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.

This example makes use of Python 3 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 underlying BitGenerator is PCG64 which is fast, has a long period and supports using PCG64.jumped to return a new generator while advancing the state. The random numbers generated are reproducible in the sense that the same seed will produce the same outputs.

from numpy.random import Generator, PCG64
import multiprocessing
import concurrent.futures
import numpy as np

class MultithreadedRNG(object):
    def __init__(self, n, seed=None, threads=None):
        rg = PCG64(seed)
        if threads is None:
            threads = multiprocessing.cpu_count()
        self.threads = threads

        self._random_generators = [rg]
        last_rg = rg
        for _ in range(0, threads-1):
            new_rg = last_rg.jumped()
            last_rg = new_rg

        self.n = n
        self.executor = concurrent.futures.ThreadPoolExecutor(threads)
        self.values = np.empty(n)
        self.step = np.ceil(n / threads).astype(

    def fill(self):
        def _fill(random_state, out, first, last):

        futures = {}
        for i in range(self.threads):
            args = (_fill,
                    i * self.step,
                    (i + 1) * self.step)
            futures[self.executor.submit(*args)] = i

    def __del__(self):

The multithreaded random number generator can be used to fill an array. The values attributes shows the zero-value before the fill and the random value after.

In [2]: mrng = MultithreadedRNG(10000000, seed=0)
...: print(mrng.values[-1])

In [3]: mrng.fill()
    ...: print(mrng.values[-1])

The time required to produce using multiple threads can be compared to the time required to generate using a single thread.

In [4]: print(mrng.threads)
    ...: %timeit mrng.fill()

32.8 ms ± 2.71 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

The single threaded call directly uses the BitGenerator.

In [5]: values = np.empty(10000000)
    ...: rg = Generator(PCG64())
    ...: %timeit rg.standard_normal(out=values)

99.6 ms ± 222 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

The gains are substantial and the scaling is reasonable even for large that are only moderately large. The gains are even larger when compared to a call that does not use an existing array due to array creation overhead.

In [6]: rg = Generator(PCG64())
    ...: %timeit rg.standard_normal(10000000)

125 ms ± 309 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

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