Tim Stitt timothy.stitt at ichec.ie
Tue Nov 20 11:45:31 CST 2007

```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)