel implementation requires a slower algorithm in some cases (this is especially noticeable for float64 and normals)

Installation

The code works and is reasonably well tested, so it's probably ready for use. It can be installed from PyPI:

$ pip install parallel-numpy-rng