多线程生成

四个核心分布（random、standard_normal、standard_exponential 和 standard_gamma）都允许使用 out 关键字参数填充现有数组.现有数组需要是连续且行为良好的（可写和已对齐）.在正常情况下,使用常见的构造函数（如 numpy.empty）创建的数组将满足这些要求.

这个例子使用了 Python 3 concurrent.futures 来使用多个线程填充一个数组.线程是长期存在的,因此重复调用不需要任何来自线程创建的额外开销.

生成的随机数是可重复的,因为相同的种子在给定线程数不变的情况下会产生相同的输出.

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(threads)
        self.values = np.empty(n)
        self.step = np.ceil(n / threads).astype(np.int_)

    def fill(self):
        def _fill(random_state, out, first, last):
            random_state.standard_normal(out=out[first:last])

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

    def __del__(self):
        self.executor.shutdown(False)

多线程随机数生成器可以用来填充数组.``values`` 属性显示填充前的零值和填充后的随机值.

In [2]: mrng = MultithreadedRNG(10000000, seed=12345)
   ...: print(mrng.values[-1])
Out[2]: 0.0

In [3]: mrng.fill()
   ...: print(mrng.values[-1])
Out[3]: 2.4545724517479104

使用多线程所需的时间可以与使用单线程生成所需的时间进行比较.

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

Out[4]: 4
   ...: 32.8 ms ± 2.71 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

单线程调用直接使用 BitGenerator.

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

Out[5]: 99.6 ms ± 222 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

收益是显著的,即使对于仅适度大的数组,扩展也是合理的.与不使用现有数组的调用相比,由于数组创建开销,收益甚至更大.

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

Out[6]: 125 ms ± 309 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

请注意,如果用户没有设置 threads ,它将由 multiprocessing.cpu_count() 决定.

In [7]: # simulate the behavior for `threads=None`, if the machine had only one thread
   ...: mrng = MultithreadedRNG(10000000, seed=12345, threads=1)
   ...: print(mrng.values[-1])
Out[7]: 1.1800150052158556