Speedup/Refaktoring loop? Extended...
David Bateman
David.Bateman at motorola.com
Wed Aug 27 09:58:01 CDT 2008
Andreas Romeyke wrote:
> Hello,
>
>
>> a=transpose(U) * X;
>> b=transpose(U) * U * V;
>> V=V .* a ./ b;
>> a=X * transpose(V);
>> b=U * V * transpose(V);
>> U=U .* a ./ b;
>>
>
> BTW, could anyone describe why Matlab 7.3 runs 4 times faster than
> Octave 3.02 in this piece of code? AFAIK both programs uses the
> Altlas/Lapack libraries. And the code is fully vectorized here.
>
> Could you explain the difference?
>
> Bye Andreas
>
>
>
Could you give a complete example to test against? Three possible
reasons are that the above explicitly transposes U and Octave in 3.1.51+
now has faster transpose code. If you wrote the above instead as
a=U.' * X;
b=U.' * U * V;
V=V .* a ./ b;
a=X * V.';
b=U * V * V.';
U=U .* a ./ b;
then Octave 3.1.51+, and presumably Matlab, will never explicitly form
the transpose matrix but call the underlying lapack code with the
transpose flagged. A final reason might be that the matlab solver for
over/under determined matrices uses a QR decomposition rather than the
xGELSD function as Octave does. The reason Octave doesn't want to change
is discussed in the thread
http://www.nabble.com/Behavior-of-mldivide-in-Octave-relative-to-Matlab-to16561235.html
Matlab gets a significant speed increase for their solution at the cost
of a solution that is not invariant with column permutations. I suspect
this third issue is the reason for the speed difference Marco pointed
out how to duplicate Matlab's behavior for this, and presumably their
speed, with 4 lines of code.
D.
--
David Bateman David.Bateman at motorola.com
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