def test_krylov_solver_lu():
mesh = UnitSquareMesh(MPI.comm_world, 12, 12)
V = FunctionSpace(mesh, ("Lagrange", 1))
u, v = TrialFunction(V), TestFunction(V)
a = inner(u, v) * dx
L = inner(1.0, v) * dx
A = assemble_matrix(a)
A.assemble()
b = assemble_vector(L)
b.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
norm = 13.0
solver = PETScKrylovSolver(mesh.mpi_comm())
solver.set_options_prefix("test_lu_")
PETScOptions.set("test_lu_ksp_type", "preonly")
PETScOptions.set("test_lu_pc_type", "lu")
solver.set_from_options()
x = A.createVecRight()
solver.set_operator(A)
solver.solve(x, b)
# *Tight* tolerance for LU solves
assert round(x.norm(PETSc.NormType.N2) - norm, 12) == 0
def test_computed_norms_against_references():
# Reference values for norm of solution vector
reference = {
("16x16 unit tri square", 1): 9.547454087328376,
("16x16 unit tri square", 2): 18.42366670418269,
("16x16 unit tri square", 3): 27.29583104732836,
("16x16 unit tri square", 4): 36.16867128121694,
("4x4x4 unit tet cube", 1): 12.23389289626038,
("4x4x4 unit tet cube", 2): 28.96491629163837,
("4x4x4 unit tet cube", 3): 49.97350551329799,
("4x4x4 unit tet cube", 4): 74.49938266409099,
("16x16 unit quad square", 1): 9.550848071820747,
("16x16 unit quad square", 2): 18.423668706176354,
("16x16 unit quad square", 3): 27.295831017251672,
("16x16 unit quad square", 4): 36.168671281610855,
("4x4x4 unit hex cube", 1): 12.151954087339782,
("4x4x4 unit hex cube", 2): 28.965646690046885,
("4x4x4 unit hex cube", 3): 49.97349423895635,
("4x4x4 unit hex cube", 4): 74.49938136593539
}
# Mesh files and degrees to check
meshes = [(UnitSquareMesh(MPI.comm_world, 16,
16), "16x16 unit tri square"),
(UnitCubeMesh(MPI.comm_world, 4, 4, 4), "4x4x4 unit tet cube")]
# (UnitSquareMesh(MPI.comm_world, 16, 16, CellType.Type.quadrilateral), "16x16 unit quad square"),
# (UnitCubeMesh(MPI.comm_world, 4, 4, 4, CellType.Type.hexahedron), "4x4x4 unit hex cube")]
degrees = [1, 2]
# For MUMPS, increase estimated require memory increase. Typically
# required for high order elements on small meshes in 3D
PETScOptions.set("mat_mumps_icntl_14", 40)
# Iterate over test cases and collect results
results = []
for mesh in meshes:
for degree in degrees:
gc_barrier()
norm = compute_norm(mesh[0], degree)
results.append((mesh[1], degree, norm))
# Change option back to default
PETScOptions.set("mat_mumps_icntl_14", 20)
# Check results
errors = check_results(results, reference, tol)
# Print errors for debugging if they fail
if errors:
print_errors(errors)
# Print results for use as reference
if any(e[-1] is None for e in errors): # e[-1] is diff
print_reference(results)
# A passing test should have no errors
assert len(errors) == 0 # See stdout for detailed norms and diffs.
def test_krylov_reuse_pc_lu():
"""Test that LU re-factorisation is only performed after
set_operator(A) is called"""
