Re: [eigen] Student contribution

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thanks for your hard work. The code is more readable now.
Unfortunately it won't be possible to merge your work into the main
eigen repo right now for the reasons mentioned by Benoit, and also
because we are planing to release 3.2 asap, and I prefer to delay as
most as possible the creation of a new branch for it to save us the
little time we have.

The performance gains are still far away what we can expect from a the
D&C approach. Speed-ups should be around an order of magnitude for a
500x500 matrix. Let's see if the "special" SVD that corresponds to
section 3 of the reference paper can be fixed.


On Fri, Jun 14, 2013 at 3:13 PM, BRUN Gauthier <brunga@xxxxxxxxxxxxxx> wrote:
> Hello,
> At the moment, the two first steps of our algorithm are working well on
> dynamic defined matrices of double and float.
> Sadly, it still doesn't work for matrices of complex.
> For matrices of int, it returns a matrix of zero in order to match the
> behaviour of jacobi algorithm (although using SVD on such matrices is
> irrelevant)
> We are sorry, we couldn't finish the third and last step, but our algorithm
> in its current state is already significantly increasing the performances of
> the singular value decomposition on big matrices (more than around 200 rows
> and columns)
> Thus we would like to submit the two first parts, the tests and the
> benchmark to you in order to validate an important step in our school
> project.
> Indeed, although the work is not entirely finished, we believe that it can
> be useful to Eigen in its current state.
> Despite the fact that our main work is supposed to be finished today, we can
> still work on it until next wednesday, so we will be able to make
> modifications on our code if you ask for it. Our final code has not been
> commited yet on our bitbucket
> Performance analysis: We joined to this mail graphs of the performances
> analysis
>                       (in red JacobiSVD times and in blue BDCSVD times)
>         - Measures are not accurate since we run each test only one time
>         - Time was measure with ctime (clock_gettime REALTIME) on the
> compute part of the algorithm
>         - We compiled with " icpc -O3 -xhost "
>         - The test machine is an Intel Ivy Bridge (i5 3570k)
> On the graph largMatrixTime we can suppose our version improve the
> complexity of the SVD, but on the largeMatrixRatio, we can noticed that the
> performance boost is stuck at x1,7. This because we still use a JacobiSVD in
> the third part, instead of using a SVD algorithm of the special matrix.
> Best regards,
> Team GL27
> Jean Ceccato, Pierre Zoppitelli, Gauthier Brun, Nicolas Carre

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