[SciPy-user] scipy.basic to numpy
Arnd Baecker
arnd.baecker at web.de
Fri Jan 6 02:36:16 EST 2006
Hi Pearu,
On Thu, 5 Jan 2006, Pearu Peterson wrote:
> On Thu, 5 Jan 2006, Rob Managan wrote:
>
> > Sorry for being unclear. I was referencing the numpy.dft.fft function
> > to show that it works but the fftpack version in scipy does not.
> >
> > The problem is in scipy not numpy.
>
> I tried building scipy.fftpack against fftw-2.1.3, fftw-3.0.1, and fortran
> fftpack. In all cases scipy.fftpack.test() finishes without failures on
> debian box, both on 32 and 64-bit boxes.
Do you also observe a very poor performance of fftw-3.0.1 for
(in particular for complex Arrays)?
Best, Arnd
(some) Details:
In [11]: numpy.__version__
Out[11]: '0.9.3.1837'
In [12]: scipy.__version__
Out[12]: '0.4.4.1526'
This is on an Opteron, but we also see similar results on
other machines ...
Fast Fourier Transform
=================================================
| real input | complex input
-------------------------------------------------
size | scipy | Numeric | scipy | Numeric
-------------------------------------------------
100 | 0.05 | 0.06 | 0.88 | 0.05 (secs for 7000 calls)
1000 | 0.04 | 0.08 | 0.51 | 0.08 (secs for 2000 calls)
256 | 0.11 | 0.10 | 1.43 | 0.11 (secs for 10000 calls)
512 | 0.17 | 0.19 | 1.68 | 0.19 (secs for 10000 calls)
1024 | 0.02 | 0.04 | 0.23 | 0.03 (secs for 1000 calls)
2048 | 0.04 | 0.07 | 0.34 | 0.07 (secs for 1000 calls)
4096 | 0.05 | 0.11 | 0.29 | 0.11 (secs for 500 calls)
8192 | 0.11 | 0.48 | 0.65 | 0.48 (secs for 500 calls)
....
Multi-dimensional Fast Fourier Transform
===================================================
| real input | complex input
---------------------------------------------------
size | scipy | Numeric | scipy | Numeric
---------------------------------------------------
100x100 | 0.06 | 0.06 | 0.05 | 0.07 (secs for 100 calls)
1000x100 | 0.05 | 0.11 | 0.06 | 0.10 (secs for 7 calls)
256x256 | 0.11 | 0.10 | 0.12 | 0.11 (secs for 10 calls)
512x512 | 0.34 | 0.20 | 0.32 | 0.20 (secs for 3 calls)
.....
Inverse Fast Fourier Transform
===============================================
| real input | complex input
-----------------------------------------------
size | scipy | Numeric | scipy | Numeric
-----------------------------------------------
100 | 0.05 | 0.15 | 0.92 | 0.14 (secs for 7000 calls)
1000 | 0.06 | 0.17 | 0.54 | 0.18 (secs for 2000 calls)
256 | 0.11 | 0.27 | 1.49 | 0.28 (secs for 10000 calls)
512 | 0.17 | 0.43 | 1.76 | 0.45 (secs for 10000 calls)
1024 | 0.02 | 0.07 | 0.24 | 0.08 (secs for 1000 calls)
2048 | 0.05 | 0.14 | 0.35 | 0.14 (secs for 1000 calls)
4096 | 0.05 | 0.18 | 0.30 | 0.20 (secs for 500 calls)
8192 | 0.10 | 0.70 | 0.67 | 0.73 (secs for 500 calls)
! ldd
/home/abaecker/BUILDS3/BuildDir/inst_numpy/lib/python2.4/site-packages/scipy/fftpack/_fftpack.so
libfftw3.so.3 => /scr/python/lib/libfftw3.so.3
(0x00002aaaaabb6000)
libg2c.so.0 => /scr/python/lib64/libg2c.so.0 (0x00002aaaaad66000)
libm.so.6 => /lib64/tls/libm.so.6 (0x00002aaaaaebc000)
libgcc_s.so.1 => /scr/python/lib64/libgcc_s.so.1
(0x00002aaaab014000)
libc.so.6 => /lib64/tls/libc.so.6 (0x00002aaaab11f000)
/lib64/ld-linux-x86-64.so.2 (0x0000555555554000)
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