# Test requires PETSc version 3.5 or later. Use petsc4py to check
# version number.
try:
from petsc4py import PETSc
except ImportError:
pytest.skip("petsc4py required to check PETSc version")
else:
if not PETSc.Sys.getVersion() >= (3, 5, 0):
pytest.skip("PETSc version must be 3.5 of higher")
mesh = UnitSquareMesh(MPI.comm_world, 12, 12)
V = FunctionSpace(mesh, ("Lagrange", 1))
u, v = TrialFunction(V), TestFunction(V)
a = Constant(1.0) * u * v * dx
L = Constant(1.0) * v * dx
assembler = fem.Assembler(a, L)
A = assembler.assemble_matrix()
b = assembler.assemble_vector()
norm = 13.0
solver = PETScKrylovSolver(mesh.mpi_comm())
solver.set_options_prefix("test_lu_")
PETScOptions.set("test_lu_ksp_type", "preonly")
PETScOptions.set("test_lu_pc_type", "lu")
solver.set_from_options()
solver.set_operator(A)
x = PETScVector(mesh.mpi_comm())
solver.solve(x, b)
assert round(x.norm(cpp.la.Norm.l2) - norm, 10) == 0
assembler = fem.assemble.Assembler(Constant(0.5) * u * v * dx, L)
assembler.assemble(A)
x = PETScVector(mesh.mpi_comm())
solver.solve(x, b)
assert round(x.norm(cpp.la.Norm.l2) - 2.0 * norm, 10) == 0
solver.set_operator(A)
solver.solve(x, b)
assert round(x.norm(cpp.la.Norm.l2) - 2.0 * norm, 10) == 0
def test_krylov_solver_lu():
mesh = UnitSquareMesh(MPI.comm_world, 12, 12)
V = FunctionSpace(mesh, ("Lagrange", 1))
u, v = TrialFunction(V), TestFunction(V)
a = Constant(1.0) * inner(u, v) * dx
L = inner(Constant(1.0), v) * dx
A = assemble(a)
b = assemble(L)
norm = 13.0
solver = PETScKrylovSolver(mesh.mpi_comm())
solver.set_options_prefix("test_lu_")
PETScOptions.set("test_lu_ksp_type", "preonly")
PETScOptions.set("test_lu_pc_type", "lu")
solver.set_from_options()
x = PETScVector()
solver.set_operator(A)
solver.solve(x, b)
# *Tight* tolerance for LU solves
assert round(x.norm(cpp.la.Norm.l2) - norm, 12) == 0
def xtest_mg_solver_stokes():
mesh0 = UnitCubeMesh(2, 2, 2)
mesh1 = UnitCubeMesh(4, 4, 4)
mesh2 = UnitCubeMesh(8, 8, 8)
Ve = VectorElement("CG", mesh0.ufl_cell(), 2)
Qe = FiniteElement("CG", mesh0.ufl_cell(), 1)
Ze = MixedElement([Ve, Qe])
Z0 = FunctionSpace(mesh0, Ze)
Z1 = FunctionSpace(mesh1, Ze)
Z2 = FunctionSpace(mesh2, Ze)
W = Z2
# Boundaries
def right(x, on_boundary):
return x[0] > (1.0 - DOLFIN_EPS)
def left(x, on_boundary):
return x[0] < DOLFIN_EPS
def top_bottom(x, on_boundary):
return x[1] > 1.0 - DOLFIN_EPS or x[1] < DOLFIN_EPS
# No-slip boundary condition for velocity
noslip = Constant((0.0, 0.0, 0.0))
bc0 = DirichletBC(W.sub(0), noslip, top_bottom)
# Inflow boundary condition for velocity
inflow = Expression(("-sin(x[1]*pi)", "0.0", "0.0"), degree=2)
bc1 = DirichletBC(W.sub(0), inflow, right)
# Collect boundary conditions
bcs = [bc0, bc1]
# Define variational problem
(u, p) = TrialFunctions(W)
(v, q) = TestFunctions(W)
f = Constant((0.0, 0.0, 0.0))
a = inner(grad(u), grad(v)) * dx + div(v) * p * dx + q * div(u) * dx
L = inner(f, v) * dx
# Form for use in constructing preconditioner matrix
b = inner(grad(u), grad(v)) * dx + p * q * dx
# Assemble system
A, bb = assemble_system(a, L, bcs)
# Assemble preconditioner system
P, btmp = assemble_system(b, L, bcs)
spaces = [Z0, Z1, Z2]
dm_collection = PETScDMCollection(spaces)
solver = PETScKrylovSolver()
solver.set_operators(A, P)
PETScOptions.set("ksp_type", "gcr")
PETScOptions.set("pc_type", "mg")
PETScOptions.set("pc_mg_levels", 3)
PETScOptions.set("pc_mg_galerkin")
PETScOptions.set("ksp_monitor_true_residual")
PETScOptions.set("ksp_atol", 1.0e-10)
PETScOptions.set("ksp_rtol", 1.0e-10)
solver.set_from_options()
from petsc4py import PETSc
ksp = solver.ksp()
ksp.setDM(dm_collection.dm())
ksp.setDMActive(False)
x = PETScVector()
solver.solve(x, bb)
# Check multigrid solution against LU solver
solver = LUSolver(A) # noqa
x_lu = PETScVector()
solver.solve(x_lu, bb)
assert round((x - x_lu).norm("l2"), 10) == 0
# Clear all PETSc options
opts = PETSc.Options()
for key in opts.getAll():
