Yes, the matrix is sparse.
On 04/30/2014 10:19 AM, Jose E. Roman wrote:
El 30/04/2014, a las 17:10, Steve Ndengue escribió:
Dear all,
I have few questions on achieving convergence with SLEPC.
I am doing some comparison on how SLEPC performs compare to a LAPACK
installation on my system (an 8 processors icore7 with 3.4 GHz running Ubuntu).
1/ It appears that a calculation requesting the LAPACK eigensolver runs faster
using my libraries than when done with SLEPC selecting the 'lapack' method. I
guess most of the time is spent when assembling the matrix? However if the time
seems reasonable for a matrix of size less than 2000*2000, for one with
4000*4000 and above, the computation time seems more than ten times slower with
SLEPC and the 'lapack' method!!!
Once again, do not use SLEPc's 'lapack' method, it is just for debugging
purposes.
2/ I was however expecting that running an iterative calculation such as
'krylovschur', 'lanczos' or 'arnoldi' the time would be shorter but that is not
the case. Inserting the Shift-and-Invert spectral transform, i could converge
faster for small matrices but it takes more time using these iteratives methods
than using the Lapack library on my system, when the size allows; even when
requesting only few eigenstates (less than 50).
Is your matrix sparse?
regarding the 2 previous comments I would like to know if there are some rules
on how to ensure a fast convergence of a diagonalisation with SLEPC?
3/ About the diagonalisation on many processors, after we assign values to the
matrix, does SLEPC automatically distribute the calculation among the requested
processes or shall we need to insert commands on the code to enforce it?
Read the manual, and have a look at examples that work in parallel (most of
them).
Sincerely,
--
Steve
--
Steve A. Ndengué
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