Hi all (again),
I finally got some data back from the KSP PETSc code that I put
together to solve this sparse inverse matrix problem I was looking
into. Ideally I am aiming for a O(N) (time complexity) approach to
getting the first 'k' columns of the inverse of a sparse matrix.
To recap the method: I have my solver which uses KSPSolve in a loop
that iterates over the first k columns of an identity matrix B and
computes the corresponding x vector.
I am just a bit curious about some of the timings I am
obtaining...which I hope someone can explain. Here are the timings I
obtained for a global sparse matrix (4704 x 4704) and solving for
the first 1176 columns in the identity using P processes
(processors) on our cluster.
(Timings are given in seconds for each process performing work in
the loop and were obtained by encapsulating the loop with the
cpu_time() Fortran intrinsic. The MUMPS package was requested for
factorisation/solving, although similar timings were obtained for
both the native solver and SUPERLU)
P=1 [30.92]
P=2 [15.47, 15.54]
P=4 [4.68, 5.49, 4.67, 5.07]
P=8 [2.36, 4,23, 2.81, 2.54, 3.42, 2.22, 1.41, 3.15]
P=16 [1.04, 0.45, 1.08, 0.27, 0.87, 0.93, 1.1, 1.06, 0.29, 0.34,
0.73, 0.25, 0.43, 1.09, 1.08, 1.1]
Firstly, I notice very good scalability up to 16 processes...is this
expected (by those people who use these solvers regularly)?
Also I notice that the timings per process vary as we scale up. Is
this a load-balancing problem related to more non-zero values being
on a given processor than others? Once again is this expected?
Please excuse my ignorance of matters relating to these solvers and
their operation...as it really isn't my field of expertise.
Regards,
Tim.
--
Dr. Timothy Stitt <timothy_dot_stitt_at_ichec.ie>
HPC Application Consultant - ICHEC (www.ichec.ie)
Dublin Institute for Advanced Studies
5 Merrion Square - Dublin 2 - Ireland
+353-1-6621333 (tel) / +353-1-6621477 (fax)