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Comment by thisguy47 | Hacker News Reader

Am I missing something? With numpy on my 2017 Macbook Pro, I can generate 1B random, normally distributed numbers on a CPU in ~1.3 microseconds.

In [3]: %timeit np.random.normal(1000000000)

1.3 µs ± 26.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

Edit: and 2.5e17 in ~7.95 microseconds

In [5]: %timeit np.random.normal((500000000, 500000000))

7.95 µs ± 90.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

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You might be misremembering the numpy.random api, since you appear to only be generating one or two random numbers in your tests.

I get results about 100x slower those of the article's CPU benchmark when using numpy on a 2019 MBP:

  python -m timeit -s 'import numpy as np; x = np.zeros(1000000)' -u msec 'np.random.normal(x)'
  10 loops, best of 5: 30.9 msec per loop

edit: I'm no python wizard myself, so I'm perfectly willing to believe that there's a better way to generate a random array in numpy than what I'm doing.

I don't think that is measured correctly: A CPU at 5GHz would take 0.2ns per cycle, how is it supposed to generate 1 billion (!) numbers in only 100x the time? Even if you had 64c/128t threadrippers in a dualsocket and this workload perfectly distributed, you'd only get within 100'000x the time of a single cycle. There's still a factor of 10'000 left.

You might want to try this one:

  np.random.normal(size=1000000000)

For size=1000 I get on my machine about 36 ns per number (total 36 µsecs). You probably measured for size=1 but a normal of 1 billion, not 1 billion numbers at a normal of 1.

Ah yes, thanks everyone for pointing out my mistake, I am not using the `size` parameter, so this is really only generating a single random number. My mistake!

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