Supposedly can control through env variables but I didn't see any effect
On Wed, Feb 24, 2021, 10:12 AM Charles R Harris
wrote:
>
>
> On Wed, Feb 24, 2021 at 8:02 AM Charles R Harris <
> charlesr.har...@gmail.com> wrote:
>
>>
>>
>> On Wed, Feb 24, 2021 at 5:36 AM Neal Becker wrote:
>>
>>> See
On Wed, Feb 24, 2021 at 8:02 AM Charles R Harris
wrote:
>
>
> On Wed, Feb 24, 2021 at 5:36 AM Neal Becker wrote:
>
>> See my earlier email - this is fedora 33, python3.9.
>>
>> I'm using fedora 33 standard numpy.
>> ldd says:
>>
>> /usr/lib64/python3.9/site-packages/numpy/core/_
>>
On Wed, Feb 24, 2021 at 5:36 AM Neal Becker wrote:
> See my earlier email - this is fedora 33, python3.9.
>
> I'm using fedora 33 standard numpy.
> ldd says:
>
> /usr/lib64/python3.9/site-packages/numpy/core/_
> multiarray_umath.cpython-39-x86_64-linux-gnu.so:
> linux-vdso.so.1
See my earlier email - this is fedora 33, python3.9.
I'm using fedora 33 standard numpy.
ldd says:
/usr/lib64/python3.9/site-packages/numpy/core/_multiarray_umath.cpython-39-x86_64-linux-gnu.so:
linux-vdso.so.1 (0x7ffdd1487000)
libflexiblas.so.3 => /lib64/libflexiblas.so.3
On Tue, Feb 23, 2021 at 5:47 PM Charles R Harris
wrote:
>
>
> On Tue, Feb 23, 2021 at 11:10 AM Neal Becker wrote:
>
>> I have code that performs dot product of a 2D matrix of size (on the
>> order of) [1000,16] with a vector of size [1000]. The matrix is
>> float64 and the vector is
On Tue, Feb 23, 2021 at 11:10 AM Neal Becker wrote:
> I have code that performs dot product of a 2D matrix of size (on the
> order of) [1000,16] with a vector of size [1000]. The matrix is
> float64 and the vector is complex128. I was using numpy.dot but it
> turned out to be a bottleneck.
>
>
The stackoverflow link above contains a simple testcase:
>>> from scipy.linalg import get_blas_funcs>>> gemm = get_blas_funcs("gemm",
>>> [X, Y])>>> np.all(gemm(1, X, Y) == np.dot(X, Y))True
It would be of interest to benchmark gemm against np.dot. Maybe np.dot
doesn't use blas at al for
On Tue, 23 Feb 2021, 7:41 pm Roman Yurchak, wrote:
> For the first benchmark apparently A.dot(B) with A real and B complex is
> a known issue performance wise https://github.com/numpy/numpy/issues/10468
I splitted B into a vector of size (N, 2) for the real and imaginary part,
and that makes
I'm using fedora 33 standard numpy.
ldd says:
/usr/lib64/python3.9/site-packages/numpy/core/_multiarray_umath.cpython-39-x86_64-linux-gnu.so:
linux-vdso.so.1 (0x7ffdd1487000)
libflexiblas.so.3 => /lib64/libflexiblas.so.3 (0x7f0512787000)
So whatever flexiblas is doing controls blas.
On
https://stackoverflow.com/questions/19839539/how-to-get-faster-code-than-numpy-dot-for-matrix-multiplication
maybe C_CONTIGUOUS vs F_CONTIGUOUS?
Carl
Am Di., 23. Feb. 2021 um 19:52 Uhr schrieb Neal Becker :
> One suspect is that it seems the numpy version was multi-threading.
> This isn't
One suspect is that it seems the numpy version was multi-threading.
This isn't useful here, because I'm running parallel monte-carlo
simulations using all cores. Perhaps this is perversely slowing
things down? I don't know how to account for 1000x slowdown though.
On Tue, Feb 23, 2021 at 1:40
For the first benchmark apparently A.dot(B) with A real and B complex is
a known issue performance wise https://github.com/numpy/numpy/issues/10468
In general, it might be worth trying different BLAS backends. For
instance, if you install numpy from conda-forge you should be able to
switch
Hi,
On Tue, 23 Feb 2021 at 19.11, Neal Becker wrote:
> I have code that performs dot product of a 2D matrix of size (on the
> order of) [1000,16] with a vector of size [1000]. The matrix is
> float64 and the vector is complex128. I was using numpy.dot but it
> turned out to be a bottleneck.
>
I have code that performs dot product of a 2D matrix of size (on the
order of) [1000,16] with a vector of size [1000]. The matrix is
float64 and the vector is complex128. I was using numpy.dot but it
turned out to be a bottleneck.
So I coded dot2x1 in c++ (using xtensor-python just for the
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