[Numpy-discussion] numarray interface and performance issues (for dot product and transpose)
R.M.Everson at exeter.ac.uk
Fri Mar 1 01:42:05 CST 2002
On 28 Feb 2002, Travis Oliphant wrote:
> On 28 Feb 2002, A.Schmolck wrote:
>> Two essential matrix operations (matrix-multiplication and
>> transposition (which is what I am mainly using) are both
>> a) less efficient and
>> b) less notationally elegant
> You are not alone in your concerns. The developers of SciPy are
> quite concerned about speed, hence the required linking to ATLAS.
> The question of notational elegance is stickier because we just
> can't add new operators.
> The solution I see is to use other classes.
At the moment, I agree this is probably the best solution, although it
would be nice if the core python was able to add operators :)
>> The following Matlab fragment
>> M * (C' * C) * V' * u
> This becomes (using SciPy which defines Mat = Matrix.Matrix and
> could later redefine it to use the ATLAS libraries for matrix
> C, V, u, M = apply(Mat, (C, V, u, M))
> M * (C.H * C) * V.H * M
Yes, much better.
> not bad.. and with a Mat class that uses the ATLAS blas (not a
> very hard thing to do now.), this could be made as fast as
> Perhaps, as as start we could look at how you make the current
> Numeric use blas if it is installed to do dot on real and complex
> arrays (I know you can get rid of lapack_lite and use your own
> lapack) but, the dot function is defined in multiarray and would
> have to be modified to use the BLAS instead of its own homegrown
This is precisely what Alex and I have done. Please see the patch to
Numeric and timings on http://www.dcs.ex.ac.uk/~aschmolc/Numeric/
It's not beautiful but about 40 times faster on 1000 by 1000 matrix
multiplies. I'll attempt to provide a similar patch for numarray over
the next week or so.
Many thanks for your comments.
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