opts.delValue(key)
def test_mg_solver_laplace():
# Create meshes and function spaces
meshes = [UnitSquareMesh(N, N) for N in [16, 32, 64]]
V = [FunctionSpace(mesh, "Lagrange", 1) for mesh in meshes]
# Create variational problem on fine grid
u, v = TrialFunction(V[-1]), TestFunction(V[-1])
a = dot(grad(u), grad(v)) * dx
L = v * dx
bc = DirichletBC(V[-1], Constant(0.0), "on_boundary")
A, b = assemble_system(a, L, bc)
# Create collection of PETSc DM objects
dm_collection = PETScDMCollection(V)
# Create PETSc Krylov solver and set operator
solver = PETScKrylovSolver()
solver.set_operator(A)
# Set PETSc solver type
PETScOptions.set("ksp_type", "richardson")
PETScOptions.set("pc_type", "mg")
# Set PETSc MG type and levels
PETScOptions.set("pc_mg_levels", len(V))
PETScOptions.set("pc_mg_galerkin")
# Set smoother
PETScOptions.set("mg_levels_ksp_type", "chebyshev")
PETScOptions.set("mg_levels_pc_type", "jacobi")
# Set tolerance and monitor residual
PETScOptions.set("ksp_monitor_true_residual")
PETScOptions.set("ksp_atol", 1.0e-12)
PETScOptions.set("ksp_rtol", 1.0e-12)
solver.set_from_options()
# Get fine grid DM and attach fine grid DM to solver
solver.set_dm(dm_collection.get_dm(-1))
solver.set_dm_active(False)
# Solve
x = PETScVector()
solver.solve(x, b)
# Check multigrid solution against LU solver solution
solver = LUSolver(A) # noqa
x_lu = PETScVector()
solver.solve(x_lu, b)
assert round((x - x_lu).norm("l2"), 10) == 0
# Clear all PETSc options
from petsc4py import PETSc
opts = PETSc.Options()
for key in opts.getAll():
opts.delValue(key)
# symmetry)
A, b = assemble_system(a, L, bc)
# Create solution function
u = Function(V)
# Create near null space basis (required for smoothed aggregation
# AMG). The solution vector is passed so that it can be copied to
# generate compatible vectors for the nullspace.
null_space = build_nullspace(V, u.vector())
# Attach near nullspace to matrix
A.set_near_nullspace(null_space)
# Set solver options
PETScOptions.set("ksp_view")
PETScOptions.set("ksp_type", "cg")
PETScOptions.set("ksp_rtol", 1.0e-12)
PETScOptions.set("pc_type", "gamg")
# Use Chebyshev smoothing for multigrid
PETScOptions.set("mg_levels_ksp_type", "chebyshev")
PETScOptions.set("mg_levels_pc_type", "jacobi")
# Improve estimate of eigenvalues for Chebyshev smoothing
PETScOptions.set("mg_levels_esteig_ksp_type", "cg")
PETScOptions.set("mg_levels_ksp_chebyshev_esteig_steps", 20)
# Monitor solver
PETScOptions.set("ksp_monitor")
def test_krylov_samg_solver_elasticity():
"Test PETScKrylovSolver with smoothed aggregation AMG"
def build_nullspace(V, x):
"""Function to build null space for 2D elasticity"""
# Create list of vectors for null space
nullspace_basis = [x.copy() for i in range(3)]
with ExitStack() as stack:
vec_local = [stack.enter_context(x.localForm()) for x in nullspace_basis]
basis = [np.asarray(x) for x in vec_local]
# Build translational null space basis
V.sub(0).dofmap.set(basis[0], 1.0)
V.sub(1).dofmap.set(basis[1], 1.0)
# Build rotational null space basis
V.sub(0).set_x(basis[2], -1.0, 1)
V.sub(1).set_x(basis[2], 1.0, 0)
null_space = VectorSpaceBasis(nullspace_basis)
null_space.orthonormalize()
return null_space
def amg_solve(N, method):
# Elasticity parameters
E = 1.0e9
nu = 0.3
mu = E / (2.0 * (1.0 + nu))
lmbda = E * nu / ((1.0 + nu) * (1.0 - 2.0 * nu))
# Stress computation
def sigma(v):
return 2.0 * mu * sym(grad(v)) + lmbda * tr(sym(
grad(v))) * Identity(2)
# Define problem
mesh = UnitSquareMesh(MPI.comm_world, N, N)
V = VectorFunctionSpace(mesh, 'Lagrange', 1)
bc0 = Function(V)
with bc0.vector.localForm() as bc_local:
bc_local.set(0.0)
def boundary(x, only_boundary):
return [only_boundary] * x.shape(0)
bc = DirichletBC(V.sub(0), bc0, boundary)
u = TrialFunction(V)
v = TestFunction(V)
# Forms
a, L = inner(sigma(u), grad(v)) * dx, dot(ufl.as_vector((1.0, 1.0)), v) * dx
# Assemble linear algebra objects
A = assemble_matrix(a, [bc])
