[Numpy-discussion] Looking for a difference between Numpy 0.19.5 and 0.20 explaining a perf regression with Pythran
pierre.augier at univ-grenoble-alpes.fr
Fri Mar 12 18:33:42 EST 2021
I tried to compile Numpy with `pip install numpy==1.20.1 --no-binary numpy --force-reinstall` and I can reproduce the regression.
Good news, I was able to reproduce the difference with only Numpy 1.20.1.
Arrays prepared with (`df` is a Pandas dataframe)
arr = df.values.copy()
arr = np.ascontiguousarray(df.values)
lead to "slow" execution while arrays prepared with
arr = np.copy(df.values)
lead to faster execution.
arr.copy() or np.copy(arr) do not give the same result, with arr obtained from a Pandas dataframe with arr = df.values. It's strange because type(df.values) gives <class 'numpy.ndarray'> so I would expect arr.copy() and np.copy(arr) to give exactly the same result.
Note that I think I'm doing quite serious and reproducible benchmarks. I also checked that this regression is reproducible on another computer.
----- Mail original -----
> De: "Sebastian Berg" <sebastian at sipsolutions.net>
> À: "numpy-discussion" <numpy-discussion at python.org>
> Envoyé: Vendredi 12 Mars 2021 22:50:24
> Objet: Re: [Numpy-discussion] Looking for a difference between Numpy 0.19.5 and 0.20 explaining a perf regression with
> On Fri, 2021-03-12 at 21:36 +0100, PIERRE AUGIER wrote:
>> I'm looking for a difference between Numpy 0.19.5 and 0.20 which
>> could explain a performance regression (~15 %) with Pythran.
>> I observe this regression with the script
>> Pythran reimplements Numpy so it is not about Numpy code for
>> computation. However, Pythran of course uses the native array
>> contained in a Numpy array. I'm quite sure that something has changed
>> between Numpy 0.19.5 and 0.20 (or between the corresponding wheels?)
>> since I don't get the same performance with Numpy 0.20. I checked
>> that the values in the arrays are the same and that the flags
>> characterizing the arrays are also the same.
>> Good news, I'm now able to obtain the performance difference just
>> with Numpy 0.19.5. In this code, I load the data with Pandas and need
>> to prepare contiguous Numpy arrays to give them to Pythran. With
>> Numpy 0.19.5, if I use np.copy I get better performance that with
>> np.ascontiguousarray. With Numpy 0.20, both functions create array
>> giving the same performance with Pythran (again, less good that with
>> Numpy 0.19.5).
>> Note that this code is very efficient (more that 100 times faster
>> than using Numpy), so I guess that things like alignment or memory
>> location can lead to such difference.
>> More details in this issue
>> Any help to understand what has changed would be greatly appreciated!
> If you want to really dig into this, it would be good to do profiling
> to find out at where the differences are.
> Without that, I don't have much appetite to investigate personally. The
> reason is that fluctuations of ~30% (or even much more) when running
> the NumPy benchmarks are very common.
> I am not aware of an immediate change in NumPy, especially since you
> are talking pythran, and only the memory space or the interface code
> should matter.
> As to the interface code... I would expect it to be quite a bit faster,
> not slower.
> There was no change around data allocation, so at best what you are
> seeing is a different pattern in how the "small array cache" ends up
> being used.
> Unfortunately, getting stable benchmarks that reflect code changes
> exactly is tough... Here is a nice blog post from Victor Stinner where
> he had to go as far as using "profile guided compilation" to avoid
> I somewhat hope that this is also the reason for the huge fluctuations
> we see in the NumPy benchmarks due to absolutely unrelated code
> But I did not have the energy to try it (and a probably fixed bug in
> gcc makes it a bit harder right now).
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