On May 25, 2010, at 9:37 PM, Daniel Wheeler wrote:
> Maybe try without any preconditioner or with only Jacobi.
Here's what I get for a variety of configurations (2 runs each, showing pretty
good run-to-run consistency).
The default solver and preconditioner definitely seem bad, at least for this
problem. Of this batch, Trilinos' PCG solver with no preconditioner at all is
preferred, but is still substantially slower than PySparse's PCG.
total _prepareLinearSystem _solve (i)
precon
PySparse default (PCG) 71.9 49.4 7.3
PySparse default 61.6 50.2 7.4
Trilinos default (ii) 170.6 50.4 116.2
42.2
Trilinos default 168.6 50.3 114.7
42.0
Trilinos, GMRES, no precon (iii) 117.2 50.7 62.4
Trilinos, GMRES, no precon 116.6 50.2 62.7
Trilinos, GMRES, Jacobi 123.9 51.0 69.1
Trilinos, GMRES, Jacobi 120.2 50.2 66.0
Trilinos, PCG, no precon 101.1 49.9 47.2
Trilinos, PCG, no precon 101.9 51.2 46.9
(iv)
mpirun -np 2, GMRES, no precon 75.5 29.1 42.5
mpirun -np 2, PCG, no precon 68.4 31.7 32.6
(i) includes preconditioning
(ii) GMRES, MultiLevelDDPreconditioner
(iii) obtained with
solver = DefaultSolver(precon=None)
and
phaseEq.solve(phase, dt=dt, solver=solver)
heatEq.solve(dT, dt=dt, solver=solver)
(iv) profiling results for parallel are dubious. They likely only represent
results for a single process, at best. Moreover, the .prof file was garbled the
second time I tried this, for both configurations. Still, they all seem to
scale consistently with wall-clock time.
