Multithreaded generation

The four core distributions (random, standard_normal, standard_exponential, and standard_gamma) all allow existing arrays to be filled using the out keyword argument. Existing arrays need to be contiguous and well-behaved (writable and aligned). Under normal circumstances, arrays created using the common constructors such as numpy.empty will satisfy these requirements.

See also

Thread Safety for general information about thread safety in NumPy.

This example makes use of concurrent.futures to fill an array using multiple threads. Threads are long-lived so that repeated calls do not require any additional overheads from thread creation.

The random numbers generated are reproducible in the sense that the same seed will produce the same outputs, given that the number of threads does not change.

from numpy.random import default_rng, SeedSequence
import multiprocessing
import concurrent.futures
import numpy as np

class MultithreadedRNG:
    def __init__(self, n, seed=None, threads=None):
        if threads is None:
            threads = multiprocessing.cpu_count()
        self.threads = threads

        seq = SeedSequence(seed)
        self._random_generators = [default_rng(s)
                                   for s in seq.spawn(threads)]

        self.n = n
        self.executor = concurrent.futures.ThreadPoolExecutor