[Numpy-discussion] Re: Performance mystery

[Sebastian Berg]

On Wed, 2022-01-19 at 11:49 +0100, Francesc Alted 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?
> > 

Yes, it is a caching issue for sure.  We have seen similar random
fluctuations before.
You can proof that it is a cache page-fault issue by running it with
`perf --stat`.  I did this twice, once with the second loop removed
(page-faults only):

28333629      page-faults               #  936.234 K/sec
28362718      page-faults               #    1.147 M/sec

The number of page faults is low.  Running only the second one (or
running the first one only once, rather), gave me:

15024      page-faults               #    1.837 K/sec

So that is the *reason*.  I had before tried to figure out why the page
faults differ too much, or if we can do something about it.  But I
never had any reasonable lead on it.

In general, these fluctuations are pretty random, in the sense that
unrelated code changes and recompilation can swap the behaviour easily.
As Andras noted in that he sees the opposite.

I would love to have an idea if there is a way to figure out why the
page-faults are so imbalanced between the two.

(I have not looked at CPU cache misses this time, but since page-faults
happen, I assume that should not matter?)

Cheers,

Sebastian


> 
> 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:
> 
> 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()
> """
> 
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