[Numpy-discussion] Numpy x Matlab: some synthetic benchmarks
Paulo J. S. Silva
pjssilva at ime.usp.br
Wed Jan 18 11:03:02 CST 2006
Em Qua, 2006-01-18 às 11:15 -0700, Travis Oliphant escreveu:
> Will you run these again with the latest SVN version of numpy. I
> couldn't figure out why a copy was being made on transpose (because it
> shouldn't have been). Then, I dug deep into the PyArray_FromAny code
> and found bad logic in when a copy was needed that was causing an
> inappropriate copy.
>
> I fixed that and now wonder how things will change. Because presumably,
> the dotblas function should handle the situation now...
>
Good work Travis :-)
Tests x.T*y x*y.T A*x A*B A.T*x half 2in2
Dimension: 5
Array 0.9000 0.2400 0.2000 0.2600 0.7100 0.9400 1.1600
Matrix 4.7800 1.5700 0.6200 0.7600 1.0600 3.0400 4.6500
NumArr 3.2900 0.7400 0.6800 0.7800 8.4800 7.4200 11.6600
Numeri 1.3300 0.3900 0.3100 0.4200 0.7900 0.6800 0.7600
Matlab 1.88 0.44 0.41 0.35 0.37 1.20 0.98
Dimension: 50
Array 9.0000 2.1400 0.5500 18.9500 1.4100 4.2700 4.4500
Matrix 48.7400 3.9200 1.0100 20.2000 1.8000 6.5000 8.1900
NumArr 32.3900 2.6800 1.0000 18.9700 13.0300 8.6300 13.0700
Numeri 13.1000 2.2600 0.6500 18.2700 10.1500 1.0400 3.2600
Matlab 16.98 1.94 1.07 17.86 0.73 1.57 1.77
Dimension: 500
Array 1.1400 9.2300 2.0100 168.2700 2.1800 4.0200 4.2900
Matrix 5.0300 9.3500 2.1500 167.5300 2.1700 4.1100 4.4200
NumArr 3.4400 9.1000 2.1000 168.7100 21.8400 4.3900 5.8900
Numeri 1.5800 9.2700 2.0700 167.5600 20.0500 3.4000 4.6800
Matlab 2.09 6.07 2.17 169.45 2.10 2.56 3.06
Note the 10-fold speed-up for higher dimensions :-)
It looks like that now that numpy only looses to matlab in small
dimensions. Probably, the problem is the creation of the object to
represent the transposed object. Probably Matlab creation of objects is
very lightweight (they only have matrices objects to deal with).
Probably this phenomenon explains the behavior for the indexing
operations too.
Paulo
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