Hello everybody,
I have got a problem with the attached code when it is run in parrallel
using mpirun. The more processes I use, the more often it crashes with
the message:
Traceback (most recent call last):
File "test.py", line 32, in <module>
solver.solve()
RuntimeError:
***
-------------------------------------------------------------------------
*** DOLFIN encountered an error. If you are not able to resolve this issue
*** using the information listed below, you can ask for help at
***
*** [email protected]
***
*** Remember to include the error message listed below and, if possible,
*** include a *minimal* running example to reproduce the error.
***
***
-------------------------------------------------------------------------
*** Error: Unable to successfully call PETSc function 'KSPSolve'.
*** Reason: PETSc error code is: 76.
*** Where: This error was encountered inside
/build/buildd/dolfin-1.3.0+dfsg/dolfin/la/PETScKrylovSolver.cpp.
*** Process: 11
***
*** DOLFIN version: 1.3.0
*** Git changeset: unknown
***
-------------------------------------------------------------------------
Using only few processes the programm terminates properly, but the
results are obviously not correct. The installation is the ppa
installation on a compute server running ubuntu 13.10 server. It seems
that commenting out line 8 and using the Expression to define the
boundary condition solves the problem.
Please tell me if any further information is needed or if I should post
this problem anywhere else.
Thanks in advance,
Heinz Zorn
from dolfin import *
mesh = UnitCubeMesh( 30, 30, 30 )
V = VectorFunctionSpace( mesh, "CG", 1 )
u0 = Expression(("x[0]","0","0"))
# u0 = project( u0, V )
def u0_boundary(x, on_boundary):
return on_boundary
bc = DirichletBC(V, u0, u0_boundary)
def sigmaIso( u, lmbda, mu ):
return 2*mu*sym(grad(u))+lmbda*tr(grad(u))*Identity(u.cell().d)
E = 100
nu = 0.3
mu = E/(2.0*(1.0+nu))
lmbda = E*nu/((1.0+nu)*(1.0-2.0*nu))
u = TrialFunction( V )
v = TestFunction( V )
pde = inner( sigmaIso(u,lmbda,mu), sym(grad(v)) )*dx
a, L = system(pde)
u = Function( V )
problem = LinearVariationalProblem(a, L, u, bc)
solver = LinearVariationalSolver(problem)
solver.parameters["linear_solver"] = "cg"
solver.parameters["preconditioner"] = "hypre_amg"
solver.solve()
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