The python3 version is compiled without any optimised library and is falling back on a slow version. Where did you get this installation from?
Jens On Wed, Mar 20, 2013 at 3:01 PM, Colin J. Williams <cjwilliam...@gmail.com>wrote: > On 20/03/2013 10:30 AM, Frédéric Bastien wrote: > > Hi, > > > > win32 do not mean it is a 32 bits windows. sys.platform always return > > win32 on 32bits and 64 bits windows even for python 64 bits. > > > > But that is a good question, is your python 32 or 64 bits? > 32 bits. > > Colin W. > > > > Fred > > > > On Wed, Mar 20, 2013 at 10:14 AM, Daπid <davidmen...@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 <c...@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 > >>> > >>> > >>> > >>> _______________________________________________ > >>> NumPy-Discussion mailing list > >>> NumPy-Discussion@scipy.org > >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion > >>> > >> _______________________________________________ > >> NumPy-Discussion mailing list > >> NumPy-Discussion@scipy.org > >> http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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