El 30/04/2014, a las 17:25, Steve Ndengue escribió: > Yes, the matrix is sparse.
How sparse? How are you running the solver? Where is the time spent (log_summary)? Jose > > > 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 > --- >
