It previous tests, core vnl BLASlike routines i.e.
operator*(vnl_vector, vnl_matrix) were at least as fast as tuned BLAS
libraries, since BLAS has to handle things like skip intervals, etc.
On the other hand the LAPACK libraries provided in $VXLSRC/v3p just use
standard BLAS libraries in $VXLSRC/v3p that haven't been tuned at all.
This is the cause of the situation you found. It would be really useful
if someone wanted to sort out the v3p CMake config so that you could use
a systemprovided (and tuned) BLAS library.
Ian.
On 12/03/2011 02:05, Gelas, Arnaud Joel Florent wrote:
> David,
>
> Most of direct sparse solver use BLAS for underlying low level
> operation. BLAS like FFTW is autooptimizing on your machine. You'll
> see a bug difference if you compile BLAS yourself or if you use the
> binaries from your linux distribution for instance. One more
> important point is that recently the UMFPACK group released GOTOBLAS
> which appears to be much faster than BLAS (but released under GPL
> License).
>
> That could explain the timing difference. But also I am not sure if
> VNL makes extensively use of BLAS or not ?
>
> In the case of LDL^T decomposition or Cholesky decomposition, I have
> seen even higher difference in between VNL, TAUCS, and CHOLMOD...
>
> My 2cts, Arnaud
>
> ________________________________________ From: David Doria
> [daviddoria@...] Sent: Friday, March 11, 2011 6:02 PM To:
> VxlUsers Subject: [Vxlusers] Speed of sparse LU solver
>
> I implemented a Poisson solver with a flag to either use VNL or
> Eigen+UMFPACK. For a standard test image, it takes 1.5 minutes with
> VNL's sparse LU solver, but only 1.5 SECONDS with Eigen's interface
> to UMFPACK's sparse LU solver. Has anyone else noticed these drastic
> speed differences from VNL to other libraries?
>
> Thanks,
>
> David
>
> 
>
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