A.assemble()
b = assemble_vector(L)
apply_lifting(b, [a], [[bc]])
b.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
set_bc(b, [bc])
# Create solution function
u = Function(V)
# Create near null space basis and orthonormalize
null_space = build_nullspace(V, u.vector)
# Attached near-null space to matrix
A.set_near_nullspace(null_space)
# Test that basis is orthonormal
assert null_space.is_orthonormal()
# Create PETSC smoothed aggregation AMG preconditioner, and
# create CG solver
solver = PETScKrylovSolver("cg", method)
# Set matrix operator
solver.set_operator(A)
# Compute solution and return number of iterations
return solver.solve(u.vector, b)
# Set some multigrid smoother parameters
PETScOptions.set("mg_levels_ksp_type", "chebyshev")
PETScOptions.set("mg_levels_pc_type", "jacobi")
# Improve estimate of eigenvalues for Chebyshev smoothing
PETScOptions.set("mg_levels_esteig_ksp_type", "cg")
PETScOptions.set("mg_levels_ksp_chebyshev_esteig_steps", 50)
# Build list of smoothed aggregation preconditioners
methods = ["petsc_amg"]
# if "ml_amg" in PETScPreconditioner.preconditioners():
# methods.append("ml_amg")
# Test iteration count with increasing mesh size for each
# preconditioner
for method in methods:
for N in [8, 16, 32, 64]:
print("Testing method '{}' with {} x {} mesh".format(method, N, N))
niter = amg_solve(N, method)
assert niter < 18
def test_krylov_samg_solver_elasticity():
"Test PETScKrylovSolver with smoothed aggregation AMG"
def build_nullspace(V, x):
"""Function to build null space for 2D elasticity"""
# Create list of vectors for null space
nullspace_basis = [x.copy() for i in range(3)]
# Build translational null space basis
V.sub(0).dofmap().set(nullspace_basis[0], 1.0)
V.sub(1).dofmap().set(nullspace_basis[1], 1.0)
# Build rotational null space basis
V.sub(0).set_x(nullspace_basis[2], -1.0, 1)
V.sub(1).set_x(nullspace_basis[2], 1.0, 0)
for x in nullspace_basis:
x.apply("insert")
null_space = VectorSpaceBasis(nullspace_basis)
null_space.orthonormalize()
return null_space
def amg_solve(N, method):
# Elasticity parameters
E = 1.0e9
nu = 0.3
mu = E / (2.0 * (1.0 + nu))
lmbda = E * nu / ((1.0 + nu) * (1.0 - 2.0 * nu))
# Stress computation
def sigma(v):
return 2.0 * mu * sym(grad(v)) + lmbda * tr(sym(
grad(v))) * Identity(2)
# Define problem
mesh = UnitSquareMesh(MPI.comm_world, N, N)
V = VectorFunctionSpace(mesh, 'Lagrange', 1)
bc = DirichletBC(V, Constant((0.0, 0.0)),
lambda x, on_boundary: on_boundary)
u = TrialFunction(V)
v = TestFunction(V)
# Forms
a, L = inner(sigma(u), grad(v)) * dx, dot(Constant((1.0, 1.0)), v) * dx
# Assemble linear algebra objects
A, b = assemble_system(a, L, bc)
# Create solution function
u = Function(V)
# Create near null space basis and orthonormalize
null_space = build_nullspace(V, u.vector())
# Attached near-null space to matrix
A.set_near_nullspace(null_space)
# Test that basis is orthonormal
assert null_space.is_orthonormal()
# Create PETSC smoothed aggregation AMG preconditioner, and
# create CG solver
solver = PETScKrylovSolver("cg", method)
# Set matrix operator
solver.set_operator(A)
# Compute solution and return number of iterations
return solver.solve(u.vector(), b)
# Set some multigrid smoother parameters
PETScOptions.set("mg_levels_ksp_type", "chebyshev")
PETScOptions.set("mg_levels_pc_type", "jacobi")
# Improve estimate of eigenvalues for Chebyshev smoothing
PETScOptions.set("mg_levels_esteig_ksp_type", "cg")
PETScOptions.set("mg_levels_ksp_chebyshev_esteig_steps", 50)
# Build list of smoothed aggregation preconditioners
methods = ["petsc_amg"]
# if "ml_amg" in PETScPreconditioner.preconditioners():
# methods.append("ml_amg")
# Test iteration count with increasing mesh size for each
# preconditioner
for method in methods:
for N in [8, 16, 32, 64]:
print("Testing method '{}' with {} x {} mesh".format(method, N, N))
niter = amg_solve(N, method)
assert niter < 18
