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<div class="moz-cite-prefix">On 20/03/2013 10:29 AM, Jens Nielsen
wrote:<br>
</div>
<blockquote
cite="mid:CAM-Pw024HQQGooWdfFAvT_ig7JrEWMSZG07ACnu+53q20sFfbg@mail.gmail.com"
type="cite">
<div dir="ltr">Hi,
<div><br>
</div>
<div style="">Could also be that they are linked to different
libs such as atlas and standart Blas. What is the output of </div>
<div style="">numpy.show_config() in the two different python
versions. </div>
<div style=""><br>
</div>
<div style="">Jens <br>
</div>
</div>
</blockquote>
<big><big>Thanks for this pointer.<br>
The result for Py2.7:<br>
</big></big>
<blockquote><big><big>>>> numpy.show_config()<br>
atlas_threads_info:<br>
NOT AVAILABLE<br>
blas_opt_info:<br>
libraries = ['f77blas', 'cblas', 'atlas']<br>
library_dirs = ['C:\\local\\lib\\yop\\sse3']<br>
define_macros = [('NO_ATLAS_INFO', -1)]<br>
language = c<br>
atlas_blas_threads_info:<br>
NOT AVAILABLE<br>
lapack_opt_info:<br>
libraries = ['lapack', 'f77blas', 'cblas', 'atlas']<br>
library_dirs = ['C:\\local\\lib\\yop\\sse3']<br>
define_macros = [('NO_ATLAS_INFO', -1)]<br>
language = f77<br>
atlas_info:<br>
libraries = ['lapack', 'f77blas', 'cblas', 'atlas']<br>
library_dirs = ['C:\\local\\lib\\yop\\sse3']<br>
define_macros = [('NO_ATLAS_INFO', -1)]<br>
language = f77<br>
lapack_mkl_info:<br>
NOT AVAILABLE<br>
blas_mkl_info:<br>
NOT AVAILABLE<br>
atlas_blas_info:<br>
libraries = ['f77blas', 'cblas', 'atlas']<br>
library_dirs = ['C:\\local\\lib\\yop\\sse3']<br>
define_macros = [('NO_ATLAS_INFO', -1)]<br>
language = c<br>
mkl_info:<br>
NOT AVAILABLE<br>
>>></big></big><br>
</blockquote>
<big><big>The result for 3.2:<br>
</big></big>
<blockquote><big><big>>>> import numpy<br>
>>> numpy.show_config()<br>
lapack_info:<br>
NOT AVAILABLE<br>
lapack_opt_info:<br>
NOT AVAILABLE<br>
blas_info:<br>
NOT AVAILABLE<br>
atlas_threads_info:<br>
NOT AVAILABLE<br>
blas_src_info:<br>
NOT AVAILABLE<br>
atlas_blas_info:<br>
NOT AVAILABLE<br>
lapack_src_info:<br>
NOT AVAILABLE<br>
atlas_blas_threads_info:<br>
NOT AVAILABLE<br>
blas_mkl_info:<br>
NOT AVAILABLE<br>
blas_opt_info:<br>
NOT AVAILABLE<br>
atlas_info:<br>
NOT AVAILABLE<br>
lapack_mkl_info:<br>
NOT AVAILABLE<br>
mkl_info:<br>
NOT AVAILABLE<br>
>>><br>
</big></big></blockquote>
<big><big>I hope that this helps.<br>
<br>
Colin W.<br>
</big></big>
<blockquote
cite="mid:CAM-Pw024HQQGooWdfFAvT_ig7JrEWMSZG07ACnu+53q20sFfbg@mail.gmail.com"
type="cite">
<div class="gmail_extra"><br>
<div class="gmail_quote">On Wed, Mar 20, 2013 at 2:14 PM, Daπid
<span dir="ltr"><<a moz-do-not-send="true"
href="mailto:davidmenhur@gmail.com" target="_blank">davidmenhur@gmail.com</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">Without
much detailed knowledge of the topic, I would expect both<br>
versions to give very similar timing, as it is essentially a
call to<br>
ATLAS function, not much is done in Python.<br>
<br>
Given this, maybe the difference is in ATLAS itself. How
have you<br>
installed it? When you compile ATLAS, it will do some
machine-specific<br>
optimisation, but if you have installed a binary chances are
that your<br>
version is optimised for a machine quite different from
yours. So, two<br>
different installations could have been compiled in
different machines<br>
and so one is more suited for your machine. If you want to
be sure, I<br>
would try to compile ATLAS (this may be difficult) or check
the same<br>
on a very different machine (like an AMD processor,
different<br>
architecture...).<br>
<br>
<br>
<br>
Just for reference, on Linux Python 2.7 64 bits can deal
