On Wed, Mar 20, 2013 at 2:14 PM, Daπid <davidmenhur@gmail.com> wrote:

Without much detailed knowledge of the topic, I would expect both
versions to give very similar timing, as it is essentially a call to
ATLAS function, not much is done in Python.

Given this, maybe the difference is in ATLAS itself. How have you
installed it? When you compile ATLAS, it will do some machine-specific
optimisation, but if you have installed a binary chances are that your
version is optimised for a machine quite different from yours. So, two
different installations could have been compiled in different machines
and so one is more suited for your machine. If you want to be sure, I
would try to compile ATLAS (this may be difficult) or check the same
on a very different machine (like an AMD processor, different
architecture...).

Just for reference, on Linux Python 2.7 64 bits can deal with these
matrices easily.

%timeit mat=np.random.random((6143,6143)); matinv= np.linalg.inv(mat);
res = np.dot(mat, matinv); diff= res-np.eye(6143); print
np.sum(np.abs(diff))
2.41799631031e-05
1.13955868701e-05
3.64338191541e-05
1.13484781021e-05
1 loops, best of 3: 156 s per loop

Intel i5, 4 GB of RAM and SSD. ATLAS installed from Fedora repository
(I don't run heavy stuff on this computer).

On 20 March 2013 14:46, Colin J. Williams <cjw@ncf.ca> wrote:
> I have a small program which builds random matrices for increasing matrix
> orders, inverts the matrix and checks the precision of the product. At some
> point, one would expect operations to fail, when the memory capacity is
> exceeded. In both Python 2.7 and 3.2 matrices of order 3,071 area handled,
> but not 6,143.
>
> Using wall-clock times, with win32, Python 3.2 is slower than Python 2.7.
> The profiler indicates a problem in the solver.
>
> Done on a Pentium, with 2.7 GHz processor, 2 GB of RAM and 221 GB of free
> disk space. Both Python 3.2.3 and Python 2.7.3 use numpy 1.6.2.
>
> The results are show below.
>
> Colin W.
>
> aaaa_ssss
> 2.7.3 (default, Apr 10 2012, 23:31:26) [MSC v.1500 32 bit (Intel)]
> order= 2 measure ofimprecision= 0.097 Time elapsed (seconds)=
> 0.004143
> order= 5 measure ofimprecision= 2.207 Time elapsed (seconds)=
> 0.001514
> order= 11 measure ofimprecision= 2.372 Time elapsed (seconds)=
> 0.001455
> order= 23 measure ofimprecision= 3.318 Time elapsed (seconds)=
> 0.001608
> order= 47 measure ofimprecision= 4.257 Time elapsed (seconds)=
> 0.002339
> order= 95 measure ofimprecision= 4.986 Time elapsed (seconds)=
> 0.005747
> order= 191 measure ofimprecision= 5.788 Time elapsed (seconds)=
> 0.029974
> order= 383 measure ofimprecision= 6.765 Time elapsed (seconds)=
> 0.145339
> order= 767 measure ofimprecision= 7.909 Time elapsed (seconds)=
> 0.841142
> order= 1535 measure ofimprecision= 8.532 Time elapsed (seconds)=
> 5.793630
> order= 3071 measure ofimprecision= 9.774 Time elapsed (seconds)=
> 39.559540
> order= 6143 Process terminated by a MemoryError
>
> Above: 2.7.3 Below: Python 3.2.3
>
> bbb_bbb
> 3.2.3 (default, Apr 11 2012, 07:15:24) [MSC v.1500 32 bit (Intel)]
> order= 2 measure ofimprecision= 0.000 Time elapsed (seconds)=
> 0.113930
> order= 5 measure ofimprecision= 1.807 Time elapsed (seconds)=
> 0.001373
> order= 11 measure ofimprecision= 2.395 Time elapsed (seconds)=
> 0.001468
> order= 23 measure ofimprecision= 3.073 Time elapsed (seconds)=
> 0.001609
> order= 47 measure ofimprecision= 5.642 Time elapsed (seconds)=
> 0.002687
> order= 95 measure ofimprecision= 5.745 Time elapsed (seconds)=
> 0.013510
> order= 191 measure ofimprecision= 5.866 Time elapsed (seconds)=
> 0.061560
> order= 383 measure ofimprecision= 7.129 Time elapsed (seconds)=
> 0.418490
> order= 767 measure ofimprecision= 8.240 Time elapsed (seconds)=
> 3.815713
> order= 1535 measure ofimprecision= 8.735 Time elapsed (seconds)=
> 27.877270
> order= 3071 measure ofimprecision= 9.996 Time elapsed
> (seconds)=212.545610
> order= 6143 Process terminated by a MemoryError
>
>
>

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