Re: [Numpy-discussion] Numpy Benchmarking

[Arnd B. <arn...@we...>]

Hi,

On Wed, 28 Jun 2006, Jon Wright wrote:

>
> >>This strikes me as a little bit odd. Why not just provide the best-performing
> >>function to both SciPy and NumPy? Would NumPy be more difficult to install
> >>if the SciPy algorithm for inv() was incorporated?
> >>
> >>
> Having spent a few days recently trying out various different
> eigenvector routines in Lapack I would have greatly appreciated having a
> choice of which one to use

which routine are you trying to use?

> from without having to create my own
> wrappers, compiling atlas and lapack under windows (ouch). I noted that
> Numeric (24.2) seemed to be converting Float32 to double meaning my
> problem no longer fits in memory, which was the motivation for the work.
> Poking around in the svn of numpy.linalg appears to find the same lapack
> routine as Numeric (dsyevd). Perhaps I miss something in the code logic?

if you can convince the code to  get ssyevd instead of dsyevd
it might do what you want>


> The divide and conquer (*evd) uses more memory than the (*ev), as well
> as a factor of 2 for float/double, hence my problem, and the reason why
> "best performing" is a hard choice. I thought matlab has a look at the
> matrix dimensions and problem before deciding what to do (eg: the \
> operator).

Hmm, this is a hard choice, which might better left
in the hands of the knowledgeable user.
(e.g., aren't the divide and conquer routines substantially faster?)

Best, Arnd