Multithreaded Generation

The four core distributions 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 PRNG is xorshift2014 which is fast, has a long period and supports using jumped to advance the state. The random numbers generated are reproducible in the sense that the same seed will produce the same outputs.

from randomgen import Xoshiro256
import multiprocessing
import concurrent.futures
import numpy as np
import warnings

warnings.filterwarnings("ignore", "Generator", FutureWarning)

class MultithreadedRNG(object):
    def __init__(self, n, seed=None, threads=None):
        last_bg = Xoshiro256(seed, mode="sequence")
        if threads is None:
            threads = multiprocessing.cpu_count()
        self.threads = threads

        self._random_generators = []
        for _ in range(0, threads):
            last_bg = last_bg.jumped()

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

    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, threads=4)
...: 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()

17.9 ms ± 85.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

The single threaded call directly uses the PRNG.

In [5]: values = np.empty(10000000)
    ...: rg = Generator(Xoshiro256())
    ...: %timeit rg.standard_normal(out=values)

66.5 ms ± 171 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

The gains are substantial and the scaling is reasonable even for arrays 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(Xoshiro256())
    ...: %timeit rg.standard_normal(10000000)

76.1 ms ± 208 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)