On Wed, Jan 19, 2022 at 11:50 AM Francesc Alted <fal...@gmail.com> wrote:
>
>
> On Wed, Jan 19, 2022 at 7:33 AM Stefan van der Walt <stef...@berkeley.edu>
> wrote:
>
>> On Tue, Jan 18, 2022, at 21:55, Warren Weckesser wrote:
>> > expr = 'z.real**2 + z.imag**2'
>> >
>> > z = generate_sample(n, rng)
>>
>> 🤔 If I duplicate the `z = ...` line, I get the fast result throughout.
>> If, however, I use `generate_sample(1, rng)` (or any other value than `n`),
>> it does not improve matters.
>>
>> Could this be a memory caching issue?
>>
>
> I can also reproduce that, but only on my Linux boxes. My MacMini does
> not notice the difference.
>
> Interestingly enough, you don't even need an additional call to
> `generate_sample(n, rng)`. If one use `z = np.empty(...)` and then do an
> assignment, like:
>
> z = np.empty(n, dtype=np.complex128)
> z[:] = generate_sample(n, rng)
>
> then everything runs at the same speed:
>
Just turning the view into a copy inside `generate_sample()` will also make
the difference go away, for probably the same reason as this version.
András
>
> numpy version 1.20.3
>
> 142.3667 microseconds
> 142.3717 microseconds
> 142.3781 microseconds
>
> 142.7593 microseconds
> 142.3579 microseconds
> 142.3231 microseconds
>
> As another data point, by doing the same operation but using numexpr I am
> not seeing any difference either, not even on Linux:
>
> numpy version 1.20.3
> numexpr version 2.8.1
>
> 95.6513 microseconds
> 88.1804 microseconds
> 97.1322 microseconds
>
> 105.0833 microseconds
> 100.5555 microseconds
> 100.5654 microseconds
>
> [it is rather like a bit the other way around, the second iteration seems
> a hair faster]
> See the numexpr script below.
>
> I am totally puzzled here.
>
> """
> import timeit
> import numpy as np
> import numexpr as ne
>
>
> def generate_sample(n, rng):
> return rng.normal(scale=1000, size=2*n).view(np.complex128)
>
>
> print(f'numpy version {np.__version__}')
> print(f'numexpr version {ne.__version__}')
> print()
>
> rng = np.random.default_rng()
> n = 250000
> timeit_reps = 10000
>
> expr = 'ne.evaluate("zreal**2 + zimag**2")'
>
> z = generate_sample(n, rng)
> zreal = z.real
> zimag = z.imag
> for _ in range(3):
> t = timeit.timeit(expr, globals=globals(), number=timeit_reps)
> print(f"{1e6*t/timeit_reps:9.4f} microseconds")
> print()
>
> z = generate_sample(n, rng)
> zreal = z.real
> zimag = z.imag
> for _ in range(3):
> t = timeit.timeit(expr, globals=globals(), number=timeit_reps)
> print(f"{1e6*t/timeit_reps:9.4f} microseconds")
> print()
> """
>
> --
> Francesc Alted
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