[SciPy-user] some benchmark data for numarray, Numeric and scipy-newcore

Gerard Vermeulen gerard.vermeulen at grenoble.cnrs.fr
Sun Dec 4 16:25:24 CST 2005


I took a look at the difference between arange in numarray and scipy:
in numarray arange is a Python function which dispatches the real work
to a type dependent C function, whereas in scipy arange does
all calculations in C doubles, which are cast to the requested type.

This may explain why numarray's arange is 5 times faster than scipy's
arange on my system (don't ask me why David's results for numarray are
so slow).

As a side effect, numarray's arange works for complex numbers contrary
to scipy's arange.

Gerard

On Sun, 4 Dec 2005 15:40:58 -0600
Ted Horst <ted.horst at earthlink.net> wrote:

> I took a quick look at this in numarray and scipy (I don't have 
> Numeric).
> 
> It seems that numarray has separate lowest level functions for vector, 
> vector multiplication and scalar, vector multiplication whereas scipy 
> uses the same function for both.  This results in an extra load double 
> instruction being executed in scipy for the scalar, vector case.
> 
> This is on a Powerbook G4, running OSX 10.3.9.
> 
> Ted
> 
> On Dec 3, 2005, at 21:07, Travis Oliphant wrote:
> 
> > David M. Cooke wrote:
> >
> >> These are my numbers. They don't support the above conclusions :-)
> >> This is on an Athlon 64 3200+, running Debian. Numeric and numarray
> >> are from Debian unstable.
> >>
> >> cookedm at arbutus$ py bench.py 12
> >> Numeric-24.2
> >> numarray-1.4.0
> >> scipy-core-0.7.4.1550
> >> benchmark size = 12  (vectors of length 16777216)
> >> label            Numeric       numarray     scipy.base
> >>     1             0.5642          1.086          0.541
> >>     2              1.044          1.952         0.8472
> >>     3             0.7367         0.7428         0.6201
> >>     4              1.965         0.8811           2.45
> >>     5              1.048         0.9061         0.9241
> >>     6             0.7419         0.7496         0.5371
> >>     7              1.972          1.293          2.412
> >>     8              1.222           0.94         0.9529
> >>     9              23.99          13.65          13.16
> >>    10              14.96          13.65          11.42
> >>    11              10.86          10.61          9.056
> >> TOTAL               59.1          46.47          42.92
> >
> >
> > I like your version better.... :-)   But, seriously,  it does still 
> > look
> > like something is going on for lines 4 and 7 that could be improved in
> > scipy core....
> >
> > -Travis
> >
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> >
> 
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