Hello
This is my observation as well (with MUMPS). The first solve (after
assembly which is super fast) takes a few mins (for ~1 million unknowns
on 12/24 cores) but from then on only a few seconds for each subsequent
solve for each time step.
Perhaps symbolic factorization in MUMPS is all serial?
Like the OP I often do multiple runs on the same problem but I dont know
if MUMPS or any other direct solver can save the symbolic factorization
info to a file that perhaps can be utilized in subsequent reruns to
avoid the costly "first solves".
Tabrez
On 01/28/2014 04:04 PM, Barry Smith wrote:
On Jan 28, 2014, at 1:36 PM, David Liu<[email protected]> wrote:
Hi, I'm writing an application that solves a sparse matrix many times using
Pastix. I notice that the first solves takes a very long time,
Is it the first “solve” or the first time you put values into that matrix
that “takes a long time”? If you are not properly preallocating the matrix then
the initial setting of values will be slow and waste memory. See
http://www.mcs.anl.gov/petsc/petsc-current/docs/manualpages/Mat/MatXAIJSetPreallocation.html
The symbolic factorization is usually much faster than a numeric
factorization so that is not the cause of the slow “first solve”.
Barry
while the subsequent solves are very fast. I don't fully understand what's
going on behind the curtains, but I'm guessing it's because the very first
solve has to read in the non-zero structure for the LU factorization, while the
subsequent solves are faster because the nonzero structure doesn't change.
My question is, is there any way to save the information obtained from the very
first solve, so that the next time I run the application, the very first solve
can be fast too (provided that I still have the same nonzero structure)?
--
No one trusts a model except the one who wrote it; Everyone trusts an
observation except the one who made it- Harlow Shapley
