# Issues with interpolation of CompiledExpression in parallel

Hello,

as the title says, I am having issues with interpolating a CompiledExpression in parallel (with mpirun).
The task I want to solve and the problem can be boiled down to the following: I want to create a certain profile on a two-dimensional mesh. To do so, I solve a one-dimensional PDE and want to use the solution of the PDE to define the two-dimensional profile, by repeating the pattern.
Mathematically, this can be expressed as follows: Let u(x) be the solution to the 1D PDE. Then the 2D profile I want to achieve is given by

v(x,y) = \begin{cases} u(x \mod \alpha) \quad &\text{ if } (x \mod \alpha) \leq \beta \\ 0 \quad &\text{ else} \end{cases}

where \alpha >= \beta. Additionally, for the sake of simplicity, let \beta be the length of the 1D interval where the 1D profile is computed.

This means, that the 1D profile should be repeated (starting from x=0) with a distance given by \alpha. I hope the screenshots attached below will make the problem more obvious.

I have the following MWE which illustrates the problem. The code is given by

from fenics import *

mesh = UnitSquareMesh(32, 32)
W = FunctionSpace(mesh, "CG", 1)

def create_profile():
channel_width = 1 / 8
mesh = IntervalMesh(10, 0.0, channel_width)
dx = Measure("dx", mesh)
V = FunctionSpace(mesh, "CG", 1)
u = TrialFunction(V)
v = TestFunction(V)

L = Constant(1.0) * v * dx

def bdry(x, on_boundary):
return on_boundary

bcs = DirichletBC(V, Constant(0.0), bdry)
u = Function(V)
solve(a == L, u, bcs)

cpp_code = """
#include <pybind11/pybind11.h>
#include <pybind11/eigen.h>
namespace py = pybind11;

#include <dolfin/function/Expression.h>
#include <dolfin/function/Function.h>

class Profile : public dolfin::Expression
{
public:

Profile() : dolfin::Expression() {}

void eval(Eigen::Ref<Eigen::VectorXd> values, Eigen::Ref<const Eigen::VectorXd> x) const override
{
Eigen::VectorXd dx(2);
dx[0] = std::fmod(x[0], 0.2);
dx[1] = 0.0;

if (dx[0] > 0.1 | dx[0] <= 0.0) {
values[0] = 0.0;
}
else {
p->eval(values, dx);
const double val = values[0];
values[0] = val;
}
}

std::shared_ptr<dolfin::Function> p;
};

PYBIND11_MODULE(SIGNATURE, m)
{
py::class_<Profile, std::shared_ptr<Profile>, dolfin::Expression>
(m, "Profile")
.def(py::init<>())
}
"""
u.set_allow_extrapolation(1)
expr_u_desired = CompiledExpression(
compile_cpp_code(cpp_code).Profile(), degree=1, p=u.cpp_object(), domain=mesh
)

# using LagrangeInterpolator.interpolate does not work either
# u_des = Function(W)
# LagrangeInterpolator.interpolate(u_des, expr_u_desired)

u_des = interpolate(expr_u_desired, W)

return u_des

u_des = create_profile()

with XDMFFile(MPI.comm_world, "u_des.xdmf") as file:
file.parameters["flush_output"] = True
file.parameters["functions_share_mesh"] = False
file.write_checkpoint(u_des, "u_test", 0, XDMFFile.Encoding.HDF5, False)



Running this program in serial gives the following output

Which achieves exactly what I am looking for.
However, when the exact same program in parallel, I get the following results with mpirun -n 2 python script.py

And it only gets worse for 3 MPI processes mpirun -n 3 python script.py

or even 4 processes (mpirun -n 4 python script.py)

and for the sake of completeness, here is the one with 4 processes, but rescaled

As you can guess, this effect is amplified for my real application as I cannot use a regular grid and have to use even more processes. I have not tried using anything but a CompiledExpression, but as the grids I have to use are large, using a UserExpression is not really feasible, which is why I have not tried this.

As you can see in the comment in the source code, I have also tried using LagrangeInterpolator.interpolate, but this gives rise to the same problem.
Ah, and by the way: I am using (legacy) FEniCS, version 2019.1.

Does anyone have a hint how to achieve a consistent interpolation of a CompiledExpression in parallel?

Small update:
It seems that I have now understood where the issue comes from: The evaluation of a function in parallel (with MPI) is not well defined due to the partioning of the mesh. There have been several issues addressing this point already in this forum.
The same issue also seems to appear in the CompiledExpression either when calling p->evaluate or when the expression is evaluated during the interpolation.

Still, I have not found a (true parallel) solution to this problem. Is there a way to use a function as part of a CompiledExpression and evaluate it correctly in parallel?

You would need to pre-partition your mesh in such a way that you do not need extrapolation from one mesh to the other.

The HDF5File in dolfin can take in such partitions.

So if I do not want to pre-partition my mesh there is no way to do that?
And even if I would pre-partition the mesh, then I have to make sure that the function I want to interpolate using the CompiledExpression can be evaluated “unambigously” without extrapolation?

The only other workaround I have found is to do the interpolation of the CompiledExpression in serial, save the result, and then load it back in, but that does of course not scale when the mesh becomes large.

As far as Im aware, there is no way of doing this in legacy dolfin without Pre-partitioning (if anyone knows a way please feel free to correct me).

Yes.

In DOLFINx, @Massimiliano_Leoni has improved interpolation between meshes.

1 Like

@dokken I searched internet, but I cannot find:
(1) how to do pre-parition of fenics through HDF5File
(2) how to use pre-partition to solve the problem in multi-thread of this topic
could you please teach how to do it. It is very important to me, my research has been stuck in this similar place for a long time

@plugged have you fixed this problem? Could you give me a hint how to solve it?