with these<br>
matrices easily.<br>
<br>
%timeit mat=np.random.random((6143,6143)); matinv=
np.linalg.inv(mat);<br>
res = np.dot(mat, matinv); diff= res-np.eye(6143); print<br>
np.sum(np.abs(diff))<br>
2.41799631031e-05<br>
1.13955868701e-05<br>
3.64338191541e-05<br>
1.13484781021e-05<br>
1 loops, best of 3: 156 s per loop<br>
<br>
Intel i5, 4 GB of RAM and SSD. ATLAS installed from Fedora
repository<br>
(I don't run heavy stuff on this computer).<br>
<div>
<div class="h5"><br>
On 20 March 2013 14:46, Colin J. Williams <<a
moz-do-not-send="true" href="mailto:cjw@ncf.ca">cjw@ncf.ca</a>>
wrote:<br>
> I have a small program which builds random matrices
for increasing matrix<br>
> orders, inverts the matrix and checks the precision
of the product. At some<br>
> point, one would expect operations to fail, when
the memory capacity is<br>
> exceeded. In both Python 2.7 and 3.2 matrices of
order 3,071 area handled,<br>
> but not 6,143.<br>
><br>
> Using wall-clock times, with win32, Python 3.2 is
slower than Python 2.7.<br>
> The profiler indicates a problem in the solver.<br>
><br>
> Done on a Pentium, with 2.7 GHz processor, 2 GB of
RAM and 221 GB of free<br>
> disk space. Both Python 3.2.3 and Python 2.7.3 use
numpy 1.6.2.<br>
><br>
> The results are show below.<br>
><br>
> Colin W.<br>
><br>
> aaaa_ssss<br>
> 2.7.3 (default, Apr 10 2012, 23:31:26) [MSC v.1500
32 bit (Intel)]<br>
> order= 2 measure ofimprecision= 0.097 Time
elapsed (seconds)=<br>
> 0.004143<br>
> order= 5 measure ofimprecision= 2.207 Time
elapsed (seconds)=<br>
> 0.001514<br>
> order= 11 measure ofimprecision= 2.372 Time
elapsed (seconds)=<br>
> 0.001455<br>
> order= 23 measure ofimprecision= 3.318 Time
elapsed (seconds)=<br>
> 0.001608<br>
> order= 47 measure ofimprecision= 4.257 Time
elapsed (seconds)=<br>
> 0.002339<br>
> order= 95 measure ofimprecision= 4.986 Time
elapsed (seconds)=<br>
> 0.005747<br>
> order= 191 measure ofimprecision= 5.788 Time
elapsed (seconds)=<br>
> 0.029974<br>
> order= 383 measure ofimprecision= 6.765 Time
elapsed (seconds)=<br>
> 0.145339<br>
> order= 767 measure ofimprecision= 7.909 Time
elapsed (seconds)=<br>
> 0.841142<br>
> order= 1535 measure ofimprecision= 8.532 Time
elapsed (seconds)=<br>
> 5.793630<br>
> order= 3071 measure ofimprecision= 9.774 Time
elapsed (seconds)=<br>
> 39.559540<br>
> order= 6143 Process terminated by a MemoryError<br>
><br>
> Above: 2.7.3 Below: Python 3.2.3<br>
><br>
> bbb_bbb<br>
> 3.2.3 (default, Apr 11 2012, 07:15:24) [MSC v.1500
32 bit (Intel)]<br>
> order= 2 measure ofimprecision= 0.000 Time
elapsed (seconds)=<br>
> 0.113930<br>
> order= 5 measure ofimprecision= 1.807 Time
elapsed (seconds)=<br>
> 0.001373<br>
> order= 11 measure ofimprecision= 2.395 Time
elapsed (seconds)=<br>
> 0.001468<br>
> order= 23 measure ofimprecision= 3.073 Time
elapsed (seconds)=<br>
> 0.001609<br>
> order= 47 measure ofimprecision= 5.642 Time
elapsed (seconds)=<br>
> 0.002687<br>
> order= 95 measure ofimprecision= 5.745 Time
elapsed (seconds)=<br>
> 0.013510<br>
> order= 191 measure ofimprecision= 5.866 Time
elapsed (seconds)=<br>
> 0.061560<br>
> order= 383 measure ofimprecision= 7.129 Time
elapsed (seconds)=<br>
> 0.418490<br>
> order= 767 measure ofimprecision= 8.240 Time
elapsed (seconds)=<br>
> 3.815713<br>
> order= 1535 measure ofimprecision= 8.735 Time
elapsed (seconds)=<br>
> 27.877270<br>
> order= 3071 measure ofimprecision= 9.996 Time
elapsed<br>
> (seconds)=212.545610<br>
> order= 6143 Process terminated by a MemoryError<br>
><br>
><br>
><br>
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