On Wednesday 19 August 2009 19:11:42 Garth N. Wells wrote:
Johan Hake wrote:
On Wednesday 19 August 2009 18:50:13 Garth N. Wells wrote:
Johan Hake wrote:
On Wednesday 19 August 2009 17:29:52 Garth N. Wells wrote:
What's going on behind the scene when I copy one vector to another
in
PyDOLFIN using
u0.vector()[:] = u.vector()[:]
When I add:
u2 = Function(V)
u2.vector()[:] = u.vector()[:]
# Plot solution
plot(u2)
In a the Poisson demo it works fine.
In parallel?
Yupp!
This type of slice only uses the assignment operator.
The GenericVector assignment operator?
Yes. This is done in __setslice__/__getslice__ in the extended python
class.
I don't see then why the we end up inside
_compare_vector_with_vector
for the Cahn-Hilliard demo when running in parallel?
Are you sure it is in
_compare_vector_with_vector
this should only kick in if you used '==' or some other comparison
operator.
When I run this demo in parallel, I get passed the assignment line but it
stops when calling the solve function with the following message:
Traceback (most recent call last):
File "demo.py", line 86, in <module>
Traceback (most recent call last):
solver.solve(problem, u.vector())
RuntimeError: *** Error: MUMPS is required for parallel symbolic LU.
I delved into the problem and the program ends up in the function
_compare_vector_with_vector from dolfin_la_get_set_items.i. It uses
GenericVector::get and GenericVector::set. These need to be used with
caution in parallel.
Yes, when other type of slices are used we need to find a way to do
that
in parallel. I have just started digging in the parallel code, and
need
to look into all the changes to get familiar with it before I try to
solve this.
This shouldn't be too hard.
set(const double* block, uint m, const uint* rows)
works in parallel but
get(double* block, uint m, const uint* rows)
doesn't yet (not too hard to fix though). The really evil functions are
set(const double*)
and
get(double*)
I think I am only using
set(const double* block, uint m, const uint* rows)
get(double* block, uint m, const uint* rows)
However GenericVector.array is using the get(double*) function, and
returns
some fishy numbers in the numpy.array.
Johan
