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On 20/03/2013 10:14 AM, Daπid wrote:
I know nothing about what goes on behind the scenes. I am using the win32 binary package.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? Colin W. 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 |
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